261 research outputs found
Limiting efficiencies of solar energy conversion and photo-detection via internal emission of hot electrons and hot holes in gold
We evaluate the limiting efficiency of full and partial solar spectrum
harvesting via the process of internal photoemission in Au-semiconductor
Schottky junctions. Our results based on the ab initio calculations of the
electron density of states (e-DOS) reveal that the limiting efficiency of the
full-spectrum Au converter based on hot electron injection is below 4%. This
value is even lower than previously established limit based on the parabolic
approximation of the Au electron energy bands. However, we predict limiting
efficiency exceeding 10% for the hot holes collection through the Schottky
junction between Au and p-type semiconductor. Furthermore, we demonstrate that
such converters have more potential if used as a part of the hybrid system for
harvesting high- and low-energy photons of the solar spectrum.Comment: Proc. SPIE 9608, Infrared Remote Sensing and Instrumentation XXIII,
960816 (September 1, 2015) 7 pages, 4 figure
Competitive Information Disclosure with Multiple Receivers
This paper analyzes a model of competition in Bayesian persuasion in which
two symmetric senders vie for the patronage of multiple receivers by disclosing
information about the qualities (i.e., binary state -- high or low) of their
respective proposals. Each sender is allowed to commit to a signaling policy
where he sends a private (possibly correlated) signal to every receiver. The
sender's utility is a monotone set function of receivers who make a patron to
this sender.
We characterize the equilibrium structure and show that the equilibrium is
not unique (even for simple utility functions). We then focus on the price of
stability (PoS) in the game of two senders -- the ratio between the best of
senders' welfare (i.e., the sum of two senders' utilities) in one of its
equilibria and that of an optimal outcome. When senders' utility function is
anonymous submodular or anonymous supermodular, we analyze the relation between
PoS with the ex ante qualities (i.e., the probability of high
quality) and submodularity or supermodularity of utility functions. In
particular, in both families of utility function, we show that
when the ex ante quality is weakly smaller than , that is, there
exists equilibrium that can achieve welfare in the optimal outcome. On the
other side, we also prove that when the ex ante quality
is larger than , that is, there exists no equilibrium that can
achieve the welfare in the optimal outcome. We also derive the upper bound of
as a function of and the properties of the value
function. Our analysis indicates that the upper bound becomes worse as the ex
ante quality increases or the utility function becomes more
supermodular (resp.\ submodular)
Code Generation as a Dual Task of Code Summarization
Code summarization (CS) and code generation (CG) are two crucial tasks in the
field of automatic software development. Various neural network-based
approaches are proposed to solve these two tasks separately. However, there
exists a specific intuitive correlation between CS and CG, which have not been
exploited in previous work. In this paper, we apply the relations between two
tasks to improve the performance of both tasks. In other words, exploiting the
duality between the two tasks, we propose a dual training framework to train
the two tasks simultaneously. In this framework, we consider the dualities on
probability and attention weights, and design corresponding regularization
terms to constrain the duality. We evaluate our approach on two datasets
collected from GitHub, and experimental results show that our dual framework
can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing
Systems (NeurIPS) 201
A Novel K-Means Clustered Support Vector Machine Technique for Prediction of Consumer Decision-Making Behaviour
A greater number of consumers are using social networks to express their feedback about the level of service provided by hotels. Online reviews from patrons can be used as a forum to enhance the level of service of hotels. Customer reviews are indeed a reliable and dependable source that aid diners in determining the quality of their cuisine. It is critical to develop techniques for evaluating client feedback on hotel services. In order to accurately anticipate the consumers' decision-making behaviors based on hotel internet evaluations, this study proposes a novel K-Means Clustered Support Vector Machine (KMC+SVM) technique. Principal Component Analysis (PCA) is employed to determine the characteristics from the preprocessed data while the Min-Max normalization approach is used to standardize the raw data. The performance of the suggested technique is then evaluated and contrasted with a few other methods that are currently in use in terms of accuracy, sensitivity, RMSE, and MAE. The findings demonstrated that segmenting customers based on their online evaluations can accurately predict their choices and assist hotel management in establishing priorities for service quality enhancements
ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (Extended)
For efficient query processing, DBMS query optimizers have for decades relied
on delicate cardinality estimation methods. In this work, we propose an
Attention-based LEarned Cardinality Estimator (ALECE for short) for SPJ
queries. The core idea is to discover the implicit relationships between
queries and underlying dynamic data using attention mechanisms in ALECE's two
modules that are built on top of carefully designed featurizations for data and
queries. In particular, from all attributes in the database, the data-encoder
module obtains organic and learnable aggregations which implicitly represent
correlations among the attributes, whereas the query-analyzer module builds a
bridge between the query featurizations and the data aggregations to predict
the query's cardinality. We experimentally evaluate ALECE on multiple dynamic
workloads. The results show that ALECE enables PostgreSQL's optimizer to
achieve nearly optimal performance, clearly outperforming its built-in
cardinality estimator and other alternatives.Comment: VLDB 202
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