356 research outputs found
Greedy MAXCUT Algorithms and their Information Content
MAXCUT defines a classical NP-hard problem for graph partitioning and it
serves as a typical case of the symmetric non-monotone Unconstrained Submodular
Maximization (USM) problem. Applications of MAXCUT are abundant in machine
learning, computer vision and statistical physics. Greedy algorithms to
approximately solve MAXCUT rely on greedy vertex labelling or on an edge
contraction strategy. These algorithms have been studied by measuring their
approximation ratios in the worst case setting but very little is known to
characterize their robustness to noise contaminations of the input data in the
average case. Adapting the framework of Approximation Set Coding, we present a
method to exactly measure the cardinality of the algorithmic approximation sets
of five greedy MAXCUT algorithms. Their information contents are explored for
graph instances generated by two different noise models: the edge reversal
model and Gaussian edge weights model. The results provide insights into the
robustness of different greedy heuristics and techniques for MAXCUT, which can
be used for algorithm design of general USM problems.Comment: This is a longer version of the paper published in 2015 IEEE
Information Theory Workshop (ITW
Carry On
Carry On is my thesis project for a MFA in Film and Animation at Rochester Institute of Technology. The entire process took more than two years from start to finish. In November 2011, I wrote the first draft of the script. In April 2013, we finished filming. I did not wrap up editing and the final stages of production until December 2013. I ran into many difficulties and faced many challenges during this special experience, yet I also received more help than I had ever imagined in getting me through these hard times. Truthfully, there would be no way for me to finish a project of this magnitude all on my own. I have learned so much and solidified many professional relationships through this project. I believe this invaluable experience will serve as a cornerstone for my future career development.
Carry On tells a story set in Japan-occupied China during World War II. In 1944, the tide turned against Japan and the war began winding down. Prior to their retreat, Japanese troops looted every Chinese village in their path. They took only food and women from these villages followed by massacring all others and burning everything to the ground. To save his daughter, a Chinese father in my film stuffs her into a large bag disguised as food. As he loads the bag onto the back of a truck along with other bags with food, a Japanese army officer spots his secret. But, surprisingly, moved by the Chinese father\u27s love and courage the officer plays along and lets the daughter go
SAIBench: A Structural Interpretation of AI for Science Through Benchmarks
Artificial Intelligence for Science (AI4S) is an emerging research field that
utilizes machine learning advancements to tackle complex scientific
computational issues, aiming to enhance computational efficiency and accuracy.
However, the data-driven nature of AI4S lacks the correctness or accuracy
assurances of conventional scientific computing, posing challenges when
deploying AI4S models in real-world applications. To mitigate these, more
comprehensive benchmarking procedures are needed to better understand AI4S
models. This paper introduces a novel benchmarking approach, known as
structural interpretation, which addresses two key requirements: identifying
the trusted operating range in the problem space and tracing errors back to
their computational components. This method partitions both the problem and
metric spaces, facilitating a structural exploration of these spaces. The
practical utility and effectiveness of structural interpretation are
illustrated through its application to three distinct AI4S workloads:
machine-learning force fields (MLFF), jet tagging, and precipitation
nowcasting. The benchmarks effectively model the trusted operating range, trace
errors, and reveal novel perspectives for refining the model, training process,
and data sampling strategy. This work is part of the SAIBench project, an AI4S
benchmarking suite
A Spatial Sigma-Delta Approach to Mitigation of Power Amplifier Distortions in Massive MIMO Downlink
In massive multiple-input multiple-output (MIMO) downlink systems, the
physical implementation of the base stations (BSs) requires the use of cheap
and power-efficient power amplifiers (PAs) to avoid high hardware cost and high
power consumption. However, such PAs usually have limited linear amplification
ranges. Nonlinear distortions arising from operation beyond the linear
amplification ranges can significantly degrade system performance. Existing
approaches to handle the nonlinear distortions, such as digital predistortion
(DPD), typically require accurate knowledge, or acquisition, of the PA transfer
function. In this paper, we present a new concept for mitigation of the PA
distortions. Assuming a uniform linear array (ULA) at the BS, the idea is to
apply a Sigma-Delta () modulator to spatially shape the PA
distortions to the high-angle region. By having the system operating in the
low-angle region, the received signals are less affected by the PA distortions.
To demonstrate the potential of this spatial approach, we study
the application of our approach to the multi-user MIMO-orthogonal frequency
division modulation (OFDM) downlink scenario. A symbol-level precoding (SLP)
scheme and a zero-forcing (ZF) precoding scheme, with the new design
requirement by the spatial approach being taken into account,
are developed. Numerical simulations are performed to show the effectiveness of
the developed precoding schemes
Effectivity on Continuous Functions in Topological Spaces
AbstractIn this paper we investigate aspects of effectivity and computability on partial continuous functions in topological spaces. We use the framework of TTE, where continuity and computability on finite and infinite sequences of symbols are defined canonically and transferred to abstract sets by means of notations and representations. We generalize the representations introduced in [Weihrauch, K., “Computable Analysis,” Springer, Berlin, 2000] for the Euclidean case to computable T0-spaces and computably locally compact Hausdorff spaces respectively. We show their equivalence and in particular, prove an effective version of the Stone-Weierstrass approximation theorem
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