26 research outputs found
Two-Way Interference Channel Capacity: How to Have the Cake and Eat it Too
Two-way communication is prevalent and its fundamental limits are first
studied in the point-to-point setting by Shannon [1]. One natural extension is
a two-way interference channel (IC) with four independent messages: two
associated with each direction of communication. In this work, we explore a
deterministic two-way IC which captures key properties of the wireless Gaussian
channel. Our main contribution lies in the complete capacity region
characterization of the two-way IC (w.r.t. the forward and backward sum-rate
pair) via a new achievable scheme and a new converse. One surprising
consequence of this result is that not only we can get an interaction gain over
the one-way non-feedback capacities, we can sometimes get all the way to
perfect feedback capacities in both directions simultaneously. In addition, our
novel outer bound characterizes channel regimes in which interaction has no
bearing on capacity.Comment: Presented in part in the IEEE International Symposium on Information
Theory 201
Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training
Neural based approaches to automatic evaluation of subjective responses have
shown superior performance and efficiency compared to traditional rule-based
and feature engineering oriented solutions. However, it remains unclear whether
the suggested neural solutions are sufficient replacements of human raters as
we find recent works do not properly account for rubric items that are
essential for automated essay scoring during model training and validation. In
this paper, we propose a series of data augmentation operations that train and
test an automated scoring model to learn features and functions overlooked by
previous works while still achieving state-of-the-art performance in the
Automated Student Assessment Prize dataset.Comment: 13 page
Finding the global semantic representation in GAN through Frechet Mean
The ideally disentangled latent space in GAN involves the global
representation of latent space with semantic attribute coordinates. In other
words, considering that this disentangled latent space is a vector space, there
exists the global semantic basis where each basis component describes one
attribute of generated images. In this paper, we propose an unsupervised method
for finding this global semantic basis in the intermediate latent space in
GANs. This semantic basis represents sample-independent meaningful
perturbations that change the same semantic attribute of an image on the entire
latent space. The proposed global basis, called Fr\'echet basis, is derived by
introducing Fr\'echet mean to the local semantic perturbations in a latent
space. Fr\'echet basis is discovered in two stages. First, the global semantic
subspace is discovered by the Fr\'echet mean in the Grassmannian manifold of
the local semantic subspaces. Second, Fr\'echet basis is found by optimizing a
basis of the semantic subspace via the Fr\'echet mean in the Special Orthogonal
Group. Experimental results demonstrate that Fr\'echet basis provides better
semantic factorization and robustness compared to the previous methods.
Moreover, we suggest the basis refinement scheme for the previous methods. The
quantitative experiments show that the refined basis achieves better semantic
factorization while constrained on the same semantic subspace given by the
previous method.Comment: 25 pages, 21 figure
Analyzing the Latent Space of GAN through Local Dimension Estimation
The impressive success of style-based GANs (StyleGANs) in high-fidelity image
synthesis has motivated research to understand the semantic properties of their
latent spaces. In this paper, we approach this problem through a geometric
analysis of latent spaces as a manifold. In particular, we propose a local
dimension estimation algorithm for arbitrary intermediate layers in a
pre-trained GAN model. The estimated local dimension is interpreted as the
number of possible semantic variations from this latent variable. Moreover,
this intrinsic dimension estimation enables unsupervised evaluation of
disentanglement for a latent space. Our proposed metric, called Distortion,
measures an inconsistency of intrinsic tangent space on the learned latent
space. Distortion is purely geometric and does not require any additional
attribute information. Nevertheless, Distortion shows a high correlation with
the global-basis-compatibility and supervised disentanglement score. Our work
is the first step towards selecting the most disentangled latent space among
various latent spaces in a GAN without attribute labels
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
Diffusion models have recently shown remarkable success in high-quality image
generation. Sometimes, however, a pre-trained diffusion model exhibits partial
misalignment in the sense that the model can generate good images, but it
sometimes outputs undesirable images. If so, we simply need to prevent the
generation of the bad images, and we call this task censoring. In this work, we
present censored generation with a pre-trained diffusion model using a reward
model trained on minimal human feedback. We show that censoring can be
accomplished with extreme human feedback efficiency and that labels generated
with a mere few minutes of human feedback are sufficient. Code available at:
https://github.com/tetrzim/diffusion-human-feedback.Comment: Published in NeurIPS 202
CO_2, water, and sunlight to hydrocarbon fuels: a sustained sunlight to fuel (Joule-to-Joule) photoconversion efficiency of 1%
If we wish to sustain our terrestrial ecosphere as we know it, then reducing the concentration of atmospheric CO_2 is of critical importance. An ideal pathway for achieving this would be the use of sunlight to recycle CO_2, in combination with water, into hydrocarbon fuels compatible with our current energy infrastructure. However, while the concept is intriguing such a technology has not been viable due to the vanishingly small CO_2-to-fuel photoconversion efficiencies achieved. Herein we report a photocatalyst, reduced blue-titania sensitized with bimetallic CuโPt nanoparticles that generates a substantial amount of both methane and ethane by CO_2 photoreduction under artificial sunlight (AM1.5): over a 6 h period 3.0 mmol g^(โ1) methane and 0.15 mmol g^(โ1) ethane are obtained (on an area normalized basis 0.244 mol m^(โ2) methane and 0.012 mol m^(โ2) ethane), while no H_2 nor CO is detected. This activity (6 h) translates into a sustained Joule (sunlight) to Joule (fuel) photoconversion efficiency of 1%, with an apparent quantum efficiency of ฯ = 86%. The time-dependent photoconversion efficiency over 0.5 h intervals yields a maximum value of 3.3% (ฯ = 92%). Isotopic tracer experiments confirm the hydrocarbon products originate from CO_2 and water
Kernel Rotation Forests for Classification
There have been significant research efforts for developing decision tree (DT)-based ensemble methods. Such methods generally construct an ensemble by aggregating a large number of unpruned DTs, thereby yielding good classification accuracy. A recently developed method, rotation forest, is known to achieve better classification accuracy by rotating the dataset using principal component analysis (PCA). This paper describes a new method called kernel rotation forest, which is an extension of rotation forest. The proposed method applies kernel PCA instead of linear PCA to extract non-linear features when training DTs. Experimental results showed that kernel rotation forest outperforms rotation forest as well as other DT-based ensemble methods.N
Adaptive fault detection framework for recipe transition in semiconductor manufacturing
The fault detection and classification (FDC) model is a prediction model that utilizes the sensor data of equipment to predict whether each wafer is faulty or not in the future, which is important to achieve a high yield and reduce the cost. To construct a high-performance FDC model with deep learning, a large amount of labeled training data is required. However, in real-world semiconductor manufacturing processes, the transition of recipe leads to a change in the distribution of input sensor data, which causes performance degradation for the existing FDC model. Model retraining for the new recipe is required, but a large time period is required to acquire a large amount of labeled data for the new recipe. In this study, an adaptive fault detection framework is proposed to minimize the performance degradation caused by the transition of recipe. In this framework, immediately after the recipe transition occurs, unsupervised adaptation is employed to reduce the performance degradation. After inspection results for some new recipe wafers are acquired, semi-supervised adaptation is employed to quickly recover the performance with a small amount of labeled data. Through experiments using real-world data, we demonstrate that the proposed framework can adapt to the new recipe with a reduced performance degradation.N