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 Cu–Pt 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^(−1) methane and 0.15 mmol g^(−1) ethane are obtained (on an area normalized basis 0.244 mol m^(−2) methane and 0.012 mol m^(−2) 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 φ = 86%. The time-dependent photoconversion efficiency over 0.5 h intervals yields a maximum value of 3.3% (φ = 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