Reviewerly/exHarmony huggingface dataset:
Encoder-Free Multiaxis Physics-Aware Fusion Network for Remote Sensing Image Dehazing

Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose EMPF-Net, a novel encoder-free multi-axis physics-aware fusion network that exhibits both light-weighted characteristics and computational efficiency. In our pipeline, we contend that conventional u-shaped networks allocate substantial computational resources to encode haze-degraded features, which play a subordinate role in the reconstruction process. Consequently, our encoder stages solely incorporate down-sampling operations. To improve the representation efficiency and enhance the generalization capabilities, we devise a multi-axis partial queried learning block (MPQLB) that primarily concentrates on learning dimension-wise queries, instead of relying solely on strictly-correlated content of the input features. Furthermore, we augment the reconstruction procedure by incorporating ground truth supervision into each stage via a supervised cross-scale transposed attention module (SCTAM). It calculates attention maps under the guidance of clean images, thereby suppressing less informative features to propagate to the subsequent level. In addition, to address the challenge of ineffective intral-level feature fusion, which result in insufficient elimination of haze-degraded information and negatively impact the quality of reconstructed images, we introduce a physics-aware intra-level fusion module (PIFM). This module harnesses a physical inversion model to facilitate the intra-level feature interaction and alleviate the interference of dehazing-irrelevant information. Our proposed EMPF-Net is evaluated on 12 publicly available datasets, and the experimental results substantiate our superiority in terms of both metrical scores and visual quality, despite being equipped with a modest parameter count of 300 K. Our approach is readily accessible at https://github.com/chdwyb/EMPF-Net.
Untrained Low-Rank Neural Network Prior for Multi-Dimensional Image Recovery

Recently, untrained neural network priors (UNNPs) have received increasing attention for multi-dimensional image recovery. However, previous studies are based on over-parameterized untrained neural networks, which results in unstable behavior. In this letter, we propose an untrained low-rank neural network prior (ULRNNP) for multi-dimensional image recovery, which enjoys the powerful representation ability and stable behavior. More specifically, the elaborately designed nonlinear Tucker decomposition module implicitly imposes low-rank constraints on the feature tensor and can more compactly represent the feature tensor. Attributed to the suggested nonlinear Tucker decomposition module, ULRNNP can simultaneously enjoy strong representation ability and stable behavior. The friendly stable behavior allows us to design a friendly stopping criteria without the reference ground truth image as compared with classic UNNP-based methods. Extensive experiments on different multi-dimensional image datasets validate the superior performance of the proposed ULRNNP over state-of-the-art methods.
GPT-3-Powered Type Error Debugging: Investigating the Use of Large Language Models for Code Repair

Type systems are responsible for assigning types to terms in programs. That way, they enforce the actions that can be taken and can, consequently, detect type errors during compilation. However, while they are able to flag the existence of an error, they often fail to pinpoint its cause or provide a helpful error message. Thus, without adequate support, debugging this kind of errors can take a considerable amount of effort. Recently, neural network models have been developed that are able to understand programming languages and perform several downstream tasks. We argue that type error debugging can be enhanced by taking advantage of this deeper understanding of the language's structure. In this paper, we present a technique that leverages GPT-3's capabilities to automatically fix type errors in OCaml programs. We perform multiple source code analysis tasks to produce useful prompts that are then provided to GPT-3 to generate potential patches. Our publicly available tool, Mentat, supports multiple modes and was validated on an existing public dataset with thousands of OCaml programs. We automatically validate successful repairs by using Quickcheck to verify which generated patches produce the same output as the user-intended fixed version, achieving a 39% repair rate. In a comparative study, Mentat outperformed two other techniques in automatically fixing ill-typed OCaml programs.
Proposing Body Doubling as a Continuum of Space/Time and Mutuality: An Investigation with Neurodivergent Participants

Body doubling involves using the presence of others to stay focused on or accomplish tasks. The term has emerged as a community-driven phenomenon employed by neurodivergent individuals and technologically mediated services for body doubling have followed. Yet, no academic exploration exists on the topic. We survey 220 people to investigate how, when, and why they engage in body doubling and how they define it. Most participants acknowledged that they had used this technique long before learning its name. We present the variety of ways people engage in body doubling (e.g., at a café, with YouTube), a diverse range of tasks people utilize it for (e.g., studying, dishes, exercising), and their motivations for doing so (e.g., generating momentum, staying on task). We contribute a two-part model of body doubling as a continuum of space/time and mutuality. We close with implications for future development in this space.
VegaProf: Profiling Vega Visualizations

Domain-specific languages (DSLs) for visualization aim to facilitate visualization creation by providing abstractions that offload implementation and execution details from users to the system layer. Therefore, DSLs often execute user-defined specifications by transforming them into intermediate representations (IRs) in successive lowering operations.
Cells, Generators, and Lenses: Design Framework for Object-Oriented Interaction with Large Language Models

Large Language Models (LLMs) have become the backbone of numerous writing interfaces with the goal of supporting end-users across diverse writing tasks. While LLMs reduce the effort of manual writing, end-users may need to experiment and iterate with various generation configurations (e.g., inputs and model parameters) until results meet their goals. However, these interfaces are not designed for experimentation and iteration, and can restrict how end-users track, compare, and combine configurations. In this work, we present "cells, generators, and lenses", a framework to designing interfaces that support interactive objects that embody configuration components (i.e., input, model, output). Interface designers can apply our framework to produce interfaces that enable end-users to create variations of these objects, combine and recombine them into new configurations, and compare them in parallel to efficiently iterate and experiment with LLMs. To showcase how our framework generalizes to diverse writing tasks, we redesigned three different interfaces—story writing, copywriting, and email composing—and, to demonstrate its effectiveness in supporting end-users, we conducted a comparative study (N=18) where participants used our interactive objects to generate and experiment more. Finally, we investigate the usability of the framework through a workshop with designers (N=3) where we observed that our framework served as both bootstrapping and inspiration in the design process.
InteractionAdapt: Interaction-driven Workspace Adaptation for Situated Virtual Reality Environments

Virtual Reality (VR) has the potential to transform how we work: it enables flexible and personalized workspaces beyond what is possible in the physical world. However, while most VR applications are designed to operate in a single empty physical space, work environments are often populated with real-world objects and increasingly diverse due to the growing amount of work in mobile scenarios. In this paper, we present InteractionAdapt, an optimization-based method for adapting VR workspaces for situated use in varying everyday physical environments, allowing VR users to transition between real-world settings while retaining most of their personalized VR environment for efficient interaction to ensure temporal consistency and visibility. InteractionAdapt leverages physical affordances in the real world to optimize UI elements for the respectively most suitable input technique, including on-surface touch, mid-air touch and pinch, and cursor control. Our optimization term thereby models the trade-off across these interaction techniques based on experimental findings of 3D interaction in situated physical environments. Our two evaluations of InteractionAdapt in a selection task and a travel planning task established its capability of supporting efficient interaction, during which it produced adapted layouts that participants preferred to several baselines. We further showcase the versatility of our approach through applications that cover a wide range of use cases.
Multisensor Remote Sensing of the Mountain Pass Carbonatite-Hosted REE Deposit

Remote sensing aided exploration of REE-bearing carbonate and phosphate minerals distributed in the carbonatite intrusions is challenging. The electronic processes of REE oxides in VNIR and the overtones of vibrational modes of carbonate and phosphate in the SWIR are the main spectral characteristics for direct identification of REE-bearing minerals but they are generally masked by the strong spectral features of common minerals such as phyllosilicates and iron-oxides. Here, we present spectrally augmented multispectral multisensory data and compare it with hyperspectral data for direct and indirect identification of REE-bearing minerals and their spectral features in the Mountain Pass district.
Variational Autoencoders for Unsupervised Object Counting from VHR Imagery: Applications in Dwelling Extraction from Forcibly Displaced People Settlement Areas

Even though computer vision models are excellent for automatic scene segmentation and object identification from remotely sensed imagery, they demand a huge corpus of annotated data for the training and validation which is a huge challenge in humanitarian emergency response. To tackle this problem, we propose unsupervised dwelling object counting combining Variational Autoencoder (VAE) with an anomaly detection approach. The approach is tested in six Forcibly Displaced People (FDP) settlement areas situated in different parts of the world. Using an anomaly map computed with the VAE model, we demonstrated the possibility of properly locating dwelling objects using anomaly maps. Dwelling counts are obtained by further segmenting anomaly maps. Results show that, though it has strong spatio-temporal variation, the VAE model exhibits promising potential for locating and counting dwellings. It is also observed that in FDP settlements with dense buildings and extremely low contrast between buildings and ground or environment, the performance is relatively lower than the performance achieved in settlement areas with regularly spaced and less complex building structures.
Research on Novel Class Discovery of SAR Target

The availability of a large amount of labeled data has promoted the success of deep learning in Synthetic aperture radar (SAR) target recognition tasks, but these labeled data are invariably obtained manually, which requires huge labor costs. With the development of the SAR field, SAR target categories are still increasing, so there is a large amount of data to be marked. In this paper, we use labeled data to discover novel categories, thereby reducing the cumbersome labeling process. We innovatively propose to apply AutoMix to the labeled dataset to expand the neighborhood distribution and motivate the discrete sample space continuously, which greatly improves the generalization ability of the model. Three stage learning is used to solve the problem of novel class discovery. Firstly, self-supervised learning is used to learn common features from labeled data and unlabeled data. Secondly, AutoMix is used to labeled data which is used to train the feature extractor by supervised learning. Finally, the knowledge of labeled data is transferred to unlabeled images to generate pairs of pseudo-labels for clustering. We experimentally proved that our model can effectively discover novel categories in unlabeled data.
A LCT Pegmatite Spectral Library of the Aldeia Spodumene Deposit: Contributes to Mineral Exploration

Several methodologies can be employed in the prospection of Lithium (Li) in hard-rock (pegmatites). Spectrometry analysis, a Remote Sensing (RS) technique, can be applied to understand the surface spectral response of a sample, both to identify the rock-forming or alteration minerals in its composition and to validate the data collected in situ (by sensors onboard satellites/drones, for example). This paper aims to make available for public use the information acquired on the spectral composition of rock samples from the Barroso pegmatite field in Portugal, within the scope of the INOVMINERAL4.0 project. As a result, a spectral library was created with 47 spectra, collected from 11 different samples. All data is made available in a universal format, thus contributing to open science, corroborating the validation of local spectral data, and stimulating the creation of other databases, in other locations.
Knowledge Distillation for Object Detection: From Generic To Remote Sensing Datasets

Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a remote sensing context various off-the-shelf object detection knowledge distillation methods which have been originally developed on generic computer vision datasets such as Pascal VOC. In particular, methods covering both logit mimicking and feature imitation approaches are applied for vehicle detection using the well-known benchmarks such as xView and VEDAI datasets. Extensive experiments are performed to compare the relative performance and interrelationships of the methods. Experimental results show high variations and confirm the importance of result aggregation and cross validation on remote sensing datasets.
Machine learning in physics: A short guide

Abstract Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.
CrossAdapt: Cross-Scene Adaptation for Multi-Domain Depth Estimation

We address the task of monocular depth estimation in the multi-domain setting. Given a large dataset (source) with ground-truth depth maps, and a set of unlabeled datasets (targets), our goal is to create a model that works well on unlabeled target datasets across different scenes. This is a challenging problem when there is a significant domain shift, often resulting in poor performance on the target datasets. We propose to address this task with a unified approach that includes adversarial knowledge distillation and uncertainty-guided self-supervised reconstruction. We provide both quantitative and qualitative evaluations on four datasets: KITTI, Virtual KITTI, UAVid China, and UAVid Germany. These datasets contain widely varying viewpoints, including ground-level and overhead perspectives, which is more challenging than is typically considered in prior work on domain adaptation for single-image depth. Our approach significantly improves upon conventional domain adaptation baselines and does not require additional memory as the number of target sets increases.
Comparison of Quantum Neural Network Algorithms For Earth Observation Data Classification

This article describes a practical Earth Observation use case that would benefit from quantum computing. We analyze three quantum neural network algorithms. We implemented one of the algorithms on the EuroSAT dataset. We compare the algorithms with respect to complexity and degree of quantization. We believe that the algorithms we propose would be useful for the remote sensing community when quantum computing technologies become widely available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Quantum-Inspired Tensor Network for Earth Science

Deep Learning (DL) is one of many successful methodologies to extract informative patterns and insights from ever increasing noisy large-scale datasets (in our case, satellite images). However, DL models consist of a few thousand to millions of training parameters, and these training parameters require tremendous amount of electrical power for extracting informative patterns from noisy large-scale datasets (e.g., computationally expensive). Hence, we employ a quantum-inspired tensor network for compressing trainable parameters of physics-informed neural networks (PINNs) in Earth science. PINNs are DL models penalized by enforcing the law of physics; in particular, the law of physics is embedded in DL models. In addition, we apply tensor decomposition to HyperSpectral Images (HSIs) to improve their spectral resolution. A quantum-inspired tensor network is also the native formulation to efficiently represent and train quantum machine learning models on big datasets on GPU tensor cores. Furthermore, the key contribution of this paper is twofold: (I) we reduced a number of trainable parameters of PINNs by using a quantum-inspired tensor network, and (II) we improved the spectral resolution of remotely-sensed images by employing tensor decomposition. As a benchmark PDE, we solved Burger’s equation. As practical satellite data, we employed HSIs of Indian Pine, USA and of Pavia University, Italy.
Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images

We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.
Classification With Repeated Observations

We study the problem of nonparametric classification with repeated observations. Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathbf {X}}$</tex-math></inline-formula> be the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> dimensional feature vector and let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Y$</tex-math></inline-formula> denote the label taking values in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lbrace 1,\ldots, M\rbrace$</tex-math></inline-formula> . In contrast to usual setup with large sample size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> and relatively low dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> , this paper deals with the situation, when instead of observing a single feature vector <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathbf {X}}$</tex-math></inline-formula> we are given <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> repeated feature vectors <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathbf {V}}_{1},\ldots, {\mathbf {V}}_{t}$</tex-math></inline-formula> . Some simple classification rules are presented such that the conditional error probabilities have exponential rate of convergence as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t\to \infty$</tex-math></inline-formula> .
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generation wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.