Reviewerly/exHarmony huggingface dataset:
SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data

When cloud layers cover photovoltaic (PV) panels, the amount of power the panels produce fluctuates rapidly. Therefore, to maintain enough energy on a power grid to match demand, utilities companies rely on reserve power sources that typically come from fossil fuels and therefore pollute the environment. Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources. While several studies have developed machine learning models to predict solar irradiance at specific PV generation facilities, little work has been done to model short-term solar irradiance on a global scale. Furthermore, models that have been developed are proprietary and have architectures that are not publicly available or rely on computationally demanding Numerical Weather Prediction (NWP) models. Here, we propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem, is more efficient than NWP models and has a straightforward, reproducible architecture. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU and has a RMSE of 120 W/m2 when evaluated on 2 months of data.
Predicting Physical World Destinations for Commands Given to  Self-Driving Cars

In recent years, we have seen significant steps taken in the development of self-driving cars. Multiple companies are starting to roll out impressive systems that work in a variety of settings. These systems can sometimes give the impression that full self-driving is just around the corner and that we would soon build cars without even a steering wheel. The increase in the level of autonomy and control given to an AI provides an opportunity for new modes of human-vehicle interaction. However, surveys have shown that giving more control to an AI in self-driving cars is accompanied by a degree of uneasiness by passengers. In an attempt to alleviate this issue, recent works have taken a natural language-oriented approach by allowing the passenger to give commands that refer to specific objects in the visual scene. Nevertheless, this is only half the task as the car should also understand the physical destination of the command, which is what we focus on in this paper. We propose an extension in which we annotate the 3D destination that the car needs to reach after executing the given command and evaluate multiple different baselines on predicting this destination location. Additionally, we introduce a model that outperforms the prior works adapted for this particular setting.
HEAT: Hyperedge Attention Networks

Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitation for many domains with highly-structured data. One important such domain is source code, where hypergraph-based representations can better capture the semantically rich and structured nature of code. In this work, we present HEAT, a neural model capable of representing typed and qualified hypergraphs, where each hyperedge explicitly qualifies how participating nodes contribute. It can be viewed as a generalization of both message passing neural networks and Transformers. We evaluate HEAT on knowledge base completion and on bug detection and repair using a novel hypergraph representation of programs. In both settings, it outperforms strong baselines, indicating its power and generality.
PENERAPAN METODE CTL DALAM PEMBELAJARAN KARANGAN DESKRIPSI SISWA KELAS IX SMP NEGERI 2 PALU

The problem in the main research is "How is the application of the CTL method in learning descriptive essays for third grade students of SMP Negeri 2 Palu?". While the objectives to be achieved in this study are to describe the application of the CTL method in learning descriptive essays for third grade students of SMP Negeri 2 Palu. To get the data, the researcher used the technique of literature study, observation, interviews and questionnaires by focusing on learning descriptive essays that were connected to the CTL method used by the teacher in the classroom. The data analysis was carried out in 3 (three) stages, namely (1) data preparation, (2) data processing, (3) data presentation. The results of the study prove that the application of the CTL method in learning descriptive essays for third grade students of SMP Negeri 2 Palu has been running according to the concepts contained in the CTL method. Meanwhile, the components of CTL used by teachers in the field of Indonesian language studies in class III SMP Negeri 2 Palu in learning descriptive essays are Inquiry, Questioning, Constructivism, Community Learning, Authentic Assessment, Reflection and Modeling
Thermal to Visible Image Synthesis under Atmospheric Turbulence

In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions. However, such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images. Such an issue is unavoidable in long-range imaging and significantly decreases the face verification accuracy. In this paper, we first investigate the problem with a turbulence simulation method on real-world thermal images. An end-to-end reconstruction method is then proposed which can directly transform thermal images into visible-spectrum images by utilizing natural image priors based on a pre-trained StyleGAN2 network. Compared with the existing two-steps methods of consecutive turbulence mitigation and thermal to visible image translation, our method is demonstrated to be effective in terms of both the visual quality of the reconstructed results and face verification accuracy. Moreover, to the best of our knowledge, this is the first work that studies the problem of thermal to visible image translation under atmospheric turbulence.
Quasiprobabilistic state-overlap estimator for NISQ devices

As quantum technologies mature, the development of tools for benchmarking their ability to prepare and manipulate complex quantum states becomes increasingly necessary. A key concept, the state overlap between two quantum states, offers a natural tool to verify and cross-validate quantum simulators and quantum computers. Recent progress in controlling and measuring large quantum systems has motivated the development of state overlap estimators of varying efficiency and experimental complexity. Here, we demonstrate a practical approach for measuring the overlap between quantum states based on a factorable quasiprobabilistic representation of the states, and compare it with methods based on randomised measurements. Assuming realistic noisy intermediate scale quantum (NISQ) devices limitations, our quasiprobabilistic method outperforms the best circuits designed for state-overlap estimation for n-qubit states, with n > 2. For n < 7, our technique outperforms also the currently best direct estimator based on randomised local measurements, thus establishing a niche of optimality.
Entanglement criteria for the bosonic and fermionic induced ensembles

Abstract We introduce the bosonic and fermionic ensembles of density matrices and study their entanglement. In the fermionic case, we show that random bipartite fermionic density matrices have non-positive partial transposition; hence, they are typically entangled. The similar analysis in the bosonic case is more delicate, due to a large positive outlier eigenvalue. We compute the asymptotic ratio between the size of the environment and the size of the system Hilbert space for which random bipartite bosonic density matrices fail the PPT criterion, being thus entangled. We also relate moment computations for tensor-symmetric random matrices to evaluations of the circuit counting and interlace graph polynomials for directed graphs.
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering

While Visual Question Answering (VQA) has progressed rapidly, previous works raise concerns about robustness of current VQA models. In this work, we study the robustness of VQA models from a novel perspective: visual context. We suggest that the models over-rely on the visual context, i.e., irrelevant objects in the image, to make predictions. To diagnose the model's reliance on visual context and measure their robustness, we propose a simple yet effective perturbation technique, SwapMix. SwapMix perturbs the visual context by swapping features of irrelevant context objects with features from other objects in the dataset. Using SwapMix we are able to change answers to more than 45 % of the questions for a representative VQA model. Additionally, we train the models with perfect sight and find that the context over-reliance highly depends on the quality of visual representations. In addition to diagnosing, SwapMix can also be applied as a data augmentation strategy during training in order to regularize the context over-reliance. By swapping the context object features, the model reliance on context can be suppressed effectively. Two representative VQA models are studied using SwapMix: a co-attention model MCAN and a large-scale pretrained model LXMERT. Our experiments on the popular GQA dataset show the effectiveness of SwapMix for both diagnosing model robustness and regularizing the over-reliance on visual context. The code for our method is available at https://github.com/vipulgupta1011/swapmix
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an Approximate Bayesian Computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.
A study on cross-corpus speech emotion recognition and data augmentation

Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data complementation and data augmentation on the performance of SER models in matched (test-set from same corpus) and mismatched (test-set from different corpus) conditions. Investigations using six emotional speech corpora that include single and multiple speakers as well as variations in emotion style (acted, elicited, natural) and recording conditions are presented. Observations show that, as expected, models trained on single corpora perform best in matched conditions while performance decreases between 10-40% in mismatched conditions, depending on corpus specific features. Models trained on mixed corpora can be more stable in mismatched contexts, and the performance reductions range from 1 to 8% when compared with single corpus models in matched conditions. Data augmentation yields additional gains up to 4% and seem to benefit mismatched conditions more than matched ones.
Multiparameter quantum metrology in the Heisenberg Limit regime: many  repetition scenario vs. full optimization

We discuss the Heisenberg limit in the multiparameter metrology within two different paradigms -- the one, where the measurement is repeated many times (so the Cram\'er-Rao bound is guaranteed to be asymptotically saturable) and the second one, where all the resources are allocated into one experimental realization (analyzed with the mimimax approach). We investigate the potential advantage of measuring all the parameter simultaneously compared to estimating them individually, while spending the same total amount of resources. We show that in general the existence of such an advantage, its magnitude and conditions under which it occurs depends on which of the two paradigms has been chosen. In particular, for the problem of magnetic field sensing using $N$ entangled spin-$1/2$, we show that the predictions based purely on the Cram\'er-Rao formalism may be overly pessimistic in this matter -- the minimax approach reveals the superiority of measuring all the parameters jointly whereas the Cram\'er-Rao approach indicates lack of such an advantage.
Constructing quantum many-body scar Hamiltonians from Floquet automata

We provide a systematic approach for constructing approximate quantum many-body scars (QMBS) starting from two-layer Floquet automaton circuits that exhibit trivial many-body revivals. We do so by applying successively more restrictions that force local gates of the automaton circuit to commute concomitantly more accurately when acting on select scar states. With these rules in place, an effective local, Floquet Hamiltonian is seen to capture dynamics of the automaton over a long prethermal window. We provide numerical evidence for such a picture and use our construction to derive several QMBS models, including the celebrated PXP model.
Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low Data

Keystroke inference attacks are a form of side-channel attacks in which an attacker leverages various techniques to recover a user's keystrokes as she inputs information into some display (e.g., while sending a text message or entering her pin). Typically, these attacks leverage machine learning approaches, but assessing the realism of the threat space has lagged behind the pace of machine learning advancements, due in-part, to the challenges in curating large real-life datasets. We aim to overcome the challenge of having limited number of real data by introducing a video domain adaptation technique that is able to leverage synthetic data through supervised disentangled learning. Specifically, for a given domain, we decompose the observed data into two factors of variation: Style and Content. Doing so provides four learned representations: real-life style, synthetic style, real-life content and synthetic content. Then, we combine them into feature representations from all combinations of style-content pairings across domains, and train a model on these combined representations to classify the content (i.e., labels) of a given datapoint in the style of another domain. We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover. We show that our method prevents our model from overfitting to a small real-life training set, indicating that our method is an effective form of data augmentation, thereby making keystroke inference attacks more practical.
The role of delay-times in delay-based Photonic Reservoir Computing

Delay-based reservoir computing has gained a lot of attention due to the relative simplicity with which this concept can be implemented in hardware. However,there is still an misconception about the relationship between the delay-time and the input clock-cycle which has noticeable consequences for the performance. We review the existing literature on this subject and introduce the concept of delay-based reservoir computing in a manner that demonstrates that there is no predefined relationship between these two times-scales. Further, we discuss ways to improve the computing performance of a reservoir formed by delay-coupled oscillators and show the crucial impact of delay-time tuning in those multi-delay systems.
Research on an English translation method based on an improved transformer model

Abstract With the expansion of people’s needs, the translation performance of traditional models is increasingly unable to meet current demands. This article mainly studied the Transformer model. First, the structure and principle of the Transformer model were briefly introduced. Then, the model was improved by a generative adversarial network (GAN) to improve the translation effect of the model. Finally, experiments were carried out on the linguistic data consortium (LDC) dataset. It was found that the average Bilingual Evaluation Understudy (BLEU) value of the improved Transformer model improved by 0.49, and the average perplexity value reduced by 10.06 compared with the Transformer model, but the computation speed was not greatly affected. The translation results of the two example sentences showed that the translation of the improved Transformer model was closer to the results of human translation. The experimental results verify that the improved Transformer model can improve the translation quality and be further promoted and applied in practice to further improve the English translation and meet application needs in real life.
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition

Language model fusion helps smart assistants recognize words which are rare in acoustic data but abundant in text-only corpora (typed search logs). However, such corpora have properties that hinder downstream performance, including being (1) too large, (2) beset with domain-mismatched content, and (3) heavy-headed rather than heavy-tailed (excessively many duplicate search queries such as "weather"). We show that three simple strategies for selecting language modeling data can dramatically improve rare-word recognition without harming overall performance. First, to address the heavy-headedness, we downsample the data according to a soft log function, which tunably reduces high frequency (head) sentences. Second, to encourage rare-word exposure, we explicitly filter for words rare in the acoustic data. Finally, we tackle domain-mismatch via perplexity-based contrastive selection, filtering for examples matched to the target domain. We down-select a large corpus of web search queries by a factor of 53x and achieve better LM perplexities than without down-selection. When shallow-fused with a state-of-the-art, production speech engine, our LM achieves WER reductions of up to 24% relative on rare-word sentences (without changing overall WER) compared to a baseline LM trained on the raw corpus. These gains are further validated through favorable side-by-side evaluations on live voice search traffic.
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quadtree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.
Feature selection or extraction decision process for clustering using  PCA and FRSD

This paper concerns the critical decision process of extracting or selecting the features before applying a clustering algorithm. It is not obvious to evaluate the importance of the features since the most popular methods to do it are usually made for a supervised learning technique process. A clustering algorithm is an unsupervised method. It means that there is no known output label to match the input data. This paper proposes a new method to choose the best dimensionality reduction method (selection or extraction) according to the data scientist's parameters, aiming to apply a clustering process at the end. It uses Feature Ranking Process Based on Silhouette Decomposition (FRSD) algorithm, a Principal Component Analysis (PCA) algorithm, and a K-Means algorithm along with its metric, the Silhouette Index (SI). This paper presents 5 use cases based on a smart city dataset. This research also aims to discuss the impacts, the advantages, and the disadvantages of each choice that can be made in this unsupervised learning process.
Non-decimated 2D Wavelet Spectrum and Its Use in Breast Cancer Diagnostics

To improve diagnostic accuracy of breast cancer detection, several researchers have used the wavelet-based tools, which provide additional insight and information for aiding diagnostic decisions. The accuracy of such diagnoses, however, can be improved. This paper introduces a wavelet-based technique, non-decimated wavelet transform (NDWT)-based scaling estimation, that improves scaling parameter estimation over the traditional methods. One distinctive feature of NDWT is that it does not decimate wavelet coefficients at multiscale levels resulting in redundant outputs which are used to lower the variance of scaling estimators. Another interesting feature of the proposed methodology is the freedom of dyadic constraints for inputs, typical for standard wavelet-based approaches. To compare the estimation performance of the NDWT method to a conventional orthogonal wavelet transform-based method, we use simulation to estimate the Hurst exponent in two-dimensional fractional Brownian fields. The results of the simulation show that the proposed method improves the conventional estimators of scaling and yields estimators with smaller mean-squared errors. We apply the NDWT method to classification of mammograms as cancer or control and, for publicly available mammogram images from the database at the University of South Florida, find the the diagnostic accuracy in excess of 80%.
Adaptive Kalman Filter via Just-in-Time Learning for Robots with Unknown Dynamics

In many practice control processes, the measured signal is often noisy, which requires some methods to estimate the actual signal despite the noise. Kalman filter is a popular and effective information fusion state estimation method in many application fields. Conventional Kalman filtering mainly focuses on estimating the state of a known system. The mathematical model in this method is often challenging to obtain as the system becomes increasingly complicated. Aiming at the unknown system, this paper studies a Kalman filtering method based on just-in-time learning. The proposed method can adaptively obtain the actual model of the unknown nonlinear system through the process data and then realize the information fusion with the measurements under the framework of Kalman filtering to accurately estimate the state. The state estimation experiment in the ground vehicle lateral control system verifies the effectiveness of the algorithm.