Miracle-dz/UniFAR-FTUs huggingface dataset:
Local Planner Bench: Benchmarking for Local Motion Planning

Local motion planning is a heavily researched topic in the field of robotics with many promising algorithms being published every year. However, it is difficult and time-consuming to compare different methods in the field. In this paper, we present localPlannerBench, a new benchmarking suite that allows quick and seamless comparison between local motion planning algorithms. The key focus of the project lies in the extensibility of the environment and the simulation cases. Out-of-the-box, localPlannerBench already supports many simulation cases ranging from a simple 2D point mass to full-fledged 3D 7DoF manipulators, and it is straightforward to add your own custom robot using a URDF file. A post-processor is built-in that can be extended with custom metrics and plots. To integrate your own motion planner, simply create a wrapper that derives from the provided base class. Ultimately we aim to improve the reproducibility of local motion planning algorithms and encourage standardized open-source comparison.
Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices

In this paper, we study the network pinning control problem in the presence of two different types of coupling: (i) node-to-node coupling among the network nodes and (ii) input-to-node coupling from the source node to the "pinned nodes. " Previous work has mainly focused on the case that (i) and (ii) are of the same type. We decouple the stability analysis of the target synchronous solution into subproblems of the lowest dimension by using the techniques of simultaneous block diagonalization of matrices. Interestingly, we obtain two different types of blocks, driven and undriven. The overall dimension of the driven blocks is equal to the dimension of an appropriately defined controllable subspace, while all the remaining undriven blocks are scalar. Our main result is a decomposition of the stability problem into four independent sets of equations, which we call quotient controllable, quotient uncontrollable, redundant controllable, and redundant uncontrollable. Our analysis shows that the number and location of the pinned nodes affect the number and the dimension of each set of equations. We also observe that in a large variety of complex networks, the stability of the target synchronous solution is de facto only determined by a single quotient controllable block.
Robustify Transformers with Robust Kernel Density Estimation

Recent advances in Transformer architecture have empowered its empirical success in various tasks across diﬀerent domains. However, existing works mainly focus on improving the standard accuracy and computational cost, without considering the robustness of contaminated samples. Existing work [40] has shown that the self-attention mechanism, which is the center of the Transformer architecture, can be viewed as a non-parametric estimator based on the well-known kernel density estimation (KDE). This motivates us to leverage the robust kernel density estimation (RKDE) in the self-attention mechanism, to alleviate the issue of the contamination of data by down-weighting the weight of bad samples in the estimation process. The modiﬁed self-attention mechanism can be incorporated into diﬀerent Transformer variants. Empirical results on language modeling and image classiﬁcation tasks demonstrate the eﬀectiveness of this approach.
Thermodynamics of the Ising Model Encoded in Restricted Boltzmann Machines

The restricted Boltzmann machine (RBM) is a two-layer energy-based model that uses its hidden–visible connections to learn the underlying distribution of visible units, whose interactions are often complicated by high-order correlations. Previous studies on the Ising model of small system sizes have shown that RBMs are able to accurately learn the Boltzmann distribution and reconstruct thermal quantities at temperatures away from the critical point Tc. How the RBM encodes the Boltzmann distribution and captures the phase transition are, however, not well explained. In this work, we perform RBM learning of the 2d and 3d Ising model and carefully examine how the RBM extracts useful probabilistic and physical information from Ising configurations. We find several indicators derived from the weight matrix that could characterize the Ising phase transition. We verify that the hidden encoding of a visible state tends to have an equal number of positive and negative units, whose sequence is randomly assigned during training and can be inferred by analyzing the weight matrix. We also explore the physical meaning of the visible energy and loss function (pseudo-likelihood) of the RBM and show that they could be harnessed to predict the critical point or estimate physical quantities such as entropy.
Quantum divide and conquer

The divide-and-conquer framework, used extensively in classical algorithm design, recursively breaks a problem of size n into smaller subproblems (say, a copies of size n/b each), along with some auxiliary work of cost C aux ( n ), to give a recurrence relation ) for the classical complexity C ( n ). We describe a quantum divide-and-conquer framework that, in certain cases, yields an analogous recurrence relation that characterizes the quantum query complexity. We apply this framework to obtain near-optimal quantum query complexities for various string problems, such as (i) recognizing regular languages; (ii) decision versions of String Rotation and String Suﬃx; and natural parameterized versions of (iii) Longest Increasing Subsequence and (iv) Longest Common Subsequence.
Neural Approximation of Graph Topological Features

Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods. However, computing extended persistent homology summaries remains slow for large and dense graphs and can be a serious bottleneck for the learning pipeline. Inspired by recent success in neural algorithmic reasoning, we propose a novel graph neural network to estimate extended persistence diagrams (EPDs) on graphs efficiently. Our model is built on algorithmic insights, and benefits from better supervision and closer alignment with the EPD computation algorithm. We validate our method with convincing empirical results on approximating EPDs and downstream graph representation learning tasks. Our method is also efficient; on large and dense graphs, we accelerate the computation by nearly 100 times.
MedJEx: A Medical Jargon Extraction Model with Wiki’s Hyperlink Span and Contextualized Masked Language Model Score

This paper proposes a new natural language processing (NLP) application for identifying medical jargon terms potentially difficult for patients to comprehend from electronic health record (EHR) notes. We first present a novel and publicly available dataset with expert-annotated medical jargon terms from 18K+ EHR note sentences (MedJ). Then, we introduce a novel medical jargon extraction (MedJEx) model which has been shown to outperform existing state-of-the-art NLP models. First, MedJEx improved the overall performance when it was trained on an auxiliary Wikipedia hyperlink span dataset, where hyperlink spans provide additional Wikipedia articles to explain the spans (or terms), and then fine-tuned on the annotated MedJ data. Secondly, we found that a contextualized masked language model score was beneficial for detecting domain-specific unfamiliar jargon terms. Moreover, our results show that training on the auxiliary Wikipedia hyperlink span datasets improved six out of eight biomedical named entity recognition benchmark datasets. MedJEx is publicly available.
COCONUT, a Novel Fast-converging MHD Model for Solar Corona Simulations. II. Assessing the Impact of the Input Magnetic Map on Space-weather Forecasting at Minimum of Activity

This paper is dedicated to the new implicit unstructured coronal code COCONUT, which aims at providing fast and accurate inputs for space-weather forecasting as an alternative to empirical models. We use all 20 available magnetic maps of the solar photosphere covering the date of 2019 July 2, which corresponds to a solar eclipse on Earth. We use the same standard preprocessing on all maps, then perform coronal MHD simulations with the same numerical and physical parameters. We conclude by quantifying the performance of each map using three indicators from remote-sensing observations: white-light total solar eclipse images for the streamers’ edges, EUV synoptic maps for coronal holes, and white-light coronagraph images for the heliospheric current sheet. We discuss the performance of space-weather forecasting and show that the choice of the input magnetic map has a strong impact. We find performances between 24% and 85% for the streamers’ edges, 24%–88% for the coronal hole boundaries, and a mean deviation between 4° and 12° for the heliospheric current sheet position. We find that the HMI runs perform better on all indicators, with GONG-ADAPT being the second-best choice. HMI runs perform better for the streamers’ edges, and GONG-ADAPT for polar coronal holes, HMI synchronic for equatorial coronal holes, and the streamer belt. We especially illustrate the importance of the filling of the poles. This demonstrates that the solar poles have to be taken into account even for ecliptic plane previsions.
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations

Ordinary Diﬀerential Equations (ODEs) have recently gained a lot of attention in machine learning. However, the theoretical aspects, e.g., identiﬁability and asymptotic properties of statistical estimation are still obscure. This paper derives a suﬃcient condition for the identiﬁability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory. When observations are disturbed by measurement noise, we prove that under mild conditions, the parameter estimator based on the Nonlinear Least Squares (NLS) method is consistent and asymptotic normal with n − 1 / 2 convergence rate. Based on the asymptotic normality property, we construct conﬁdence sets for the unknown system parameters and propose a new method to infer the causal structure of the ODE system, i.e., inferring whether there is a causal link between system variables. Furthermore, we extend the results to degraded observations, including aggregated and time-scaled ones. To the best of our knowledge, our work is the ﬁrst systematic study of the identiﬁability and asymptotic properties in learning linear ODE systems. We also construct simulations with various system dimensions to illustrate the established theoretical results.
Digital twins of nonlinear dynamical systems

We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse. We demonstrate that, with single or parallel reservoir computing configurations, the digital twins are capable of challenging forecasting and monitoring tasks. Employing prototypical systems from climate, optics and ecology, we show that the digital twins can extrapolate the dynamics of the target system to certain parameter regimes never experienced before, make continual forecasting/monitoring with sparse real-time updates under non-stationary external driving, infer hidden variables and accurately predict their dynamical evolution, adapt to different forms of external driving, and extrapolate the global bifurcation behaviors to systems of some different sizes. These features make our digital twins appealing in significant applications such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes.
Multi-purpose Pickup and Delivery Problem for Combined Passenger and Freight Transport

Recent developments in modular transport vehicles allow deploying multi-purpose vehicles which can alternately transport di ﬀ erent kinds of ﬂows. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity ﬂow. We solve a series of use case scenarios using an exact optimization algorithm and an adaptive large neighborhood search algorithm. We compare the performance of a multi-purpose vehicle ﬂeet to a mixed single-use vehicle ﬂeet. Our ﬁndings suggest that total costs can be reduced by an average of 13% when multi-purpose vehicles are deployed, while at the same time reducing the total vehicle trip duration and total distance travelled by an average of 33% and 16%, respectively. The size of the ﬂeet can be reduced by an average of 35%. The results can be used by practitioners and policymakers to decide on whether the combination of passenger and freight demand ﬂows with multi-purpose vehicles in a given system will yield beneﬁts compared to existing ﬂeet conﬁgurations.
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation

Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space. This heterogeneous transfer learning problem for CATE estimation is ubiquitous in areas such as healthcare where we may wish to evaluate the effectiveness of a treatment for a new patient population for which different clinical covariates and limited data are available. In this paper, we address this problem by introducing several building blocks that use representation learning to handle the heterogeneous feature spaces and a flexible multi-task architecture with shared and private layers to transfer information between potential outcome functions across domains. Then, we show how these building blocks can be used to recover transfer learning equivalents of the standard CATE learners. On a new semi-synthetic data simulation benchmark for heterogeneous transfer learning we not only demonstrate performance improvements of our heterogeneous transfer causal effect learners across datasets, but also provide insights into the differences between these learners from a transfer perspective.
Constraining Neutrinoless Double-Beta Decay Matrix Elements from Ab Initio Nuclear Theory

As experimental searches for neutrinoless double-beta (0 νββ ) decay are entering a new generation, with hopes to completely probe the inverted mass hierarchy, the need for reliable nuclear matrix elements, which govern the rate of this decay, is stronger than ever. Since a large discrepancy in results is typically found with nuclear models [1], a large unknown still exists on the sensitivity of these experiments to the effective neutrino mass. We consider this problem from a ﬁrst-principles perspective, using the ab initio valence-space in medium similarity renormalization group. In particular, we study correlations of the 0 νββ decay matrix elements in 76 Ge with other observables, such as the double Gamow-Teller giant resonance, from 34 input chiral interactions in an attempt to constrain our uncertainties and investigate the interaction dependence of the nuclear matrix element.
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis

Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that exploring other modalities (e.g., images) increases sentiment analysis performance. State-of-the-art multimodal models, such as CLIP and VisualBERT, are pre-trained on datasets with the text paired with images. Although the results obtained by these models are promising, pre-training and sentiment analysis fine-tuning tasks of these models are computationally expensive. This paper introduces a transfer learning approach using joint fine-tuning for sentiment analysis. Our proposal achieved competitive results using a more straightforward alternative fine-tuning strategy that leverages different pre-trained unimodal models and efficiently combines them in a multimodal space. Moreover, our proposal allows flexibility when incorporating any pre-trained model for texts and images during the joint fine-tuning stage, being especially interesting for sentiment classification in low-resource scenarios.
Cross-Lingual Speaker Identification Using Distant Supervision

Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks. Most existing approaches use expert-defined rules or rule-based features to directly approach this task, but these approaches come with significant drawbacks, such as lack of contextual reasoning and poor cross-lingual generalization. In this work, we propose a speaker identification framework that addresses these issues. We first extract large-scale distant supervision signals in English via general-purpose tools and heuristics, and then apply these weakly-labeled instances with a focus on encouraging contextual reasoning to train a cross-lingual language model. We show that the resulting model outperforms previous state-of-the-art methods on two English speaker identification benchmarks by up to 9% in accuracy and 5% with only distant supervision, as well as two Chinese speaker identification datasets by up to 4.7%.
Deep Counterfactual Estimation with Categorical Background Variables

Referred to as the third rung of the causal inference ladder, counterfactual queries typically ask the"What if ? "question retrospectively. The standard approach to estimate counterfactuals resides in using a structural equation model that accurately reflects the underlying data generating process. However, such models are seldom available in practice and one usually wishes to infer them from observational data alone. Unfortunately, the correct structural equation model is in general not identifiable from the observed factual distribution. Nevertheless, in this work, we show that under the assumption that the main latent contributors to the treatment responses are categorical, the counterfactuals can be still reliably predicted. Building upon this assumption, we introduce CounterFactual Query Prediction (CFQP), a novel method to infer counterfactuals from continuous observations when the background variables are categorical. We show that our method significantly outperforms previously available deep-learning-based counterfactual methods, both theoretically and empirically on time series and image data. Our code is available at https://github.com/edebrouwer/cfqp.
JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE

This paper proposes a model that generates a drum track in the audio domain to play along to a user-provided drum-free recording. Specifically, using paired data of drumless tracks and the corresponding human-made drum tracks, we train a Transformer model to improvise the drum part of an unseen drumless recording. We combine two approaches to encode the input audio. First, we train a vector-quantized variational autoencoder (VQ-VAE) to represent the input audio with discrete codes, which can then be readily used in a Transformer. Second, using an audio-domain beat tracking model, we compute beat-related features of the input audio and use them as embeddings in the Transformer. Instead of generating the drum track directly as waveforms, we use a separate VQ-VAE to encode the mel-spectrogram of a drum track into another set of discrete codes, and train the Transformer to predict the sequence of drum-related discrete codes. The output codes are then converted to a mel-spectrogram with a decoder, and then to the waveform with a vocoder. We report both objective and subjective evaluations of variants of the proposed model, demonstrating that the model with beat information generates drum accompaniment that is rhythmically and stylistically consistent with the input audio.
Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis

Disclaimer: All statements of fact, analysis, or opinion are the author’s and do not reflect the official policy or position of the National Intelligence University, the Department of Defense or any of its components, or the U.S. government. Abstract Efforts by foreign actors to influence public opinion have gained considerable attention because of their potential to impact democratic elections. Thus, the ability to identify and counter sources of disinformation is increasingly becoming a top priority for government entities in order to protect the integrity of democratic processes. This study presents a method of identifying Russian disinformation bots on Twitter using centering resonance analysis and Clauset-Newman-Moore community detection. The data reflect a significant degree of discursive dissimilarity between known Russian disinformation bots and a control set of Twitter users during the timeframe of the 2016 U.S. Presidential Election. The data also demonstrate statistically significant classification capabilities (MCC = 0.9070) based on community clustering. The prediction algorithm is very effective at identifying true positives (bots), but is not able to resolve true negatives (non-bots) because of the lack of discursive similarity between control users. This leads to a highly sensitive means of identifying propagators of disinformation with a high degree of discursive similarity on Twitter, with implications for limiting the spread of disinformation that could impact democratic processes.
fAux: Testing Individual Fairness via Gradient Alignment

Machine learning models are vulnerable to biases that result in unfair treatment of individuals from different populations. Recent work that aims to test a model's fairness at the individual level either relies on domain knowledge to choose metrics, or on input transformations that risk generating out-of-domain samples. We describe a new approach for testing individual fairness that does not have either requirement. We propose a novel criterion for evaluating individual fairness and develop a practical testing method based on this criterion which we call fAux (pronounced fox). This is based on comparing the derivatives of the predictions of the model to be tested with those of an auxiliary model, which predicts the protected variable from the observed data. We show that the proposed method effectively identifies discrimination on both synthetic and real-world datasets, and has quantitative and qualitative advantages over contemporary methods.
Question Answering Over Biological Knowledge Graph via Amazon Alexa

Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A knowledge graph (KG) can be constructed by integrating such facts and data and be used for data integration, exploration, and federated queries. However, exploration and querying large-scale KGs is tedious for certain groups of users due to a lack of knowledge about underlying data assets or semantic technologies. A question-answering (QA) system allows the answer of natural language questions over KGs automatically using triples contained in a KG. Recently, the use and adaption of digital assistants are getting wider owing to their capability at enabling users to voice commands to control smart systems or devices. This paper is about using Amazon Alexa's voice-enabled interface for QA over KGs. As a proof-of-concept, we use the well-known DisgeNET KG, which contains knowledge covering 1.13 million gene-disease associations between 21,671 genes and 30,170 diseases, disorders, and clinical or abnormal human phenotypes. Our study shows how Alex could be of help to find facts about certain biological entities from large-scale knowledge bases.