Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Information Change in Science Communication with Semantically Matched Paraphrases

Whether the media faithfully communicate scientific information has long been a core issue to the science community. Automatically identifying paraphrased scientific findings could enable large-scale tracking and analysis of information changes in the science communication process, but this requires systems to understand the similarity between scientific information across multiple domains. To this end, we present the SCIENTIFIC PARAPHRASE AND INFORMATION CHANGE DATASET (SPICED), the first paraphrase dataset of scientific findings annotated for degree of information change. SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers. We demonstrate that SPICED poses a challenging task and that models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims. Finally, we show that models trained on SPICED can reveal large-scale trends in the degrees to which people and organizations faithfully communicate new scientific findings. Data, code, and pre-trained models are available at http://www.copenlu. com/publication/2022_emnlp_wright/.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables

One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result in over-conservative policies. To balance the worst-case (robustness) and average performance, we propose Group Distributionally Robust Markov Decision Process (GDR-MDP), a flexible hierarchical MDP formulation that encodes task groups via a latent mixture model. GDR-MDP identifies the optimal policy that maximizes the expected return under the worst-possible qualified belief over task groups within an ambiguity set. We rigorously show that GDR-MDP's hierarchical structure improves distributional robustness by adding regularization to the worst possible outcomes. We then develop deep RL algorithms for GDR-MDP for both value-based and policy-based RL methods. Extensive experiments on Box2D control tasks, MuJoCo benchmarks, and Google football platforms show that our algorithms outperform classic robust training algorithms across diverse environments in terms of robustness under belief uncertainties. Demos are available on our project page (\url{https://sites.google.com/view/gdr-rl/home}).
Equivalence Checking of Parameterized Quantum Circuits: Verifying the Compilation of Variational Quantum Algorithms

Variational quantum algorithms have been introduced as a promising class of quantum-classical hybrid algorithms that can already be used with the noisy quantum computing hardware available today by employing parameterized quantum circuits. Considering the non-trivial nature of quantum circuit compilation and the subtleties of quantum computing, it is essential to verify that these parameterized circuits have been compiled correctly. Established equivalence checking procedures that handle parameter-free circuits already exist. However, no methodology capable of handling circuits with parameters has been proposed yet. This work fills this gap by showing that verifying the equivalence of parameterized circuits can be achieved in a purely symbolic fashion using an equivalence checking approach based on the ZX calculus. At the same time, proofs of inequality can be efficiently obtained with conventional methods by taking advantage of the degrees of freedom inherent to parameterized circuits. We implemented the corresponding methods and proved that the resulting methodology is complete. Experimental evaluations (using the entire parametric ansatz circuit library provided by Qiskit as benchmarks) demonstrate the efficacy of the proposed approach.
Social norms of fairness with reputation-based role assignment in the dictator game

A vast body of experiments share the view that social norms are major factors for the emergence of fairness in a population of individuals playing the dictator game (DG). Recently, to explore which social norms are conducive to sustaining cooperation has obtained considerable concern. However, thus, far few studies have investigated how social norms influence the evolution of fairness by means of indirect reciprocity. In this study, we propose an indirect reciprocal model of the DG and consider that an individual can be assigned as the dictator due to its good reputation. We investigate the "leading eight" norms and all second-order social norms by a two-timescale theoretical analysis. We show that when role assignment is based on reputation, four of the "leading eight" norms, including stern judging and simple standing, lead to a high level of fairness, which increases with the selection intensity. Our work also reveals that not only the correct treatment of making a fair split with good recipients but also distinguishing unjustified unfair split from justified unfair split matters in elevating the level of fairness.
Investigating the effect of domain selection on automatic speech recognition performance: a case study on Bangladeshi Bangla

The performance of data-driven natural language processing systems is contingent upon the quality of corpora. However, principal corpus design criteria are often not identified and examined adequately, particularly in the speech processing discipline. Speech corpora development requires additional attention with regard to clean/noisy, read/spontaneous, multi-talker speech, accents/dialects, etc. Domain selection is also a crucial decision point in speech corpus development. In this study, we demonstrate the significance of domain selection by assessing a state-of-the-art Bangla automatic speech recognition (ASR) model on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark — BanSpeech, which contains 7.2 hours of speech and 9802 utterances from 19 distinct domains. The ASR model has been trained with deep convolutional neural network (CNN), layer normalization technique, and Connectionist Temporal Classification (CTC) loss criterion on SUBAK.KO, a mostly read speech corpus for the low-resource and morphologically rich language Bangla. Experimental evaluation reveals the ASR model on SUBAK.KO faces difficulty recognizing speech from domains with mostly spontaneous speech and has a high number of out-of-vocabulary (OOV) words. The same ASR model, on the other hand, performs better in read speech domains and contains fewer OOV words. In addition, we report the outcomes of our experiments with layer normalization, input feature extraction, number of convolutional layers, etc., and set a baseline on SUBAK.KO. The BanSpeech will be publicly available to meet the need for a challenging evaluation benchmark for Bangla ASR.
Translation Word-Level Auto-Completion: What Can We Achieve Out of the Box?

Research on Machine Translation (MT) has achieved important breakthroughs in several areas. While there is much more to be done in order to build on this success, we believe that the language industry needs better ways to take full advantage of current achievements. Due to a combination of factors, including time, resources, and skills, businesses tend to apply pragmatism into their AI workflows. Hence, they concentrate more on outcomes, e.g. delivery, shipping, releases, and features, and adopt high-level working production solutions, where possible. Among the features thought to be helpful for translators are sentence-level and word-level translation auto-suggestion and auto-completion. Suggesting alternatives can inspire translators and limit their need to refer to external resources, which hopefully boosts their productivity. This work describes our submissions to WMT’s shared task on word-level auto-completion, for the Chinese-to-English, English-to-Chinese, German-to-English, and English-to-German language directions. We investigate the possibility of using pre-trained models and out-of-the-box features from available libraries. We employ random sampling to generate diverse alternatives, which reveals good results. Furthermore, we introduce our open-source API, based on CTranslate2, to serve translations, auto-suggestions, and auto-completions.
Learning to Advise Humans By Leveraging Algorithm Discretion

Expert decision-makers (DMs) in high-stakes AI-aDvised Team (AIDeT) settings receive and reconcile recommendations from AI systems before making their ﬁnal decisions. We identify distinct properties of these settings which are key to developing AIDeT models that effectively beneﬁt team performance. First, DMs in AIDeT settings exhibit algorithm discretion behavior (ADB), i.e., an idiosyncratic tendency to imperfectly accept or reject algorithmic recommendations for any given decision task. Second, DMs incur contradiction costs from exerting decision-making resources (e.g., time and effort) when reconciling AI recommendations that contradict their own judgment. Third, the human’s imperfect discretion and reconciliation costs introduce the need for the AI to offer advice selectively . We refer to the task of developing AI to advise humans in AIDeT settings as learning to advise and we address this task by ﬁrst introducing the AIDeT-Learning Framework. Additionally, we argue that leveraging the human partner’s ADB is key to maximizing the AIDeT’s decision accuracy while regularizing for contradiction costs. Finally, we instantiate our framework to develop TeamRules ( TR ): an algorithm that produces rule-based models and recommendations for AIDeT settings. TR is optimized to selectively advise a human and to trade-off contradiction costs and team accuracy for a given environment by leveraging the human partner’s ADB. Evaluations on synthetic and real-world benchmark datasets with a variety of simulated human accuracy and discretion behaviors show that TR robustly improves the team’s objective across settings over interpretable, rule-based alternatives.
Removing Radio Frequency Interference From Auroral Kilometric Radiation With Stacked Autoencoders

Radio frequency data in astronomy enable scientists to analyze astrophysical phenomena. However, these data can be corrupted by radio frequency interference (RFI) that limits the observation of underlying natural processes. In this study, we extend recent developments in deep learning algorithms to astronomy data. We remove RFI from time-frequency spectrograms containing auroral kilometric radiation (AKR), a coherent radio emission originating from the Earth’s auroral zones that is used to study astrophysical plasmas. We propose a Denoising Autoencoder for Auroral Radio Emissions (DAARE) trained with synthetic spectrograms to denoise AKR signals collected at the South Pole Station. DAARE achieves 42.2 peak signal-to-noise ratio (PSNR) and 0.981 structural similarity (SSIM) on synthesized AKR observations, improving PSNR by 3.9 and SSIM by 0.064 compared to state-of-the-art filtering and denoising networks. Qualitative comparisons demonstrate DAARE’s capability to effectively remove RFI from real AKR observations, despite being trained completely on a dataset of simulated AKR. The framework for simulating AKR, training DAARE, and employing DAARE can be accessed at github.com/Cylumn/daare.
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data

Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences. In this paper, we introduce a new RL formulation for text generation from the soft Q-learning (SQL) perspective. It enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward. We apply the approach to a wide range of novel text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation. Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods.
High Mobility p -type van der Waals One Dimensional PdBr 2

One-dimensional (1D) van der Waals (vdW) materials oﬀer nearly defect-free strands as channel material in the ﬁeld-eﬀect transistor (FET) devices and probably, a better interconnect than conventional copper with higher current density and resistance to electro-migration with sustain-able down-scaling. We report a new halide based “truly” 1D few-chain atomic thread, PdBr 2 , isolable from its bulk which crystallizes in a monoclinic space group C2/c. Liquid phase exfoliated nanowires with mean length (20 ± 1) µ m transferred onto SiO 2 /Si wafer with a maximum aspect ratio (length:width) of ≈ 5000 conﬁrms the lower cleavage energy perpendicular to chain direction. Gate-dependent electrical transport measurement of a single wire bridged between source-drain electrodes conﬁrms the out-of-the-common p-type FET characteristics with the highest mobility of 6850 cm 2 V − 1 s − 1 and a maximum ON/OFF ratio of 10 4 . Moreover, an isolated nanowire can also sustain current density of 200 MA/cm 2 which is atleast one-order higher than typical copper interconnects. However, local transport measurement via conducting atomic force microscopy (CAFM) tip along the cross direction of the single chain records a much lower current density due to the anisotropic electronic band structure. While 1D nature of the nanoobject can be linked with non-trivial collective quantum behavior, vdW nature could be beneﬁcial for the new pathways in interconnect fabrication strategy with better control of placement in an integrated circuit (IC).
Sim-to-Real Learning of Compliant Bipedal Locomotion on Torque Sensor-Less Gear-Driven Humanoid

—Sim-to-real is a mainstream method to cope with the large number of trials needed by typical deep reinforcement learning. However, transferring a policy trained in simulation to actual hardware remains challenging due to the reality gap . In particular, the characteristics of actuators in legged robots have a considerable inﬂuence on sim-to-real transfer. High reduction ratio gears are widely used in actuators, and the reality gap issue becomes especially pronounced when even the utilization of backdrivability is considered to control joints compliantly. We propose a new simulation model of gears to address this gap. Additionally, the difﬁculty in achieving stable bipedal locomotion causes typical methods to fail to tune physical parameters in simulation with the behavior of transferred policy. Thus, we propose a method for system identiﬁcation that can utilize failed attempts. The method’s effectiveness is veriﬁed using a biped robot, the ROBOTIS-OP3, and the sim-to-real transferred policy can stabilize the robot under severe disturbances and walk on uneven surfaces without force and torque sensors. 1
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling

Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequences, both taken at irregular time intervals. Dealing with such irregularity in every modality, and integrating irregularity into multimodal representations to improve medical predictions, is a challenging problem. Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism. We further integrate irregularity in multimodal fusion with an interleaved attention mechanism across temporal steps. To the best of our knowledge, this is the first work to thoroughly model irregularity in multimodalities for improving medical predictions. Our proposed methods for two medical prediction tasks consistently outperforms state-of-the-art (SOTA) baselines in each single modality and multimodal fusion scenarios. Specifically, we observe relative improvements of 6.5\%, 3.6\%, and 4.3\% in F1 for time series, clinical notes, and multimodal fusion, respectively. These results demonstrate the effectiveness of our methods and the importance of considering irregularity in multimodal EHRs.
AI-based Arabic Language and Speech Tutor

In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intelligent self-learning environment that goes beyond multiple-choice questions and/or fill in the blank exercises. In addition, NLP allows for learning to be adaptive in that it offers more than an indication that an error has occurred. It also provides a description of the error, uses linguistic analysis to isolate the source of the error, and then suggests additional drills to achieve optimal individualized learning outcomes. In this paper, we present our approach for developing an Artificial Intelligence-based Arabic Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect. The AI-ALST system is an intelligent tutor that provides analysis and assessment of students learning the Moroccan dialect at University of Arizona (UA). The AI-ALST provides a self-learned environment to practice each lesson for pronunciation training. In this paper, we present our initial experimental evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory), attention mechanism, and a cost-based strategy for dealing with class-imbalance learning. We evaluated our tutor on the word pronunciation of lesson 1 of the Moroccan Arabic dialect class. The experimental results show that the AI-ALST can effectively and successfully detect pronunciation errors and evaluate its performance by using $\boldsymbol{F}_{\mathbf{1}}$ - score, accuracy, precision, and recall.
Planning Coordinated Human-Robot Motions with Neural Network Full-Body Prediction Models

—Numerical optimization has become a popular ap- proach to plan smooth motion trajectories for robots. However, when sharing space with humans, balancing properly safety, comfort and efﬁciency still remains challenging. This is notably the case because humans adapt their behavior to that of the robot, raising the need for intricate planning and prediction. In this paper, we propose a novel optimization-based motion planning algorithm, which generates robot motions, while simultaneously maximizing the human trajectory likelihood under a data-driven predictive model. Considering planning and prediction together allows us to formulate objective and constraint functions in the joint human-robot state space. Key to the approach are added latent space modiﬁers to a differentiable human predictive model based on a dedicated recurrent neural network. These modiﬁers allow to change the human prediction within motion optimization.Weempirically evaluate our method using the publicly avail-able MoGaze [1] dataset. Our results indicate that the proposed framework outperforms current baselines for planning handover trajectories and avoiding collisions between a robot and a human. Our experiments demonstrate collaborative motion trajectories, where both, the human prediction and the robot plan, adapt to each other.
Exploring ultra-high-intensity wakefields in carbon nanotube arrays: An effective plasma-density approach

Charged particle acceleration using solid-state nanostructures has attracted attention in recent years as a method of achieving ultra-high-gradient acceleration in the TV/m domain. More concretely, metallic hollow nanostructures could be suitable for particle acceleration through the excitation of wakefields by a laser or a high-intensity charged particle beam in a high-density solid-state plasma. For instance, due to their special channeling properties as well as optoelectronic and thermo-mechanical properties, carbon nanotubes could be an excellent medium for this purpose. This article investigates the feasibility of generating ultra-high-gradient acceleration using carbon nanotube arrays, modeled as solid-state plasmas in conventional particle-in-cell simulations performed in a two-dimensional axisymmetric (quasi-3D) geometry. The generation of beam-driven plasma wakefields depending on different parameters of the solid structure is discussed in detail. Furthermore, by adopting an effective plasma-density approach, existing analytical expressions, originally derived for homogeneous plasmas, can be used to describe wakefields driven in periodic non-uniform plasmas.
Containing the spread of a contagion on a tree

Contact tracing can be thought of as a race between two processes: an infection process and a tracing process. In this paper, we study a simple model of infection spreading on a tree, and a tracer who stabilizes one node at a time. We focus on the question, how should the tracer choose nodes to stabilize so as to prevent the infection from spreading further? We study simple policies, which prioritize nodes based on time, infectiousness, or probability of generating new contacts.
Top-quark loops and the muon anomalous magnetic moment

The current status of electroweak radiative corrections to the muon anomalous magnetic moment is discussed. Asymptotic expansions for some important electroweak two-loop top quark triangle diagrams are illustrated and extended to higher order. Results are compared with the more general integral representation solution for generic fermion triangle loops coupled to pseudoscalar and scalar bosons of arbitrary mass. Furthermore, excellent agreement is found for a broader than expected range of mass parameters.
On All-Action Policy Gradients

In this paper, we analyze the variance of stochastic policy gradient with many action samples per state (all-action SPG). We decompose the variance of SPG and derive an optimality condition for all-action SPG. The optimality condition shows when all-action SPG should be preferred over single-action counterpart and allows to determine a variance-minimizing sampling scheme in SPG estimation. Furthermore, we propose dynamics-all-action (DAA) module, an augmentation that allows for all-action sampling without manipulation of the environment. DAA addresses the problems associated with using a Q-network for all-action sampling and can be readily applied to any on-policy SPG algorithm. We ﬁnd that using DAA with a canonical on-policy algorithm (PPO) yields better sample efﬁciency and higher policy returns on a variety of continuous action environments.