Reviewerly/exHarmony huggingface dataset:
Attention Mechanisms and Their Applications to Complex Systems

Deep learning models and graphics processing units have completely transformed the field of machine learning. Recurrent neural networks and long short-term memories have been successfully used to model and predict complex systems. However, these classic models do not perform sequential reasoning, a process that guides a task based on perception and memory. In recent years, attention mechanisms have emerged as a promising solution to these problems. In this review, we describe the key aspects of attention mechanisms and some relevant attention techniques and point out why they are a remarkable advance in machine learning. Then, we illustrate some important applications of these techniques in the modeling of complex systems.
Human-Piloted Drone Racing: Visual Processing and Control

Humans race drones faster than algorithms, despite being limited to a fixed camera angle, body rate control, and response latencies in the order of hundreds of milliseconds. A better understanding of the ability of human pilots of selecting appropriate motor commands from highly dynamic visual information may provide key insights for solving current challenges in vision-based autonomous navigation. This work investigates the relationship between human eye movements, control behavior, and flight performance in a drone racing task. We collected a multimodal dataset from 21 experienced drone pilots using a highly realistic drone racing simulator, also used to recruit professional pilots. Our results show task-specific improvements in drone racing performance over time. In particular, we found that eye gaze tracks future waypoints (i.e., gates), with first fixations occurring on average 1.5 seconds and 16 meters before reaching the gate. Moreover, human pilots consistently looked at the inside of the future flight path for lateral (i.e., left and right turns) and vertical maneuvers (i.e., ascending and descending). Finally, we found a strong correlation between pilots' eye movements and the commanded direction of quadrotor flight, with an average visual-motor response latency of 220 ms. These results highlight the importance of coordinated eye movements in human-piloted drone racing. We make our dataset publicly available.
Efficient Coverage Path Planning for Mobile Disinfecting Robots Using Graph-Based Representation of Environment

The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.
Nondestructive detection of photonic qubits

One of the biggest challenges in experimental quantum information is to sustain the fragile superposition state of a qubit1. Long lifetimes can be achieved for material qubit carriers as memories2, at least in principle, but not for propagating photons that are rapidly lost by absorption, diffraction or scattering3. The loss problem can be mitigated with a nondestructive photonic qubit detector that heralds the photon without destroying the encoded qubit. Such a detector is envisioned to facilitate protocols in which distributed tasks depend on the successful dissemination of photonic qubits4,5, improve loss-sensitive qubit measurements6,7 and enable certain quantum key distribution attacks8. Here we demonstrate such a detector based on a single atom in two crossed fibre-based optical resonators, one for qubit-insensitive atom-photon coupling and the other for atomic-state detection9. We achieve a nondestructive detection efficiency upon qubit survival of 79 ± 3 per cent and a photon survival probability of 31 ± 1 per cent, and we preserve the qubit information with a fidelity of 96.2 ± 0.3 per cent. To illustrate the potential of our detector, we show that it can, with the current parameters, improve the rate and fidelity of long-distance entanglement and quantum state distribution compared to previous methods, provide resource optimization via qubit amplification and enable detection-loophole-free Bell tests.
BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression

The slow speed of BERT has motivated much research on accelerating its inference, and the early exiting idea has been proposed to make trade-offs between model quality and efficiency.This paper aims to address two weaknesses of previous work: (1) existing fine-tuning strategies for early exiting models fail to take full advantage of BERT; (2) methods to make exiting decisions are limited to classification tasks.We propose a more advanced fine-tuning strategy and a learning-toexit module that extends early exiting to tasks other than classification.Experiments demonstrate improved early exiting for BERT, with better trade-offs obtained by the proposed finetuning strategy, successful application to regression tasks, and the possibility to combine it with other acceleration methods.
Metal source and ore-forming process of the Maoping carbonate-hosted Pb-Zn deposit in Yunnan, SW China: Evidence from deposit geology and sphalerite Pb-Zn-Cd isotopes

The Maoping carbonate-hosted Pb-Zn deposit (25 Mt ores @ 12–35% Zn + Pb) is a representative and the third-largest Pb-Zn deposit in the Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province (SYGMP). In this study, Pb-Zn-Cd isotope analyses were performed on sphalerite from Maoping, in order to understand the ore metal source and metallogenesis in the SYGMP. The sphalerite samples have narrow ranges of Pb isotope ratios: 206Pb/204Pb = 18.675–18.751, 207Pb/204Pb = 15.758–15.768, and 208Pb/204Pb = 39.287–38.436. This implies thorough mixing of two Pb sources, which we interpreted to be the Devonian-Carboniferous ore-hosting carbonate wallrocks and the Neoproterozoic Kunyang/Huili Group (Gp.) clastic and metamorphic rocks. The Maoping sphalerite samples have uniform δ66Zn (−80.07 to + 0.25‰) and δ114Cd (−0.10 to + 0.27‰) values, which further support complete source mixing. In the δ66Zn vs. Cd/Zn plot, the Maoping sphalerite data fall close to the field of basement rocks and distal to the carbonate wallrocks, indicating that the ore zinc was mainly derived from the metamorphic basement with heavier δ66Zn values and low Cd/Zn ratios with certain contributions from the minor carbonate wallrock with lower δ66Zn values and high Cd/Zn ratios. In the δ114Cd vs. 1/Cd plot, all Maoping sphalerite samples fall inside the Mississippi Valley-type (MVT) field, and the deposit shares many geological and sulfide geochemical (incl. isotopic) features with MVT deposits. Thus, the Maoping Pb-Zn mineralization is best classified as MVT, and the sulfide precipitation in structural (fault-fold) traps was caused by binary mixing of a metal-bearing and a reduced S-bearing fluid.
Transfer learning: improving neural network based prediction of earthquake ground shaking for an area with insufficient training data

SUMMARY In a recent study, we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicentre. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs significantly from the standard procedure adopted by earthquake early warning systems that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation (39 stations) waveforms for the 2016 earthquake sequence in central Italy for 915 M ≥ 3.0 events (CI data set). The CI data set has a large number of spatially concentrated earthquakes and a dense network of stations. In this work, we applied the same CNN model to an area of central western Italy. In our initial application of the technique, we used a data set consisting of 266 M ≥ 3.0 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller-sized data set performed worse compared to the results presented in the previously published study. To counter the lack of data, we explored the adoption of ‘transfer learning’ (TL) methodologies using two approaches: first, by using a pre-trained model built on the CI data set and, next, by using a pre-trained model built on a different (seismological) problem that has a larger data set available for training. We show that the use of TL improves the results in terms of outliers, bias and variability of the residuals between predicted and true IM values. We also demonstrate that adding knowledge of station relative positions as an additional layer in the neural network improves the results. The improvements achieved through the experiments were demonstrated by the reduction of the number of outliers by 5 per cent, the residuals R median by 39 per cent and their standard deviation by 11 per cent.
Open Domain Question Answering over Tables via Dense Retrieval

Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages.In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context.We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives.As relevant datasets are missing, we extract a subset of NATURAL QUESTIONS (Kwiatkowski et al., 2019) into a Table QA dataset.We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever.
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction

Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.Most existing methods assume that events appear in sentences without overlaps, which are not applicable to the complicated overlapping event extraction.This work systematically studies the realistic event overlapping problem, where a word may serve as triggers with several types or arguments with different roles.To tackle the above problem, we propose a novel joint learning framework with cascade decoding for overlapping event extraction, termed as CasEE.Particularly, CasEE sequentially performs type detection, trigger extraction and argument extraction, where the overlapped targets are extracted separately conditioned on the specific former prediction.All the subtasks are jointly learned in a framework to capture dependencies among the subtasks.The evaluation on a public event extraction benchmark FewFC demonstrates that CasEE 1 achieves significant improvements on overlapping event extraction over previous competitive methods.
Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion

Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse measurements from low-cost active depth sensors. We introduce Sparse Auxiliary Networks (SANs), a new module enabling monodepth networks to perform both the tasks of depth prediction and completion, depending on whether only RGB images or also sparse point clouds are available at inference time. First, we decouple the image and depth map encoding stages using sparse convolutions to process only the valid depth map pixels. Second, we inject this information, when available, into the skip connections of the depth prediction network, augmenting its features. Through extensive experimental analysis on one indoor (NYUv2) and two outdoor (KITTI and DDAD) benchmarks, we demonstrate that our proposed SAN architecture is able to simultaneously learn both tasks, while achieving a new state of the art in depth prediction by a significant margin.
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment.Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three elements and has the problem of error propagation.In this paper, we propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model (S 3 E 2 ) to fully exploit the syntactic and semantic relationships between the triplet elements and jointly extract them.Specifically, we design a Graph-Sequence duel representation and modeling paradigm for the task of ASTE: we represent the semantic and syntactic relationships between word pairs in a sentence by graph and encode it by Graph Neural Networks (GNNs), as well as modeling the original sentence by LSTM to preserve the sequential information.Under this setting, we further apply a more efficient inference strategy for the extraction of triplets.Extensive evaluations on four benchmark datasets show that S 3 E 2 significantly outperforms existing approaches, which proves our S 3 E 2 's superiority and flexibility in an end-to-end fashion.
Text-Guided Graph Neural Networks for Referring 3D Instance Segmentation

This paper addresses a new task called referring 3D instance segmentation, which aims to segment out the target instance in a 3D scene given a query sentence. Previous work on scene understanding has explored visual grounding with natural language guidance, yet the emphasis is mostly constrained on images and videos. We propose a Text-guided Graph Neural Network (TGNN) for referring 3D instance segmentation on point clouds. Given a query sentence and the point cloud of a 3D scene, our method learns to extract per-point features and predicts an offset to shift each point toward its object center. Based on the point features and the offsets, we cluster the points to produce fused features and coordinates for the candidate objects. The resulting clusters are modeled as nodes in a Graph Neural Network to learn the representations that encompass the relation structure for each candidate object. The GNN layers leverage each object's features and its relations with neighbors to generate an attention heatmap for the input sentence expression. Finally, the attention heatmap is used to "guide" the aggregation of information from neighborhood nodes. Our method achieves state-of-the-art performance on referring 3D instance segmentation and 3D localization on ScanRefer, Nr3D, and Sr3D benchmarks, respectively.
A Survey of Race, Racism, and Anti-Racism in NLP

Despite inextricable ties between race and language, little work has considered race in NLP research and development.In this work, we survey 79 papers from the ACL anthology that mention race.These papers reveal various types of race-related bias in all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies.However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed singledimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature.By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.
Deep Semantic Dictionary Learning for Multi-label Image Classification

Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic information as type of complements for visual representation without further exploitation. In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. A novel end-to-end model named Deep Semantic Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to generate the semantic dictionary from class-level semantics and then such dictionary is utilized for representing the visual features extracted by Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a simple but elegant way to exploit and reconcile the label, semantic and visual spaces simultaneously via conducting the dictionary learning among them. Moreover, inspired by iterative optimization of traditional dictionary learning, we further devise a novel training strategy named Alternately Parameters Update Strategy (APUS) for optimizing DSDL, which alternately optimizes the representation coefficients and the semantic dictionary in forward and backward propagation. Extensive experimental results on three popular benchmarks demonstrate that our method achieves promising performances in comparison with the state-of-the-arts. Our codes and models have been released.
Language Conditioned Imitation Learning Over Unstructured Data

Natural language is perhaps the most flexible and intuitive way for humans to communicate tasks to a robot.Prior work in imitation learning typically requires each task be specified with a task id or goal image-something that is often impractical in open-world environments.On the other hand, previous approaches in instruction following allow agent behavior to be guided by language, but typically assume structure in the observations, actuators, or language that limit their applicability to complex settings like robotics.In this work, we present a method for incorporating free-form natural language conditioning into imitation learning.Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network.Unlike prior work in imitation learning, our method is able to incorporate unlabeled and unstructured demonstration data (i.e.no task or language labels).We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data.At test time, a single language conditioned visuomotor policy trained with our method can perform a wide variety of robotic manipulation skills in a 3D environment, specified only with natural language descriptions of each task (e.g."open the drawer...now pick up the block...now press the green button...") (see video).To scale up the number of instructions an agent can follow, we propose combining text conditioned policies with large pretrained neural language models.We find this allows a policy to be robust to many out-of-distribution synonym instructions, without requiring new demonstrations.See videos of a human typing live text commands to our agent at https://groundinglanguage.github.io
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Adapter-based tuning has recently arisen as an alternative to fine-tuning.It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating the parameters of adapter modules when learning on a downstream task.As such, it adds only a few trainable parameters per new task, allowing a high degree of parameter sharing.Prior studies have shown that adapter-based tuning often achieves comparable results to finetuning.However, existing work only focuses on the parameter-efficient aspect of adapterbased tuning while lacking further investigation on its effectiveness.In this paper, we study the latter.We first show that adapterbased tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM.We then empirically compare the two tuning methods on several downstream NLP tasks and settings.We demonstrate that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.
Bad Seeds: Evaluating Lexical Methods for Bias Measurement

A common factor in bias measurement methods is the use of hand-curated seed lexicons, but there remains little guidance for their selection.We gather seeds used in prior work, documenting their common sources and rationales, and in case studies of three English-language corpora, we enumerate the different types of social biases and linguistic features that, once encoded in the seeds, can affect subsequent bias measurements.Seeds developed in one context are often re-used in other contexts, but documentation and evaluation remain necessary precursors to relying on seeds for sensitive measurements.
Argumentative XAI: A Survey

Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years. Among various approaches to XAI, argumentative models have been advocated in both the AI and social science literature, as their dialectical nature appears to match some basic desirable features of the explanation activity. In this survey we overview XAI approaches built using methods from the field of computational argumentation, leveraging its wide array of reasoning abstractions and explanation delivery methods. We overview the literature focusing on different types of explanation (intrinsic and post-hoc), different models with which argumentation-based explanations are deployed, different forms of delivery, and different argumentation frameworks they use. We also lay out a roadmap for future work.