Miracle-dz/UniFAR-FTUs huggingface dataset:
ADAPT: Mitigating Idling Errors in Qubits via Adaptive Dynamical Decoupling

The fidelity of applications on near-term quantum computers is limited by hardware errors. In addition to errors that occur during gate and measurement operations, a qubit is susceptible to idling errors, which occur when the qubit is idle and not actively undergoing any operations. To mitigate idling errors, prior works in the quantum devices community have proposed Dynamical Decoupling (DD), that reduces stray noise on idle qubits by continuously executing a specific sequence of single-qubit operations that effectively behave as an identity gate. Unfortunately, existing DD protocols have been primarily studied for individual qubits and their efficacy at the application-level is not yet fully understood. Our experiments show that naively enabling DD for every idle qubit does not necessarily improve fidelity. While DD reduces the idling error-rates for some qubits, it increases the overall error-rate for others due to the additional operations of the DD protocol. Furthermore, idling errors are program-specific and the set of qubits that benefit from DD changes with each program. To enable robust use of DD, we propose Adaptive Dynamical Decoupling (ADAPT), a software framework that estimates the efficacy of DD for each qubit combination and judiciously applies DD only to the subset of qubits that provide the most benefit. ADAPT employs a Decoy Circuit, which is structurally similar to the original program but with a known solution, to identify the DD sequence that maximizes the fidelity. To avoid the exponential search of all possible DD combinations, ADAPT employs a localized algorithm that has linear complexity in the number of qubits. Our experiments on IBM quantum machines (with 16-27 qubits) show that ADAPT improves the application fidelity by 1.86x on average and up-to 5.73x compared to no DD and by 1.2x compared to DD on all qubits.
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entity retrieval shows that the dual-encoder structure is a powerful and efficient framework to nominate candidates if entities are only identified by descriptions. However, they ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions, which are treated equally in previous works. In this work, we propose Multi-View Entity Representations (MuVER), a novel approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. Our method achieves the state-of-the-art performance on ZESHEL and improves the quality of candidates on three standard Entity Linking datasets.
Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion

Clinical trials are a multi-billion-dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.
Universal Protoplanetary Disk Size under Complete Nonideal Magnetohydrodynamics: The Interplay between Ion-neutral Friction, Hall Effect, and Ohmic Dissipation

The role of nonideal magnetohydrodynamics has been proven critical during the formation of protoplanetary disks, particularly in regulating their sizes. We provide a simple model to predict the disk size under the interplay among ambipolar diffusion, the Hall effect, and ohmic dissipation. The model predicts a small disk size of around 20 au that depends only sublinearly on disk parameters, for a wide range of initial conditions of subsolar mass and moderate magnetization. It is able to explain phenomena manifested in existing numerical simulations, including the bimodal disk behavior under parallel and antiparallel alignment between the rotation and magnetic field. In the parallel configuration, the disk size decreases and eventually disappears. In the antiparallel configuration, the disk has an outer partition (or pseudodisk), which is flat, shrinking, and short-lived, as well as an inner partition, which grows slowly with mass and is long-lived. Even with significant initial magnetization, the vertical field in the disk can only dominate at the early stage when the mass is low, and the toroidal field eventually dominates in all disks.
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding

When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
Wine is Not v i n. - On the Compatibility of Tokenizations Across Languages

The size of the vocabulary is a central design choice in large pretrained language models, with respect to both performance and memory requirements. Typically, subword tokenization algorithms such as byte pair encoding and WordPiece are used. In this work, we investigate the compatibility of tokenizations for multilingual static and contextualized embedding spaces and propose a measure that reflects the compatibility of tokenizations across languages. Our goal is to prevent incompatible tokenizations, e.g.,"wine"(word-level) in English vs.\"v i n"(character-level) in French, which make it hard to learn good multilingual semantic representations. We show that our compatibility measure allows the system designer to create vocabularies across languages that are compatible -- a desideratum that so far has been neglected in multilingual models.
DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection

Object detection is a fundamental task in computer vision. While approaches for axis-aligned bounding box detection have made substantial progress in recent years, they perform poorly on oriented objects which are common in several real-world scenarios such as aerial view imagery and security camera footage. In these cases, a large part of a predicted bounding box will, undesirably, cover non-object related areas. Therefore, oriented object detection has emerged with the aim of generalizing object detection to arbitrary orientations. This enables a tighter ﬁt to oriented objects, leading to a better separation of bounding boxes especially in case of dense object distributions. The vast majority of the work in this area has focused on complex two-stage anchor-based approaches, where the detection is split into a region-of-interest identiﬁcation step followed by the object localization and classiﬁcation based on sets of predeﬁned bounding box anchors. These anchors act as priors on the bounding box shape and require attentive hyper-parameter ﬁne-tuning on a per-dataset basis, increased model size, and come with computational overhead. In this work, we present DAFNe: A Dense one-stage Anchor-Free deep Network for oriented object detection. As a one-stage model, DAFNe performs predictions on a dense grid over the input image, being architecturally simpler in design, as well as easier to optimize than its two-stage counterparts. Furthermore, as an anchor-free model, DAFNe reduces the prediction complexity by refraining from employing bounding box anchors. Moreover, we introduce an orientation-aware generalization of the center-ness function for arbitrarily oriented bounding boxes to down-weight low-quality predictions and a center-to-corner bounding box prediction strategy that improves object localization performance. Our experiments show that, to the best of our knowledge, DAFNe outperforms all previous one-stage anchor-free models on DOTA 1.0. DAFNe improves the prediction accuracy over the previous best results by 4.65% mAP, setting the new state-of-the-art results by achieving 76.95% mAP. We provide the code at https://github.com/steven-lang/DAFNe.
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information

Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-aware NMT have focused on developing new model architectures for incorporating additional contexts and have shown some promising results. However, most of existing works rely on cross-entropy loss, resulting in limited use of contextual information. In this paper, we propose CorefCL, a novel data augmentation and contrastive learning scheme based on coreference between the source and contextual sentences. By corrupting automatically detected coreference mentions in the contextual sentence, CorefCL can train the model to be sensitive to coreference inconsistency. We experimented with our method on common context-aware NMT models and two document-level translation tasks. In the experiments, our method consistently improved BLEU of compared models on English-German and English-Korean tasks. We also show that our method significantly improves coreference resolution in the English-German contrastive test suite.
Adversarially Trained Object Detector for Unsupervised Domain Adaptation

Unsupervised domain adaptation, which involves transferring knowledge from a label-rich source domain to an unlabeled target domain, can be used to reduce annotation costs in the field of object detection substantially. This study demonstrates that adversarial training in the source domain can be employed as a new approach for unsupervised domain adaptation. Specifically, we establish that adversarially trained detectors achieve improved detection performance in target domains that are significantly shifted from source domains. This phenomenon is attributed to the fact that adversarially trained detectors can be used to extract robust features that that align with human perception and worth transferring across domains while discarding domain-specific non-robust features. In addition, we propose a method that combines adversarial training and feature alignment to ensure the improved alignment of robust features with the target domain. We conduct experiments on four benchmark datasets and confirm the effectiveness of our proposed approach on large domain shifts from real to artistic images. Compared to the baseline models, the adversarially trained detectors improve the mean average precision by up to 7.7%, and further by up to 11.8% when feature alignments are incorporated. Although our method degrades the performance for small domain shifts, quantifying the domain shift based on the Fréchet distance allows us to determine whether adversarial training should be conducted.
Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids’ Representations

Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models. In this work, we extend the probing studies to two other models in the family, namely ELECTRA and XLNet, showing that variations in the pre-training objectives or architectural choices can result in different behaviors in encoding linguistic information in the representations. Most notably, we observe that ELECTRA tends to encode linguistic knowledge in the deeper layers, whereas XLNet instead concentrates that in the earlier layers. Also, the former model undergoes a slight change during fine-tuning, whereas the latter experiences significant adjustments. Moreover, we show that drawing conclusions based on the weight mixing evaluation strategy—which is widely used in the context of layer-wise probing—can be misleading given the norm disparity of the representations across different layers. Instead, we adopt an alternative information-theoretic probing with minimum description length, which has recently been proven to provide more reliable and informative results.
A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem

We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the scenario in which one agent is human, while the other is an autonomous agent. We run experiments with human subjects in a simple grid domain, which simulates the single track road problem. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. However, we show that an agent that tries to maximize a linear combination of the human's utility and its own utility, achieves a high score, and significantly outperforms other baselines, including an agent that tries to maximize only its own utility.
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints

The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature. The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent. Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs. We evaluate a standalone random search and a combined Bayesian optimization with random search in a closed-loop auditory event-related potential protocol. We aimed at finding the individually best stimulation speed -- also known as stimulus onset asynchrony (SOA) -- that maximizes the classification performance of a regularized linear discriminant analysis. To make the Bayesian optimization feasible under noise and the time pressure posed by an online BCI experiment, we first used offline simulations to initialize and constrain the internal optimization model. Then we evaluated our approach online with 13 healthy subjects. We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values. Our data suggests, however, that subjects were influenced to very different degrees by the SOA parameter. This makes the automatic parameter selection infeasible for subjects where the influence is limited. Our work proposes an approach to exploit the benefits of individualized experimental protocols and evaluated it in an auditory BCI. When applied to other experimental parameters our approach could enhance the usability of BCI for different target groups -- specifically if an individual disease progress may prevent the use of standard parameters.
Neutrino Cooling Bounds on the Internal Magnetic Fields of White Dwarfs

The evolution of white dwarfs (WDs) depends crucially on thermal processes. The plasma in their core can produce neutrinos which escape from the star, thus contributing to the energy loss. While in absence of a magnetic field the main cooling mechanism is plasmon decay at high temperature and photon surface emission at low temperature, a large magnetic field in the core hiding beneath the surface even of ordinary WDs, and undetectable to spectropolarimetric measurements, can potentially leave an imprint in the cooling. In this paper, we revisit the contribution to WD cooling stemming from neutrino pair synchrotron radiation and the effects of the magnetic field on plasmon decay. Our key finding is that even if observations limit the magnetic field strength at the stellar surface, strong magnetic fields in the interior of WDs—with or without a surface magnetic field—can be high enough to modify the cooling rate, which is sensitive to the magnetic field value due to neutrino pair synchrotron emission.
ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM

Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems already exist, they are either not accurate or they struggle in real-time settings. In this paper, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking, possibly aided by a pre-tracking module, and floor detection, to ground-optimize the estimated trajectory. Efficient multi-steps loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI (urban driving) and Chilean (underground mine) datasets.
CarNet: A Lightweight and Efficient Encoder-Decoder Architecture for High-quality Road Crack Detection

—Pixel-wise crack detection is a challenging task because of poor continuity and low contrast in cracks. The existing frameworks usually employ complex models leading to good accuracy and yet low inference efﬁciency. In this paper, we present a lightweight encoder-decoder architecture, CarNet, for efﬁcient and high-quality crack detection. To this end, we ﬁrst propose that the ideal encoder should present an olive-type distribution about the number of convolutional layers at different stages. Speciﬁcally, as the network stages deepen in the encoder, the number of convolutional layers shows a downward trend after the model input is compressed in the initial network stage. Meanwhile, in the decoder, we introduce a lightweight up-sampling feature pyramid module to learn rich hierarchical features for crack detection. In particular, we compress the feature maps of the last three network stages to the same channels and then employ up-sampling with different multiples to resize them to the same resolutions for information fusion. Finally, extensive experiments on four public databases, i.e., Sun520, Rain365, BJN260, and Crack360, demonstrate that our CarNet gains a good trade-off between inference efﬁciency and test accuracy over the existing state-of-the-art methods.
Augmenting Decision Making via Interactive What-If Analysis

The fundamental goal of business data analysis is to improve business decisions using data. Business users often make decisions to achieve key performance indicators (KPIs) such as increasing customer retention or sales, or decreasing costs. To discover the relationship between data attributes hypothesized to be drivers and those corresponding to KPIs of interest, business users currently need to perform lengthy exploratory analyses. This involves considering multitudes of combinations and scenarios and performing slicing, dicing, and transformations on the data accordingly, e.g., analyzing customer retention across quarters of the year or suggesting optimal media channels across strata of customers. However, the increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses, even for simple datasets. Therefore mentally performing such analyses is hard. Existing commercial tools either provide partial solutions or fail to cater to business users altogether. Here we argue for four functionalities to enable business users to interactively learn and reason about the relationships between sets of data attributes thereby facilitating data-driven decision making. We implement these functionalities in SystemD, an interactive visual data analysis system enabling business users to experiment with the data by asking what-if questions. We evaluate the system through three business use cases: marketing mix modeling, customer retention analysis, and deal closing analysis, and report on feedback from multiple business users. Users find the SystemD functionalities highly useful for quick testing and validation of their hypotheses around their KPIs of interest, addressing their unmet analysis needs. The feedback also suggests that the UX design can be enhanced to further improve the understandability of these functionalities.
Detecting Handwritten Mathematical Terms with Sensor Based Data

In this work we propose a solution to the UbiComp 2021 Challenge by Stabilo in which handwritten mathematical terms are supposed to be automatically classified based on time series sensor data captured on the DigiPen. The input data set contains data of different writers, with label strings constructed from a total of 15 different possible characters. The label should first be split into separate characters to classify them one by one. This issue is solved by applying a data-dependant and rule-based information extraction algorithm to the labeled data. Using the resulting data, two classifiers are constructed. The first is a binary classifier that is able to predict, for unknown data, if a sample is part of a writing activity, and consists of a Deep Neural Network feature extractor in concatenation with a Random Forest that is trained to classify the extracted features at an F1 score of>90%. The second classifier is a Deep Neural Network that combines convolution layers with recurrent layers to predict windows with a single label, out of the 15 possible classes, at an F1 score of>60%. A simulation of the challenge evaluation procedure reports a Levensthein Distance of 8 and shows that the chosen approach still lacks in overall accuracy and real-time applicability.