Reviewerly/exHarmony huggingface dataset:
A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media

Patients experience various symptoms when they haveeither acute or chronic diseases or undergo some treatments for diseases. Symptoms are often indicators of the severity of the disease and the need for hospitalization. Symptoms are often described in free text written as clinical notes in the Electronic Health Records (EHR) and are not integrated with other clinical factors for disease prediction and healthcare outcome management. In this research, we propose a novel deep language model to extract patient-reported symptoms from clinical text. The deep language model integrates syntactic and semantic analysis for symptom extraction and identifies the actual symptoms reported by patients and conditional or negation symptoms. The deep language model can extract both complex and straightforward symptom expressions. We used a real-world clinical notes dataset to evaluate our model and demonstrated that our model achieves superior performance compared to three other state-of-the-art symptom extraction models. We extensively analyzed our model to illustrate its effectiveness by examining each component's contribution to the model. Finally, we applied our model on a COVID-19 tweets data set to extract COVID-19 symptoms. The results show that our model can identify all the symptoms suggested by the Center for Disease Control (CDC) ahead of their timeline and many rare symptoms.
An Adaptive Sample Assignment Strategy Based on Feature Enhancement for Ship Detection in SAR Images

Recently, ship detection in synthetic aperture radar (SAR) images has received extensive attention. Most of the current ship detectors preset dense anchor boxes to achieve spatial alignment with ground-truth (GT) objects. Then, the detector defines the positive and negative samples based on the intersection-over-unit (IoU) between the anchors and GT objects. However, this label assignment strategy confuses the learning process of the model to a certain extent and results in suboptimal classification and regression results. In this paper, an adaptive sample assignment (ASA) strategy is proposed to select high-quality positive samples according to the spatial alignment and the knowledge learned from the regression and classification branches. Using our model, the selection of positive and negative samples is more explicit, which achieves better detection performance. A regression guided loss is proposed to further lead the detector to select well-classified and well-regressed anchors as high-quality positive samples by introducing the regression performance as a soft label in the calculation of the classification loss. In order to alleviate false alarms, a feature aggregation enhancement pyramid network (FAEPN) is proposed to enhance multi-scale feature representations and suppress the interference of background noise. Extensive experiments using the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) demonstrate the superiority of our proposed approach.
A semantic and syntactic enhanced neural model for financial sentiment analysis

This paper studies the methodology of inferring bullish or bearish sentiments in the financial domain. The task aims to predict a real value to represent the sentiment intensity concerning a target (company or stock symbol) in a text. Previous researches have proved the validity of using deep neural networks to automatically learn semantic and syntactic information for sentiment prediction. Despite the promising performance, these approaches implicitly obtain the target-sentiment representation by a sentence-level vector, lacking explicitly modeling the semantic relatedness between a target and its context. In this paper, we tackle the task by a novel semantic and syntactic enhanced neural model (SSENM), which incorporates dependency graph and context words to guide a target representation. In particular, we devise a self-attentive mechanism to capture semantic contextual information and an edge-enhanced graph convolutional network (E-GCN) to aggregate node-to-node features. In addition, the existing FSA is limited in size, which is prone to the overfitting problem for modern neural models. We further develop a Manifold Mixup strategy to generate pseudo data in training. We perform extensive experiments on two public benchmarks, SemEval2017task5 and FiQA challenges. Results show that our model outperforms the state-of-the-art model by 2% wcs scores on SemEval2017task5 and 3% R2 scores on FiQA, respectively. Finally, we present detailed analysis to indicate the effectiveness of each proposed component.
HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton

With increasing applications of 3D hand pose estimation in various human-computer interaction applications, convolution neural networks (CNNs) based estimation models have been actively explored. However, the existing models require complex architectures or redundant computational resources to trade with the acceptable accuracy. To tackle this limitation, this paper proposes HandFoldingNet, an accurate and efficient hand pose estimator that regresses the hand joint locations from the normalized 3D hand point cloud input. The proposed model utilizes a folding-based decoder that folds a given 2D hand skeleton into the corresponding joint coordinates. For higher estimation accuracy, folding is guided by multi-scale features, which include both global and joint-wise local features. Experimental results show that the proposed model outperforms the existing methods on three hand pose benchmark datasets with the lowest model parameter requirement. Code is available at https://github.com/cwc1260/HandFold.
Deformable Template Network (DTN) for Object Detection

Objects often have different appearances because of viewpoint changes or part deformation. How to reasonably model these variations is still a big challenge for object detection. In this paper, we propose a novel Deformable Template Network (DTN), which exploits the pictorial structure to model possible variations of an object. DTN represents an object by virtue of a generated template in a deformable way. It has two key modules: the template generating module and the part matching module. The template generating module produces a template for a given object which defines the anchor positions of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k{\times }k$</tex-math></inline-formula> parts. Based on such a template, the part matching module aims to perform part alignment around the anchor positions. In terms of each part, the matching process makes a trade-off between maximizing the detection score and minimizing the deformation cost relative to the anchor position. Moreover, DTN is a fully convolutional network which means it is competitive in terms of detection efficiency. We evaluate DTN on both the PASCAL VOC and MSCOCO datasets, achieving the state-of-the-art results, an accuracy of 82.7% for PASCAL VOC and of 44.9% for MSCOCO.
Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdos-Rényi Graphs

Graph alignment in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph alignment in correlated Erdös-Rényi graphs. However, very little is known about the existence of efficient algorithms to achieve graph alignment without seeds. In this work we identify a region in which a straightforward O(n 11/5 log n)-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in aligning correlated Erdos-Rényi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance alignment (i.e., sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a alignment algorithm for bipartite graphs. Finally, we show that the implementation of a variant of this algorithm allows for the efficient alignment of large graphs under limited noise.
From Loop Fusion to Kernel Fusion: A Domain-Specific Approach to Locality Optimization

Optimizing data-intensive applications such as image processing for GPU targets with complex memory hierarchies requires to explore the tradeoffs among locality, parallelism, and computation. Loop fusion as one of the classical optimization techniques has been proven effective to improve locality at the function level. Algorithms in image processing are increasing their complexities and generally consist of many kernels in a pipeline. The inter-kernel communications are intensive and exhibit another opportunity for locality improvement at the system level. The scope of this paper is an optimization technique called kernel fusion for data locality improvement. We present a formal description of the problem by defining an objective function for locality optimization. By transforming the fusion problem to a graph partitioning problem, we propose a solution based on the minimum cut technique to search fusible kernels recursively. In addition, we develop an analytic model to quantitatively estimate potential locality improvement by incorporating domain-specific knowledge and architecture details. The proposed technique is implemented in an image processing DSL and source-to-source compiler called Hipacc, and evaluated over six image processing applications on three Nvidia GPUs. A geometric mean speedup of up to 2.52 can be observed in our experiments.
Radical Isogenies

This paper introduces a new approach to computing isogenies called "radical isogenies" and a corresponding method to compute chains of N-isogenies that is very efficient for small N. The method is fully deterministic and completely avoids generating N-torsion points. It is based on explicit formulae for the coordinates of an N-torsion point $$P'$$ on the codomain of a cyclic N-isogeny $$\varphi : E \rightarrow E'$$ , such that composing $$\varphi $$ with $$E' \rightarrow E' / \langle P' \rangle $$ yields a cyclic $$N^2$$ -isogeny. These formulae are simple algebraic expressions in the coefficients of E, the coordinates of a generator P of $$\ker \varphi $$ , and an Nth root $$ \root N \of {\rho } \, $$ , where the radicand $$\rho $$ itself is given by an easily computable algebraic expression in the coefficients of E and the coordinates of P. The formulae can be iterated and are particularly useful when computing chains of N-isogenies over a finite field $$\mathbb {F}_q$$ with $$\gcd (q-1, N) = 1$$ , where taking an Nth root is a simple exponentiation. Compared to the state-of-the-art, our method results in an order of magnitude speed-up for $$N \le 13$$ ; for larger N, the advantage disappears due to the increasing complexity of the formulae. When applied to CSIDH, we obtain a speed-up of about $$19 \%$$ over the implementation by Bernstein, De Feo, Leroux and Smith for the CSURF-512 parameters.
Combined Dissipative and Hamiltonian Confinement of Cat Qubits

Quantum error correction with biased-noise qubits can drastically reduce the hardware overhead for universal and fault-tolerant quantum computation. Cat qubits are a promising realization of biased-noise qubits as they feature an exponential error bias inherited from their non-local encoding in the phase space of a quantum harmonic oscillator. To confine the state of an oscillator to the cat qubit manifold, two main approaches have been considered so far: a Kerr-based Hamiltonian confinement with high gate performances, and a dissipative confinement with robust protection against a broad range of noise mechanisms. We introduce a new combined dissipative and Hamiltonian confinement scheme based on two-photon dissipation together with a Two-Photon Exchange (TPE) Hamiltonian. The TPE Hamiltonian is similar to Kerr nonlinearity, but unlike the Kerr it only induces a bounded distinction between even- and odd-photon eigenstates, a highly beneficial feature for protecting the cat qubits with dissipative mechanisms. Using this combined confinement scheme, we demonstrate fast and bias-preserving gates with drastically improved performance compared to dissipative or Hamiltonian schemes. In addition, this combined scheme can be implemented experimentally with only minor modifications of existing dissipative cat qubit experiments.
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models

While pre-trained language model (PLM) finetuning has achieved strong performance in many NLP tasks, the fine-tuning stage can be still demanding in labeled data.Recent works have resorted to active fine-tuning to improve the label efficiency of PLM fine-tuning, but none of them investigate the potential of unlabeled data.We propose ACTUNE, a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning.ACTUNE switches between data annotation and model self-training based on uncertainty: it selects high-uncertainty unlabeled samples for active annotation and lowuncertainty ones for model self-training.Under this framework, we design (1) a regionaware sampling strategy that reduces redundancy when actively querying for annotations and (2) a momentum-based memory bank that dynamically aggregates the model's pseudo labels to suppress label noise in self-training.Experiments on 6 text classification datasets show that ACTUNE outperforms the strongest active learning and self-training baselines and improves the label efficiency of PLM finetuning by 56.2% on average.Our implementation is available at https://github.com/yueyu1030/actune.
Adversarial Perturbation Attacks on ML-based CAD

There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations --- small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this paper, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving and design-constraint satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy.
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation

Given a portrait image of a person and an environment map of the target lighting, portrait relighting aims to re-illuminate the person in the image as if the person appeared in an environment with the target lighting. To achieve high-quality results, recent methods rely on deep learning. An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage. However, acquiring such data requires an expensive special capture rig and time-consuming efforts, limiting access to only a few resourceful laboratories. To address the limitation, we propose a new approach that can perform on par with the state-of-the-art (SOTA) relighting methods without requiring a light stage. Our approach is based on the realization that a successful relighting of a portrait image depends on two conditions. First, the method needs to mimic the behaviors of physically-based relighting. Second, the output has to be photorealistic. To meet the first condition, we propose to train the relighting network with training data generated by a virtual light stage that performs physically-based rendering on various 3D synthetic humans under different environment maps. To meet the second condition, we develop a novel synthetic-to-real approach to bring photorealism to the relighting network output. In addition to achieving SOTA results, our approach offers several advantages over the prior methods, including controllable glares on glasses and more temporally-consistent results for relighting videos.
Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

<h2>Abstract</h2><h3>Background and Objectives</h3> We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. <h3>Methods</h3> We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes. <h3>Results</h3> We included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (<i>n</i> = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (<i>n</i> = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (<i>n</i> = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (<i>n</i> = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (<i>n</i> = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (<i>n</i> = 494/522, 94.6% [95% CI 92.4–96.3]). <h3>Conclusion</h3> Our review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models. <h3>Systematic review registration</h3> PROSPERO, CRD42019161764.
Targeted validation: validating clinical prediction models in their intended population and setting

Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We call estimating how well a model performs within the intended population/setting "targeted validation". Use of this term sharpens the focus on the intended use of a model, which may increase the applicability of developed models, avoid misleading conclusions, and reduce research waste. It also exposes that external validation may not be required when the intended population for the model matches the population used to develop the model; here, a robust internal validation may be sufficient, especially if the development dataset was large.
CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning

Standard semi-supervised learning (SSL) using classbalanced datasets has shown great progress to leverage unlabeled data effectively. However, the more realistic setting of class-imbalanced data - called imbalanced SSL - is largely underexplored and standard SSL tends to under-perform. In this paper, we propose a novel co-learning framework (CoSSL), which decouples representation and classifier learning while coupling them closely. To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE) for classifier learning. Furthermore, the current evaluation protocol for imbalanced SSL focuses only on balanced test sets, which has limited practicality in real-world scenarios. Therefore, we further conduct a comprehensive evaluation under various shifted test distributions. In experiments, we show that our approach outperforms other methods over a large range of shifted distributions, achieving state-of-the-art performance on benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our code will be made publicly available.
Approaching optimal entangling collective measurements on quantum computing platforms

Entanglement is a fundamental feature of quantum mechanics and holds great promise for enhancing metrology and communications. Much of the focus of quantum metrology so far has been on generating highly entangled quantum states that offer better sensitivity, per resource, than what can be achieved classically. However, to reach the ultimate limits in multi-parameter quantum metrology and quantum information processing tasks, collective measurements, which generate entanglement between multiple copies of the quantum state, are necessary. Here, we experimentally demonstrate theoretically optimal single- and two-copy collective measurements for simultaneously estimating two non-commuting qubit rotations. This allows us to implement quantum-enhanced sensing, for which the metrological gain persists for high levels of decoherence, and to draw fundamental insights about the interpretation of the uncertainty principle. We implement our optimal measurements on superconducting, trapped-ion and photonic systems, providing an indication of how future quantum-enhanced sensing networks may look.
De-SAG: On the De-Anonymization of Structure-Attribute Graph Data

In this paper, we study the impacts of non-Personal Identifiable Information (non-PII) on the privacy of graph data with attribute information (e.g., social networks data with users' profiles (attributes)), namely Structure-Attribute Graph (SAG) data, both theoretically and empirically. Our main contributions are two-fold: (i) we conduct the first attribute-based anonymity analysis for SAG data under both preliminary and general models. By careful quantification, we obtain the explicit correlation between the graph anonymity and the attribute information. We also validate our analysis through numerical and real world data-based evaluations and the results indicate that the non-PII can also lead to significant anonymity loss; and (ii) according to our theoretical analysis, we propose a new de-anonymization framework for SAG data, namely De-SAG, which takes into account both the graph structure and the attribute information to the best of our knowledge. By extensive experiments, we demonstrate that De-SAG can significantly improve the performance of state-of-the-art graph de-anonymization attacks. Our attribute-based anonymity analysis and de-anonymization framework are expected to provide data owners and researchers a more complete understanding on the privacy vulnerability of graph data, and thus shed light on future graph anonymization and de-anonymization research.