Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of Weight Sharing in Graph Networks for 3D Human Pose Estimation

Graph convolutional networks (GCNs) have been applied to 3D human pose estimation (HPE) from 2D body joint detections and have shown encouraging performance. One limitation of the vanilla graph convolution is that it models the relationships between neighboring nodes via a shared weight matrix. This is suboptimal for articulated body modeling as the relations between different body joints are different. The objective of this paper is to have a comprehensive and systematic study of weight sharing in GCNs for 3D HPE. We first show there are two different ways to interpret a GCN depending on whether feature transformation occurs before or after feature aggregation. These two interpretations lead to five different weight sharing methods, and three more variants can be derived by decoupling the self-connections with other edges. We conduct extensive ablation study on these weight sharing methods under controlled settings and obtain new conclusions that will benefit the community.
CNN cascades for segmenting sparse objects in gigapixel whole slide images

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, large amounts of digital image data are being generated. Accordingly, there is a strong demand for the development of computer based image analysis systems. Here, we address application scenarios in histopathology consisting of sparse, small objects-of-interest occurring in the large gigapixel images. To tackle the thereby arising challenges, we propose two different CNN cascade approaches which are subsequently applied to segment the glomeruli in whole slide images of the kidney and compared with conventional fully-convolutional networks. To facilitate unbiased evaluation, eight-fold cross-validation is performed and finally means and standard deviations are reported. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained (precision: 0.89, recall: 0.92). Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to previous approaches. We can state that especially one of the proposed cascade networks proved to be a highly powerful tool providing the best segmentation accuracies and also keeping the computing time at the lowest level. This work facilitates accurate automated segmentation of renal whole slide images which consequently allows fully-automated big data analyses for the assessment of medical treatments. Furthermore, this approach can also easily be adapted to other similar biomedical application scenarios.
TypeWriter: neural type prediction with search-based validation

Maintaining large code bases written in dynamically typed languages, such as JavaScript or Python, can be challenging due to the absence of type annotations: simple data compatibility errors proliferate, IDE support is limited, and APIs are hard to comprehend. Recent work attempts to address those issues through either static type inference or probabilistic type prediction. Unfortunately, static type inference for dynamic languages is inherently limited, while probabilistic approaches suffer from imprecision. This paper presents TypeWriter, the first combination of probabilistic type prediction with search-based refinement of predicted types. TypeWriter’s predictor learns to infer the return and argument types for functions from partially annotated code bases by combining the natural language properties of code with programming language-level information. To validate predicted types, TypeWriter invokes a gradual type checker with different combinations of the predicted types, while navigating the space of possible type combinations in a feedback-directed manner. We implement the TypeWriter approach for Python and evaluate it on two code corpora: a multi-million line code base at Facebook and a collection of 1,137 popular open-source projects. We show that TypeWriter’s type predictor achieves an F1 score of 0.64 (0.79) in the top-1 (top-5) predictions for return types, and 0.57 (0.80) for argument types, which clearly outperforms prior type prediction models. By combining predictions with search-based validation, TypeWriter can fully annotate between 14% to 44% of the files in a randomly selected corpus, while ensuring type correctness. A comparison with a static type inference tool shows that TypeWriter adds many more non-trivial types. TypeWriter currently suggests types to developers at Facebook and several thousands of types have already been accepted with minimal changes.
Operational relevance of resource theories of quantum measurements

For any resource theory it is essential to identify tasks for which resource objects offer advantage over free objects. We show that this identification can always be accomplished for resource theories of quantum measurements in which free objects form a convex subset of measurements on a given Hilbert space. To this aim we prove that every resource measurement offers advantage for some quantum state discrimination task. Moreover, we give an operational interpretation of robustness, which quantifies the minimal amount of noise that must be added to a measurement to make it free. Specifically, we show that this geometric quantity is related to the maximal relative advantage that a resource measurement offers in a class of minimal-error state discrimination problems. Finally, we apply our results to two classes of free measurements: incoherent measurements (measurements that are diagonal in the fixed basis) and separable measurements (measurements whose effects are separable operators). For both of these scenarios we find, in the asymptotic setting in which the dimension or the number of particles increase to infinity, the maximal relative advantage that resource measurements offer for state discrimination tasks.
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation.While this results in models capable of generating coherent texts, it also prompts models to internalize social biases present in the training corpus.This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text.Given a conditioning context (e.g., a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g., country names, occupations, genders) in the conditioning context using a form of counterfactual evaluation.We quantify sentiment bias by adopting individual and group fairness metrics from the fair machine learning literature, and demonstrate that largescale models trained on two different corpora (news articles, and Wikipedia) exhibit considerable levels of bias.We then propose embedding and sentiment prediction-derived regularization on the language model's latent representations.The regularizations improve fairness metrics while retaining comparable levels of perplexity and semantic similarity.
Blockchain-Based Decentralized Authentication Modeling Scheme in Edge and IoT Environment

Authentication is the first entrance to kinds of information systems; however, traditional centered single-side authentication is weak and fragile, which has security risk of single-side failure or breakdown caused by outside attacks or internal cheating. In the edge and Internet-of-Things (IoT) environment, blockchain can apply edge devices to better serve the IoT and provide decentralized high security service solutions. In this article, we proposed a blockchain-based decentralized authentication modeling scheme (named BlockAuth) in edge and IoT environment to provide a more secure, reliable, and strong fault tolerance novel solution, in which each edge device is regarded as a node to form a blockchain network. We designed secure registration and authentication strategy, blockchain-based decentralized authentication protocol, and developed the blockchain consensus, smart contract, and implemented a whole blockchain-based authentication platform for the feasibility, security, and performance evaluation. The analysis and evaluation show that the proposed BlockAuth scheme provides a more secure, reliable, and strong fault tolerance decentralized novel authentication with high-level security driven configuration management. The proposed BlockAuth scheme is suitable for password-based, certificate-based, biotechnology-based, and token-based authentication for high-level security requirement system in edge and IoT environment.
A controlled-NOT gate for frequency-bin qubits

The realization of strong photon-photon interactions has presented an enduring challenge across photonics, particularly in quantum computing, where two-photon gates form essential components for scalable quantum information processing (QIP). While linear-optic schemes have enabled probabilistic entangling gates in spatio-polarization encoding, solutions for many other useful degrees of freedom remain missing. In particular, no two-photon gate for the important platform of frequency encoding has been experimentally demonstrated, due in large part to the additional challenges imparted by the mismatched wavelengths of the interacting photons. In this article, we design and implement the first entangling gate for frequency-bin qubits, a coincidence-basis controlled-NOT (CNOT), using line-by-line pulse shaping and electro-optic modulation. We extract a quantum gate fidelity of $0.91 \pm 0.01$ via a novel parameter inference approach based on Bayesian machine learning, which enables accurate gate reconstruction from measurements in the two-photon computational basis alone. Our CNOT imparts a single-photon frequency shift controlled by the frequency of another photon---an important capability in itself---and should enable new directions in fiber-compatible QIP.
Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

Targeted sentiment classification predicts the sentiment polarity on given target mentions in input texts. Dominant methods employ neural networks for encoding the input sentence and extracting relations between target mentions and their contexts. Recently, graph neural network has been investigated for integrating dependency syntax for the task, achieving the state-of-the-art results. However, existing methods do not consider dependency label information, which can be intuitively useful. To solve the problem, we investigate a novel relational graph attention network that integrates typed syntactic dependency information. Results on standard benchmarks show that our method can effectively leverage label information for improving targeted sentiment classification performances. Our final model significantly outperforms state-of-the-art syntax-based approaches.
Talking-Head Generation with Rhythmic Head Motion

When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing works either generate still talking-face videos or rely on landmark/video frames as sparse/dense mapping guidance to generate head movements, which leads to unrealistic or uncontrollable video synthesis. To overcome the limitations, we propose a 3D-aware generative network along with a hybrid embedding module and a non-linear composition module. Through modeling the head motion and facial expressions (In our setting, facial expression means facial movement (e.g., blinks, and lip & chin movements).) explicitly, manipulating 3D animation carefully, and embedding reference images dynamically, our approach achieves controllable, photo-realistic, and temporally coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion .
Lightweight Salient Object Detection via Hierarchical Visual Perception Learning

Recently, salient object detection (SOD) has witnessed vast progress with the rapid development of convolutional neural networks (CNNs). However, the improvement of SOD accuracy comes with the increase in network depth and width, resulting in large network size and heavy computational overhead. This prevents state-of-the-art SOD methods from being deployed into practical platforms, especially mobile devices. To promote the deployment of real-world SOD applications, we aim at developing a lightweight SOD model in this article. Our observation comes from that the primate visual system processes visual signals hierarchically with different receptive fields and eccentricities in different visual cortex areas. Inspired by this, we propose a hierarchical visual perception (HVP) module to imitate the primate visual cortex for hierarchical perception learning. With the HVP module incorporated, we design a lightweight SOD network, namely, HVPNet. Extensive experiments on popular benchmarks demonstrate that HVPNet achieves highly competitive accuracy compared with state-of-the-art SOD methods while running at 4.3 frames/s CPU speed and 333.2 frames/s GPU speed with only 1.23M parameters.
Attribute Restoration Framework for Anomaly Detection

With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods have recently shown great promise for anomaly detection, the information equivalence among input and supervision for reconstruction tasks can not effectively force the network to learn semantic feature embeddings. We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors. Through forcing the network to restore the original image, the semantic feature embeddings related to the erased attributes are learned by the network. During testing phases, because anomalous data are restored with the attribute learned from the normal data, the restoration error is expected to be large. Extensive experiments have demonstrated that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, especially on ImageNet, increasing the AUROC of the top-performing baseline by 10.1&#x0025;. We also evaluate our method on a real-world anomaly detection dataset MVTec AD.
Multi-Level Fusion and Attention-Guided CNN for Image Dehazing

In this paper, we tackle the problem of single image dehazing with a convolutional neural network. Within this network, we develop a multi-level fusion module to utilize both low-level and high-level features. The low-level features help to recover finer details, and the high-level features discover abstract semantics. They are complementary in the restoring of clear images. Moreover, a Residual Mixed-convolution Attention Module (RMAM) with an attention block is proposed to guide the network to focus on important features in the learning process. In this RMAM, group convolution, depth-wise convolution, and point-wise convolution are mixed, and thus it is much faster than its counterparts. With these two modules, we thus have an end-to-end network without explicitly estimating the atmospheric light intensity and the transmission map in the classical atmosphere scattering model. Both qualitative and quantitative experimental studies are carried out on public datasets including RESIDE, DCPDN-TestA, and the real-world dataset. The extensive results demonstrate both the effectiveness and efficiency of the proposed solution to single image dehazing.
Speaker identification through artificial intelligence techniques: A comprehensive review and research challenges

Speech is a powerful medium of communication that always convey rich and useful information, such as gender, accent, and other unique characteristics of a speaker. These unique characteristics enable researchers to recognize human voice using artificial intelligence techniques that are important in the areas of forensic voice verification, security and surveillance, electronic voice eavesdropping, mobile banking and mobile shopping. Recent advancements in deep learning and other hardware techniques have gained attention of researchers working in the field of automatic speaker identification (SI). However, to the best of our knowledge, there is no in-depth survey is available that critically appraises and summarizes the existing techniques with their strengths and weaknesses for SI. Hence, this study identified and discussed various areas of SI, presented a comprehensive survey of existing studies, and also presented the future research challenges that require significant research efforts in the field of SI systems.
From Pulses to Circuits and Back Again: A Quantum Optimal Control Perspective on Variational Quantum Algorithms

The last decade has witnessed remarkable progress in the development of quantum technologies. Although fault-tolerant devices likely remain years away, the noisy intermediate-scale quantum devices of today may be leveraged for other purposes. Leading candidates are variational quantum algorithms (VQAs), which have been developed for applications including chemistry, optimization, and machine learning, but whose implementations on quantum devices have yet to demonstrate improvements over classical capabilities. In this Perspective, we propose a variety of ways that the performance of VQAs could be informed by quantum optimal control theory. To set the stage, we identify VQAs and quantum optimal control as formulations of variational optimization at the circuit level and pulse level, respectively, where these represent just two levels in a broader hierarchy of abstractions that we consider. In this unified picture, we suggest several ways that the different levels of abstraction may be connected, in order to facilitate the application of quantum optimal control theory to VQA challenges associated with ansatz selection, optimization landscapes, noise, and robustness. A major theme throughout is the need for sufficient control resources in VQA implementations; we discuss different ways this need can manifest, outline a variety of open questions, and conclude with a look to the future.
STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks

Social Network Analysis (SNA) has been a popular field of research since the early 1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering and criminal investigation for decades. However, the graph nature of social networks makes it highly restricted to intelligence analysis tasks, such as role prediction (node classification), social relation inference (link prediction), and criminal group discovery (community detection), etc. In the past few years, many studies have focused on Graph Neural Network (GNN), which utilizes deep learning methods to solve graph-related problems. However, we have rarely seen GNNs tackle time-evolving social network problems, especially in the criminology field. The existing studies have commonly over-looked the temporal-evolution characteristics of social networks. In this paper, we propose a graph neural network framework, namely Spatial-Temporal Graph Social Network (STGSN), which models social networks from both spatial and temporal perspectives. Using a novel approach, we leverage the temporal attention mechanism to capture social networks’ temporal features. We design a method analyzing temporal attention distribution to improve the interpretation ability of our method. In the end, we conduct extensive experiments on six public datasets to prove our methods’ effectiveness.
Dynamical Properties of Ion-Acoustic Waves in Space Plasma and Its Application to Image Encryption

The nonlinear ion-acoustic waves (IAWs) in a space plasma are capable of exhibiting chaotic dynamics which can be applied to cryptography.Dynamical properties of IAWs are examined using the direct method in plasmas composed of positive and negative ions and nonextensive distributed electrons.Applying the wave transformation, the governing equations are deduced into a dynamical system (DS).Supernonlinear and nonlinear periodic IAWs are presented through phase plane analysis.The analytical periodic wave solution for IAW is obtained.Under the influence of an external periodic force, the DS is transformed to a perturbed system.The perturbed DS describes multistability property of IAWs with change of initial conditions.The multistability behavior features coexisting trajectories such as, quasiperiodic, multiperiodic and chaotic trajectories of the perturbed DS.The chaotic feature in the perturbed DS is supported by Lyapunov exponents.This interesting behavior in the windows of chaotic dynamics is exploited to design efficient encryption algorithm.First SHA-512 is used to compute the hash digest of the plain image which is then used to update the initial seed of the chaotic IAWs system.Note that SHA-512 uses one-way function to map input data to the output, consequently it is quite impossible to break the proposed encryption technique.Second DNA coding is used to confuse and diffuse the DNA version of the plain image.The diffused image follows DNA decoding process leading to the cipher image.The security performance is evaluated using some well-known metrics and results indicate that the proposed cryptosystem can resist most of existing cryptanalysis techniques.In addition complexity analysis shows the possibility of practical implementation of the proposed algorithm.
Few-Shot Classification of Aerial Scene Images via Meta-Learning

Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.