Reviewerly/exHarmony huggingface dataset:
Simultaneous Hand Gesture Classification and Finger Angle Estimation via a Novel Dual-Output Deep Learning Model

Hand gesture classification and finger angle estimation are both critical for intuitive human–computer interaction. However, most approaches study them in isolation. We thus propose a dual-output deep learning model to enable simultaneous hand gesture classification and finger angle estimation. Data augmentation and deep learning were used to detect spatial-temporal features via a wristband with ten modified barometric sensors. Ten subjects performed experimental testing by flexing/extending each finger at the metacarpophalangeal joint while the proposed model was used to classify each hand gesture and estimate continuous finger angles simultaneously. A data glove was worn to record ground-truth finger angles. Overall hand gesture classification accuracy was 97.5% and finger angle estimation R 2 was 0.922, both of which were significantly higher than shallow existing learning approaches used in isolation. The proposed method could be used in applications related to the human–computer interaction and in control environments with both discrete and continuous variables.
Wigner function of a quantum system with polynomial potential

The Moyal equation for the Wigner function was obtained under the assumption that the potential is an analytic function. The polynomial form of the potential is a natural approximation of the analytical potential with any necessary accuracy. The simplest quantum system with a second-order polynomial potential is a quantum harmonic oscillator. In this paper, for a quantum system with a polynomial potential of arbitrary order, explicit expressions are obtained for the matrix elements of the kernel operator in the basis of the eigenfunctions of the harmonic oscillator. Using the explicit representation for the kernel operator matrix elements, we construct the distributions of the Wigner function in the phase space for quantum systems with polynomial potentials. The connection of the modified Vlasov equation with the Moyal equation for the Wigner function is shown. Examples of effective numerical algorithms for finding Wigner functions with high accuracy are given.
Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis

Computer aided detection (CADe) for breast lesions can provide an important reference for radiologists in breast cancer screening. Architectural distortion (AD) is a type of breast lesion that is difficult to detect. A majority of CADe methods focus on detecting the radial pattern, which is a main characteristic of typical ADs. However, a few atypical ADs do not exhibit such a pattern. To improve the performance of CADe for typical and atypical ADs, we propose a deep-learning-based model that used mammary gland distribution as prior information to detect ADs in digital breast tomosynthesis (DBT). First, information about gland distribution, including the Gabor magnitude, the Gabor orientation field, and a convergence map, were produced using a bank of Gabor filters and convergence measures. Then, this prior information and an original slice were input into a Faster R-CNN detection network to obtain the 2-D candidates for each slice. Finally, a 3-D aggregation scheme was employed to fuse these 2-D candidates as 3-D candidates for each DBT volume. Retrospectively, 64 typical AD volumes, 74 atypical AD volumes, and 127 normal volumes were collected. Six-fold cross-validation and mean true positive fraction (MTPF) were used to evaluate the model. Compared to an existing convergence-based model, our proposed model achieved an MTPF of 0.53 ± 0.04, 0.61 ± 0.05, and 0.45 ± 0.04 for all DBT volumes, typical + normal volumes, and atypical + normal volumes, respectively. These results were significantly better than those of 0.36 ± 0.03, 0.46 ± 0.04, and 0.28 ± 0.04 for a convergence-based model (p ≪ 0.01). These results indicate that employing the prior information of gland distribution and a deep learning method can improve the performance of CADe for AD.
Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems

Multiagent systems (MASs) have received extensive attention in a variety of domains, such as robotics and distributed control.This paper focuses on how independent learners (ILs, structures used in decentralized reinforcement learning) decide on their individual behaviors to achieve coherent joint behavior.To date, Reinforcement learning(RL) approaches for ILs have not guaranteed convergence to the optimal joint policy in scenarios in which communication is difficult.Especially in a decentralized algorithm, the proportion of credit for a single agent's action in a multiagent system is not distinguished, which can lead to miscoordination of joint actions.Therefore, it is highly significant to study the mechanisms of coordination between agents in MASs.Most previous coordination mechanisms have been carried out by modeling the communication mechanism and other agent policies.These methods are applicable only to a particular system, so such algorithms do not offer generalizability, especially when there are dozens or more agents.Therefore, this paper mainly focuses on the MAS contains more than a dozen agents.By combining the method of parallel computation, the experimental environment is closer to the application scene.By studying the paradigm of centralized training and decentralized execution(CTDE), a multi-agent reinforcement learning algorithm for implicit coordination based on TD error is proposed.The new algorithm can dynamically adjust the learning rate by deeply analyzing the dissonance problem in the matrix game and combining it with a multiagent environment.By adjusting the dynamic learning rate between agents, coordination of the agents' strategies can be achieved.Experimental results show that the proposed algorithm can effectively improve the coordination ability of a MAS.Moreover, the variance of the training results is more stable than that of the hysteretic Q learning(HQL) algorithm.Hence, the problem of miscoordination in a MAS can be avoided to some extent without additional communication.Our work provides a new way to solve the miscoordination problem for reinforcement learning algorithms in the scale of dozens or more number of agents.As a new IL structure algorithm, our results should be extended and further studied.
Shareability of quantum steering and its relation with entanglement

Steerability is a characteristic of quantum correlations lying between entanglement and Bell nonlocality. Understanding how these steering correlations can be shared between different parties has profound applications in ensuring the security of quantum communication protocols. Here we show that at most two bipartite reduced states of a three-qubit state can violate the three-setting CJWR linear steering inequality, contrary to the two-setting linear steering inequality. This result explains that quantum steering correlations have limited shareability properties apart from the conventional 'nonshareable' monogamy constraint. In contrast to the two-measurement-setting scenario, the three-setting scenario turns out to be more useful for developing a deeper understanding of the shareability of tripartite steering correlations. Apart from the distribution of steering correlations, several relations between reduced bipartite steering, different measures of bipartite entanglement of reduced states, and genuine tripartite entanglement are presented here. The results enable the detection of different kinds of tripartite entanglement.
Shape Descriptor-Based Similar Feature Extraction for Finite Element Meshing

Held et al. [CAD&A 2016] introduced generalized weighted Voronoi diagrams as a tool to construct variable-radius offsets.We revisit this structure and provide two algorithms to compute it.We also introduce variable-radius skeletons of polygons as a closely related structure which inherits most properties of generalized weighted Voronoi diagrams except that its skeletal regions are always connected.Experimental results obtained by our implementation of variable-radius skeletons are discussed.In addition to variable-radius offsets we demonstrate how this structure can be used to generate intricate roofs for polygonal footprints of buildings in an automated way.
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation

Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on implicit maximum likelihood estimation and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.
Collaboratively Modeling and Embedding of Latent Topics for Short Texts

Deriving a successful document representation is the critical challenge in many downstream tasks in NLP, especially when documents are very short. It is challenging to handle the sparsity and the noise problems confronting short texts. Some approaches employ latent topic models, based on global word co-occurrence, to obtain topic distribution as the representation. Others leverage word embeddings, which consider local conditional dependencies, to map a document as a summation vector of them. Unlike the existing works which explore the strategy of utilizing one to help the other, i.e., topic models for word embeddings or vice versa, we propose CME-DMM, a collaboratively modeling and embedding framework for capturing coherent latent topics from short texts. CME-DMM incorporates topic and word embeddings through the attention mechanism and implants them into the latent topic models, which significantly improve the quality of latent topics. Extensive experiments demonstrate that CME-DMM could perceive more coherent topics than other popular methods, resulting in a better performance in downstream NLP tasks such as classification. Besides the interpretable latent topics, the corresponding topic embeddings can describe the meanings of latent topics in the semantic space. The attention vectors, as a by-product of the learning process, can identify the keywords in noisy short texts.
Fused Convolutional Neural Network for White Blood Cell Detection

Fused convolutional neural network for white blood cell detection helps us to detect white blood cells including their subtypes by using Convolutional neural networks and deep learning algorithms.Deep Learning has already shown power in many application fields, and is accepted by more and more people as a better approach than the traditional machine learning models. In particular, the implementation of deep learning algorithms, especially Convolutional Neural Networks (CNN), brings huge benefits to the medical field, where a huge number of images are to be processed and analysed. This paper aims to develop a deep learning model to address the white blood cell classification problem, which is one of the most challenging problems in blood diagnosis. A CNN-based framework is built to automatically classify the white blood cell images into subtypes of the cells. Experiments are conducted on a dataset of 13k images of white blood cells with their subtypes, and the results show that our proposed model provide better results in terms of evaluation parameters.I.
SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning

Semantic segmentation methods have achieved outstanding performance thanks to deep learning. Nevertheless, when such algorithms are deployed to new contexts not seen during training, it is necessary to collect and label scene-specific data in order to adapt them to the new domain using fine-tuning. This process is required whenever an already installed camera is moved or a new camera is introduced in a camera network due to the different scene layouts induced by the different viewpoints. To limit the amount of additional training data to be collected, it would be ideal to train a semantic segmentation method using labeled data already available and only unlabeled data coming from the new camera. We formalize this problem as a domain adaptation task and introduce a novel dataset of urban scenes with the related semantic labels. As a first approach to address this challenging task, we propose SceneAdapt, a method for scene adaptation of semantic segmentation algorithms based on adversarial learning. Experiments and comparisons with state-of-the-art approaches to domain adaptation highlight that promising performance can be achieved using adversarial learning both when the two scenes have different but points of view, and when they comprise images of completely different scenes.
Arbitrary-Shaped Text Detection With Adaptive Text Region Representation

Text detection/localization, as an important task in computer vision, has witnessed substantial advancements in methodology and performance with convolutional neural networks. However, the vast majority of popular methods use rectangles or quadrangles to describe text regions. These representations have inherent drawbacks, especially relating to dense adjacent text and loose regional text boundaries, which usually cause difficulty detecting arbitrarily shaped text. In this paper, we propose a novel text region representation method, with a robust pipeline, which can precisely detect dense adjacent text instances with arbitrary shapes. We consider a text instance to be composed of an adaptive central text region mask and a corresponding expanding ratio between the central text region and the full text region. More specifically, our pipeline generates adaptive central text regions and corresponding expanding ratios with a proposed training strategy, followed by a new proposed post-processing algorithm which expands central text regions to the complete text instance with the corresponding expanding ratios. We demonstrated that our new text region representation is effective, and that the pipeline can precisely detect closely adjacent text instances of arbitrary shapes. Experimental results on common datasets demonstrate superior performance of our work.
Roles and Utilization of Attention Heads in Transformer-based Neural Language Models

Sentence encoders based on the transformer architecture have shown promising results on various natural language tasks.The main impetus lies in the pre-trained neural language models that capture long-range dependencies among words, owing to multi-head attention that is unique in the architecture.However, little is known for how linguistic properties are processed, represented, and utilized for downstream tasks among hundreds of attention heads inside the pre-trained transformerbased model.For the initial goal of examining the roles of attention heads in handling a set of linguistic features, we conducted a set of experiments with ten probing tasks and three downstream tasks on four pre-trained transformer families (GPT, GPT2, BERT, and ELECTRA).Meaningful insights are shown through the lens of heat map visualization and utilized to propose a relatively simple sentence representation method that takes advantage of most influential attention heads, resulting in additional performance improvements on the downstream tasks.
Heterogeneous Task Co-location in Containerized Cloud Computing Environments

Although cloud computing became a mainstream industrial computing paradigm, low resource utilization remains a common problem that most warehouse-scale datacenters suffer from. This leads to a significant waste of hardware resources, infrastructure investment, and energy consumption. As the diversity in application workloads grows into an essential characteristic in modern datacenters, task co-location of different workloads to the same compute cluster has gained immense popularity as a heuristic solution for resource utilization optimization. Although the existing co-location methodologies manage to improve resource efficiency to a certain degree, application QoS is usually sacrificed as a trade-off when dealing with resource interference between different applications. This paper proposes a containerized task co-location (CTCL) scheduler to improve resource utilization and minimize task eviction rate. Our CTCL scheduler (1) applies an elastic task co-location strategy to improve resource utilization; and (2) supports a dynamic task rescheduling mechanism to prevent severe QoS degradation from frequent task evictions. We evaluate our approach in terms of resource efficiency and rescheduling cost through the ContainerCloudSim simulator. Our experiments with the Alibaba 2018 workload traces demonstrate that CTCL could improve overall resource efficiency and reduce rescheduling rate by 38% and 99% respectively.
Controllability of Control Argumentation Frameworks

Control argumentation frameworks (CAFs) allow for modeling uncertainties inherent in various argumentative settings. We establish a complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics. We also develop Boolean satisfiability based counterexample-guided abstraction refinement algorithms and direct encodings of controllability as quantified Boolean formulas, and empirically evaluate their scalability on a range of NP-hard variants of controllability.
Sentiment analysis of comments in social media

Social media platforms are witnessing a significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Research related to sentiment analysis is acquiring substantial interest as it is a promising filed that can improve user experience and provide countless personalized services. Twitter is one of the most popular social media platforms, it has users from different regions with a variety of cultures and languages. It can thus provide valuable information for a diverse and large amount of data to be used to improve decision making. In this paper, the sentiment orientation of the textual features and emoji-based components is studied targeting “Tweets” and comments posted in Arabic on Twitter, during the 2018 world cup event. This study also measures the significance of analyzing texts including or excluding emojis. The data is obtained from thousands of extracted tweets, to find the results of sentiment analysis for texts and emojis separately. Results show that emojis support the sentiment orientation of the texts and that texts or emojis cannot separately provide reliable information as they complement each other to give the intended meaning.
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages

Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly.However, the scarcity of sense-annotated data makes it difficult to induce a reliable and highcoverage distribution of the meanings in a language vocabulary.To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences.Our experiments show that Clu-BERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches.When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models.Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks.We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.
Convergence analysis on inertial proportional delayed neural networks

Abstract This article mainly explores a class of inertial proportional delayed neural networks. Abstaining reduced order strategy, a novel approach involving differential inequality technique and Lyapunov function fashion is presented to open out that all solutions of the considered system with their derivatives are convergent to zero vector, which refines some previously known research. Moreover, an example and its numerical simulations are given to display the exactness of the proposed approach.
Deep Image Compression with Residual Learning

An end-to-end image compression framework based on deep residual learning is proposed. Three levels of residual learning are adopted to improve the compression quality: (1) the ResNet structure; (2) the deep channel residual learning for quantization; and (3) the global residual learning in full resolution. Residual distribution is commonly a single Gaussian distribution, and relatively easy to be learned by the neural network. Furthermore, an attention model is combined in the proposed framework to compress regions of an image with different bits adaptively. Across the experimental results on Kodak PhotoCD test set, the proposed approach outperforms JPEG and JPEG2000 by PSNR and MS-SSIM at low BPP (bit per pixel). Furthermore, it can produce much better visual quality. Compared to the state-of-the-art deep learning-based codecs, the proposed approach also achieves competitive performance.
Cascade or Recency

Recently session search evaluation has been paid more attention as a realistic search scenario usually involves multiple queries and interactions between users and systems. Evolved from model-based evaluation metrics for a single query, existing session-based metrics also follow a generic framework based on the cascade hypothesis. The cascade hypothesis assumes that lower-ranked search results and later-issued queries receive less attention from users and should therefore be assigned smaller weights when calculating evaluation metrics. This hypothesis gains much success in modeling search users' behavior and designing evaluation metrics, by explaining why users' attention decays on search engine result pages. However, recent studies have found that the recency effect also plays an important role in determining user satisfaction in search sessions. Especially, whether a user feels satisfied in the later-issued queries heavily influences his/her search satisfaction in the whole session. To take both the cascade hypothesis and the recency effect into the design of session search evaluation metrics, we propose Recency-aware Session-based Metrics (RSMs) to simultaneously characterize users' examination process with a browsing model and cognitive process with a utility accumulation model. With both self-constructed and public available user search behavior datasets, we show the effectiveness of proposed RSMs by comparing them with existing session-based metrics in the light of correlation with user satisfaction. We also find that the influence of the cascade and the recency effects varies dramatically among tasks with different difficulties and complexities, which suggests that we should use different model parameters for different types of search tasks. Our findings highlight the importance of investigating and utilizing cognitive effects besides examination hypotheses in search evaluation.