bluuebunny/crossref_metadata_2025 huggingface dataset:
Hierarchical Video Generation From Orthogonal Information: Optical Flow and Texture
Authors: Ohnishi Katsunori , Yamamoto Shohei , Ushiku Yoshitaka , Harada Tatsuya
Published: June 2022

Learning to represent and generate videos from unlabeled data is a very challenging problem. To generate realistic videos, it is important not only to ensure that the appearance of each frame is real, but also to ensure the plausibility of a video motion and consistency of a video appearance in the time direction. The process of video generation should be divided according to these intrinsic difficulties. In this study, we focus on the motion and appearance information as two important orthogonal components of a video, and propose Flow-and-Texture-Generative Adversarial Networks (FTGAN) consisting of FlowGAN and TextureGAN. In order to avoid a huge annotation cost, we have to explore a way to learn from unlabeled data. Thus, we employ optical flow as motion information to generate videos. FlowGAN generates optical flow, which contains only the edge and motion of the videos to be begerated. On the other hand, TextureGAN specializes in giving a texture to optical flow generated by FlowGAN. This hierarchical approach brings more realistic videos with plausible motion and appearance consistency. Our experiments show that our model generates more plausible motion videos and also achieves significantly improved performance for unsupervised action classification in comparison to previous GAN works. In addition, because our model generates videos from two independent information, our model can generate new combinations of motion and attribute that are not seen in training data, such as a video in which a person is doing sit-up in a baseball ground.
Collaborative Filtering With User-Item Co-Autoregressive Models
Authors: Du Chao , Li Chongxuan , Zheng Yin , Zhu Jun , Zhang Bo
Published: June 2022

Deep neural networks have shown promise in collaborative filtering (CF). However, existing neural approaches are either user-based or item-based, which cannot leverage all the underlying information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploits the structural correlation in the domains of both users and items. The co-autoregression allows extra desired properties to be incorporated for different tasks. Furthermore, we develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art performance in both rating prediction and top-N recommendation tasks, which demonstrates the effectiveness of CF-UIcA.
Link Prediction With Personalized Social Influence
Authors: Huo Zepeng , Huang Xiao , Hu Xia
Published: June 2022

Link prediction in social networks is to infer the new links likely to be formed next or to reconstruct the links that are currently missing. Other than the pure topological network structures, social networks are often associated with rich information of social activities of users, such as tweeting, retweeting, and replying. Social theories such as social influence indicate that social activities could have potential impacts on the neighbors, and links in social media could be the results of the social influence among users. It motivates us to learn and model social influence among users to tackle the link prediction problem. However, this is a non-trivial task since it is challenging to model heterogeneous social activities. Traditional methods often define universal metrics of social influence for all users, but even for the same activity of a user, the influence towards different neighbors might not be the same. It motivates a personalized learning schema. In information theory, if a time-series signal influences another, then the uncertainty in the latter one will be reduced, given the distribution of the former one. Thus, we are motivated to learn social influence based on the timestamps of social activities. Given the timestamps of each user, we use entropy to measure the reduction of uncertainty of his/her neighbors. The learned social influence is then incorporated into a graph based link prediction model to perform joint learning. Through comprehensive experiments, we demonstrate that the proposed framework can perform better than the state-of-the-art methods on different real-world networks.
The Shape of Art History in the Eyes of the Machine
Authors: Elgammal Ahmed , Liu Bingchen , Kim Diana , Elhoseiny Mohamed , Mazzone Marian
Published: June 2022

How does the machine classify styles in art? And how does it relate to art historians' methods for analyzing style? Several studies showed the ability of the machine to learn and predict styles, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 67K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are a few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich Wölfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations.
Dress Fashionably: Learn Fashion Collocation With Deep Mixed-Category Metric Learning
Authors: Chen Long , He Yuhang
Published: June 2022

In this paper, we seek to enable machine to answer questions like, given a clutch bag, what kind of skirt, heel and even accessory best fashionably collocate with it ? This problem, dubbed fashion collocation, has almost been neglected by researchers due to the large uncertainty lies in fashion collocation and professional expertise required to address it. In this paper, we narrow down the well-collocated samples to be fashion images shared on fashion websites, with which we propose an end-to-end trainable deep mixed-category metric learning method to project well-collocated clothing items to lie close but items violating well-collocation far apart in the deep embedding space. Specifically, we simultaneously model the intra-category exclusiveness and cross-category inclusiveness of fashion collocation by feeding a set of well-collocated clothing items and corresponding bad-collocated clothing items to the deep neural network, further a hard-aware online exemplar mining strategy is designed to force the whole neural network to be trainable and learn discriminative features at the early and later training stages respectively. To motivate more research in fashion collocation, we collect a dataset of 0.2 million fashionably well-collocated images consisting of either on-body or off-body clothing items or accessories. Extensive experimental results show the feasibility and superiority of our method.
Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
Authors: Kao Hao-Cheng , Tang Kai-Fu , Chang Edward
Published: June 2022

Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.
Improving Sequence-to-Sequence Constituency Parsing
Authors: Liu Lemao , Zhu Muhua , Shi Shuming
Published: June 2022

Sequence-to-sequence constituency parsing casts the tree structured prediction problem as a general sequential problem by top-down tree linearization,and thus it is very easy to train in parallel with distributed facilities. Despite its success, it relies on a probabilistic attention mechanism for a general purpose, which can not guarantee the selected context to be informative in the specific parsing scenario. Previous work introduced a deterministic attention to select the informative context for sequence-to-sequence parsing, but it is based on the bottom-up linearization even if it was observed that top-down linearization is better than bottom-up linearization for standard sequence-to-sequence constituency parsing. In this paper, we thereby extend the deterministic attention to directly conduct on the top-down tree linearization. Intensive experiments show that our parser delivers substantial improvements over the bottom-up linearization in accuracy, and it achieves 92.3 Fscore on the Penn English Treebank section 23 and 85.4 Fscore on the Penn Chinese Treebank test dataset, without reranking or semi-supervised training.
Event Detection via Gated Multilingual Attention Mechanism
Authors: Liu Jian , Chen Yubo , Liu Kang , Zhao Jun
Published: June 2022

Identifying event instance in text plays a critical role in building NLP applications such as Information Extraction (IE) system. However, most existing methods for this task focus only on monolingual clues of a specific language and ignore the massive information provided by other languages. Data scarcity and monolingual ambiguity hinder the performance of these monolingual approaches. In this paper, we propose a novel multilingual approach---dubbed as Gated Multilingual Attention (GMLATT) framework---to address the two issues simultaneously. In specific, to alleviate data scarcity problem, we exploit the consistent information in multilingual data via context attention mechanism. Which takes advantage of the consistent evidence in multilingual data other than learning only from monolingual data. To deal with monolingual ambiguity problem, we propose gated cross-lingual attention to exploit the complement information conveyed by multilingual data, which is helpful for the disambiguation. The cross-lingual attention gate serves as a sentinel modelling the confidence of the clues provided by other languages and controls the information integration of various languages. We have conducted extensive experiments on the ACE 2005 benchmark. Experimental results show that our approach significantly outperforms state-of-the-art methods.
An Unsupervised Model With Attention Autoencoders for Question Retrieval
Authors: Zhang Minghua , Wu Yunfang
Published: June 2022

Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions. Our RAMN integrates together the deep semantic representations, the shallow lexical mismatching information and the initial rank produced by an external search engine. For the first time, we propose attention autoencoders to generate semantic representations of questions. In addition, we employ lexical mismatching to capture surface matching between two questions, which is derived from the importance of each word in a question. We conduct experiments on the open CQA datasets of SemEval-2016 and SemEval-2017. The experimental results show that our unsupervised model obtains comparable performance with the state-of-the-art supervised methods in SemEval-2016 Task 3, and outperforms the best system in SemEval-2017 Task 3 by a wide margin.
Effective Broad-Coverage Deep Parsing
Authors: Allen James , Bahkshandeh Omid , De Beaumont William , Galescu Lucian , Teng Choh Man
Published: June 2022

Current semantic parsers either compute shallow representations over a wide range of input, or deeper representations in very limited domains. We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar. This paper discusses how this core system can be customized for a particularly challenging domain, namely reading research papers in biology. We evaluate these customizations with some ablation experiments
Memory Fusion Network for Multi-view Sequential Learning
Authors: Zadeh Amir , Liang Paul Pu , Mazumder Navonil , Poria Soujanya , Cambria Erik , Morency Louis-Philippe
Published: June 2022

Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for all three multi-view datasets.
SciTaiL: A Textual Entailment Dataset from Science Question Answering
Authors: Khot Tushar , Sabharwal Ashish , Clark Peter
Published: June 2022

We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SciTail is the first entailment set that is created solely from natural sentences that already exist independently ``in the wild'' rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates, and premises from relevant web sentences retrieved from a large corpus. These sentences are often linguistically challenging. This, combined with the high lexical similarity of premise and hypothesis for both entailed and non-entailed pairs, makes this new entailment task particularly difficult. The resulting challenge is evidenced by state-of-the-art textual entailment systems achieving mediocre performance on SciTail, especially in comparison to a simple majority class baseline. As a step forward, we demonstrate that one can improve accuracy on SciTail by 5% using a new neural model that exploits linguistic structure.
Learning Conditional Generative Models for Temporal Point Processes
Authors: Xiao Shuai , Xu Hongteng , Yan Junchi , Farajtabar Mehrdad , Yang Xiaokang , Song Le , Zha Hongyuan
Published: June 2022

Estimating the future event sequence conditioned on current observations is a long-standing and challenging task in temporal analysis. On one hand for many real-world problems the underlying dynamics can be very complex and often unknown. This renders the traditional parametric point process models often fail to fit the data for their limited capacity. On the other hand, long-term prediction suffers from the problem of bias exposure where the error accumulates and propagates to future prediction. Our new model builds upon the sequence to sequence (seq2seq) prediction network. Compared with parametric point process models, its modeling capacity is higher and has better flexibility for fitting real-world data. The main novelty of the paper is to mitigate the second challenge by introducing the likelihood-free loss based on Wasserstein distance between point processes, besides negative maximum likelihood loss used in the traditional seq2seq model. Wasserstein distance, unlike KL divergence i.e. MLE loss, is sensitive to the underlying geometry between samples and can robustly enforce close geometry structure between them. This technique is proven able to improve the vanilla seq2seq model by a notable margin on various tasks.
Sensor-Based Activity Recognition via Learning From Distributions
Authors: Qian Hangwei , Pan Sinno , Miao Chunyan
Published: June 2022

Sensor-based activity recognition aims to predict users' activities from multi-dimensional streams of various sensor readings received from ubiquitous sensors. To use machine learning techniques for sensor-based activity recognition, previous approaches focused on composing a feature vector to represent sensor-reading streams received within a period of various lengths. With the constructed feature vectors, e.g., using predefined orders of moments in statistics, and their corresponding labels of activities, standard classification algorithms can be applied to train a predictive model, which will be used to make predictions online. However, we argue that in this way some important information, e.g., statistical information captured by higher-order moments, may be discarded when constructing features. Therefore, in this paper, we propose a new method, denoted by SMMAR, based on learning from distributions for sensor-based activity recognition. Specifically, we consider sensor readings received within a period as a sample, which can be represented by a feature vector of infinite dimensions in a Reproducing Kernel Hilbert Space (RKHS) using kernel embedding techniques. We then train a classifier in the RKHS. To scale-up the proposed method, we further offer an accelerated version by utilizing an explicit feature map instead of using a kernel function. We conduct experiments on four benchmark datasets to verify the effectiveness and scalability of our proposed method.
Linear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning
Authors: Piacentini Chiara , Castro Margarita , Cire Andre , Beck J. Christopher
Published: June 2022

Linear programming has been successfully used to compute admissible heuristics for cost-optimal classical planning. Although one of the strengths of linear programming is the ability to express and reason about numeric variables and constraints, their use in numeric planning is limited. In this work, we extend linear programming-based heuristics for classical planning to support numeric state variables. In particular, we propose a model for the interval relaxation, coupled with landmarks and state equation constraints. We consider both linear programming models and their harder-to-solve, yet more informative, integer programming versions. Our experimental analysis shows that considering an NP-Hard heuristic often pays off and that A* search using our integer programming heuristics establishes a new state of the art in cost-optimal numeric planning.
Load Scheduling of Simple Temporal Networks Under Dynamic Resource Pricing
Authors: Kumar T. K. Satish , Wang Zhi , Kumar Anoop , Rogers Craig , Knoblock Craig
Published: June 2022

We study load scheduling of simple temporal networks (STNs) under dynamic pricing of resources. We are given a set of processes and a set of simple temporal constraints between their execution times, i.e., an STN. Each process uses a certain amount of resource for execution. The unit price of the resource is a function of time, f(t). The goal is to find a schedule of a given STN that trades off makespan minimization against cost minimization within a user-specified suboptimality bound. We provide a polynomial-time algorithm for solving the load scheduling problem when f(t) is piecewise constant. This has important applications in many real-world domains including the smart home and smart grid domains. We then study the dependency of the unit price of the resource on time as well as the total demand at that time. This leads to a further characterization of tractable, NP-hard, and conjectured tractable cases.
Fat- and Heavy-Tailed Behavior in Satisficing Planning
Authors: Cohen Eldan , Beck J. Christopher
Published: June 2022

In this work, we study the runtime distribution of satisficing planning in ensembles of random planning problems and in multiple runs of a randomized heuristic search on a single planning instance. Using common heuristic functions (such as FF) and six benchmark problem domains from the IPC, we find a heavy-tailed behavior, similar to that found in CSP and SAT. We investigate two notions of constrainedness, often used in the modeling of planning problems, and show that the heavy-tailed behavior tends to appear in relatively relaxed problems, where the required effort is, on average, low. Finally, we show that as with randomized restarts in CSP and SAT solving, recent search enhancements that incorporate randomness in the search process can help mitigate the effect of the heavy tail.
Imitation Upper Confidence Bound for Bandits on a Graph
Authors: Lupu Andrei , Precup Doina
Published: June 2022

We consider a graph of interconnected agents implementing a common policy and each playing a bandit problem with identical reward distributions. We restrict the information propagated in the graph such that agents can uniquely observe each other's actions. We propose an extension of the Upper Confidence Bound (UCB) algorithm to this setting and empirically demonstrate that our solution improves the performance over UCB according to multiple metrics and within various graph configurations.
Verifying Properties of Binarized Deep Neural Networks
Authors: Narodytska Nina , Kasiviswanathan Shiva , Ryzhyk Leonid , Sagiv Mooly , Walsh Toby
Published: June 2022

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the first exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classification tasks. To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.
Community-Based Trip Sharing for Urban Commuting
Authors: Hasan Mohd. Hafiz , Van Hentenryck Pascal , Budak Ceren , Chen Jiayu , Chaudhry Chhavi
Published: June 2022

This paper explores Community-Based Trip Sharing which uses the structure of communities and commuting patterns to optimize car or ride sharing for urban communities. It introduces the Commuting Trip Sharing Problem (CTSP) and proposes an optimization approach to maximize trip sharing. The optimization method, which exploits trip clustering, shareability graphs, and mixed-integer programming, is applied to a dataset of 9000 daily commuting trips from a mid-size city. Experimental results show that community-based trip sharing reduces daily car usage by up to 44%, thus producing significant environmental and traffic benefits and reducing parking pressure. The results also indicate that daily flexibility in pairing cars and passengers has significant impact on the benefits of the approach, revealing new insights on commuting patterns and trip sharing.
Clustering - What Both Theoreticians and Practitioners Are Doing Wrong
Authors: Ben-David Shai
Published: June 2022

Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowadays. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and practical implementations of such tasks, in particular of clustering, is very rudimentary. This note focuses on clustering. The first challenge I address is model selection---how should a user pick an appropriate clustering tool for a given clustering problem, and how should the parameters of such an algorithmic tool be tuned? In contrast with other common computational tasks, for clustering, different algorithms often yield drastically different outcomes. Therefore, the choice of a clustering algorithm may play a crucial role in the usefulness of an output clustering solution. However, currently there exists no methodical guidance for clustering tool selection for a given clustering task. I argue the severity of this problem and describe some recent proposals aiming to address this crucial lacuna.
Learning Coarse-to-Fine Structured Feature Embedding for Vehicle Re-Identification
Authors: Guo Haiyun , Zhao Chaoyang , Liu Zhiwei , Wang Jinqiao , Lu Hanqing
Published: June 2022

Vehicle re-identification (re-ID) is to identify the same vehicle across different cameras. It’s a significant but challenging topic, which has received little attention due to the complex intra-class and inter-class variation of vehicle images and the lack of large-scale vehicle re-ID dataset. Previous methods focus on pulling images from different vehicles apart but neglect the discrimination between vehicles from different vehicle models, which is actually quite important to obtain a correct ranking order for vehicle re-ID. In this paper, we learn a structured feature embedding for vehicle re-ID with a novel coarse-to-fine ranking loss to pull images of the same vehicle as close as possible and achieve discrimination between images from different vehicles as well as vehicles from different vehicle models. In the learnt feature space, both intra-class compactness and inter-class distinction are well guaranteed and the Euclidean distance between features directly reflects the semantic similarity of vehicle images. Furthermore, we build so far the largest vehicle re-ID dataset "Vehicle-1M," which involves nearly 1 million images captured in various surveillance scenarios. Experimental results on "Vehicle-1M" and "VehicleID" demonstrate the superiority of our proposed approach.
Deep Semantic Structural Constraints for Zero-Shot Learning
Authors: Li Yan , Jia Zhen , Zhang Junge , Huang Kaiqi , Tan Tieniu
Published: June 2022

Zero-shot learning aims to classify unseen image categories by learning a visual-semantic embedding space. In most cases, the traditional methods adopt a separated two-step pipeline that extracts image features are utilized to learn the embedding space. It leads to the lack of specific structural semantic information of image features for zero-shot learning task. In this paper, we propose an end-to-end trainable Deep Semantic Structural Constraints model to address this issue. The proposed model contains the Image Feature Structure constraint and the Semantic Embedding Structure constraint, which aim to learn structure-preserving image features and endue the learned embedding space with stronger generalization ability respectively. With the assistance of semantic structural information, the model gains more auxiliary clues for zero-shot learning. The state-of-the-art performance certifies the effectiveness of our proposed method.
Supervised Deep Hashing for Hierarchical Labeled Data
Authors: Wang Dan , Huang Heyan , Lu Chi , Feng Bo-Si , Wen Guihua , Nie Liqiang , Mao Xian-Ling
Published: June 2022

Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing supervised hashing methods do not consider the hierarchical relation of labels,which means that they ignored the rich semantic information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. To tackle the aforementioned problems, in this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Speciﬁcally, we deﬁne a novel similarity formula for hierarchical labeled data by weighting each level, and design a deep neural network to obtain a hash code for each data point. Extensive experiments on two real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.
Multispectral Transfer Network: Unsupervised Depth Estimation for All-Day Vision
Authors: Kim Namil , Choi Yukyung , Hwang Soonmin , Kweon In So
Published: June 2022

To understand the real-world, it is essential to perceive in all-day conditions including cases which are not suitable for RGB sensors, especially at night. Beyond these limitations, the innovation introduced here is a multispectral solution in the form of depth estimation from a thermal sensor without an additional depth sensor.Based on an analysis of multispectral properties and the relevance to depth predictions, we propose an efficient and novel multi-task framework called the Multispectral Transfer Network (MTN) to estimate a depth image from a single thermal image. By exploiting geometric priors and chromaticity clues, our model can generate a pixel-wise depth image in an unsupervised manner. Moreover, we propose a new type of multitask module called Interleaver as a means of incorporating the chromaticity and fine details of skip-connections into the depth estimation framework without sharing feature layers. Lastly, we explain a novel technical means of stably training and covering large disparities and extending thermal images to data-driven methods for all-day conditions. In experiments, we demonstrate the better performance and generalization of depth estimation through the proposed multispectral stereo dataset, including various driving conditions.
Emphasizing 3D Properties in Recurrent Multi-View Aggregation for 3D Shape Retrieval
Authors: Xu Cheng , Leng Biao , Zhang Cheng , Zhou Xiaochen
Published: June 2022

Multi-view based shape descriptors have achieved impressive performance for 3D shape retrieval. The core of view-based methods is to interpret 3D structures through 2D observations. However, most existing methods pay more attention to discriminative models and none of them necessarily incorporate the 3D properties of the objects. To resolve this problem, we propose an encoder-decoder recurrent feature aggregation network (ERFA-Net) to emphasize the 3D properties of 3D shapes in multi-view features aggregation. In our network, a view sequence of the shape is trained to encode a discriminative shape embedding and estimate unseen rendered views of any viewpoints. This generation task gives an effective supervision which makes the network exploit 3D properties of shapes through various 2D images. During feature aggregation, a discriminative feature representation across multiple views is effectively exploited based on LSTM network. The proposed 3D representation has following advantages against other state-of-the-art: 1) it performs robust discrimination under the existence of noise such as view missing and occlusion, because of the improvement brought by 3D properties. 2) it has strong generative capabilities, which is useful for various 3D shape tasks. We evaluate ERFA-Net on two popular 3D shape datasets, ModelNet and ShapeNetCore55, and ERFA-Net outperforms the state-of-the-art methods significantly. Extensive experiments show the effectiveness and robustness of the proposed 3D representation.
Semi-Supervised Bayesian Attribute Learning for Person Re-Identification
Authors: Liu Wenhe , Chang Xiaojun , Chen Ling , Yang Yi
Published: June 2022

Person re-identification (re-ID) tasks aim to identify the same person in multiple images captured from non-overlapping camera views. Most previous re-ID studies have attempted to solve this problem through either representation learning or metric learning, or by combining both techniques. Representation learning relies on the latent factors or attributes of the data. In most of these works, the dimensionality of the factors/attributes has to be manually determined for each new dataset. Thus, this approach is not robust. Metric learning optimizes a metric across the dataset to measure similarity according to distance. However, choosing the optimal method for computing these distances is data dependent, and learning the appropriate metric relies on a sufficient number of pair-wise labels. To overcome these limitations, we propose a novel algorithm for person re-ID, called semi-supervised Bayesian attribute learning. We introduce an Indian Buffet Process to identify the priors of the latent attributes. The dimensionality of attributes factors is then automatically determined by nonparametric Bayesian learning. Meanwhile, unlike traditional distance metric learning, we propose a re-identification probability distribution to describe how likely it is that a pair of images contains the same person. This technique relies solely on the latent attributes of both images. Moreover, pair-wise labels that are not known can be estimated from pair-wise labels that are known, making this a robust approach for semi-supervised learning. Extensive experiments demonstrate the superior performance of our algorithm over several state-of-the-art algorithms on small-scale datasets and comparable performance on large-scale re-ID datasets.
Heavenly Creatures? Visions of Animal Afterlife In Seventeenth–century England
Authors: Strickland Lloyd
Published: June 2022

This article offers an extensive study of the idea of an animal afterlife in seventeenth-century England. While some have argued that the idea of an animal afterlife became prevalent at the time due to increased awareness of animals' mental abilities, others have suggested it was due to greater sensitivity to animal suffering and the perceived need to square this suffering with divine justice. I show that both views are incorrect, and that seventeenth-century thinking about an animal afterlife was first and foremost grounded in, and shaped by, speculations about end times and the restored creation based on a literal reading of a key passage in Paul's letter to the Romans (8:19–22), which thus served as the sedes doctrinaethroughout the seventeenth century. Lastly, I show that those who entertained a future life for beasts also supposed that animals would have a functional role therein, considering animals as being present in the restored creation to serve some spiritual end rather than as being true beneficiaries in their own right.
"The Indications" (1857)
Authors: Turpin Zachary
Published: June 2022

Discusses a previously unknown non-parodic Whitman imitation that pre-dates Adah Isaac Menken's Infelicia (1868). Titled "The Indications," this poem appeared in the June 3, 1857, issue of Life Illustrated magazine (New York: Fowler and Wells).
Heteropatriarchy's Blame Game: Reading Genesis 37 with Izitabane during COVID 19
Authors: van der Walt Charlene , Davids Hanzline R.
Published: June 2022

The COVID 19 pandemic compounded the insecurity and vulnerability experienced by LGBTIQ+ people who remain confined to their family homes during the lockdown in South Africa. LGBTIQ+ people are often referred to as Izitabane, a term that gives derogatory expression to the othering, stigmatisation and exclusion experienced by LGBTIQ+ people in African contexts in general and African faith communities in particular. As the pandemic unfolded, faith leaders reached out to their flock via social media through online worship services and daily devotions. In some instances, these devotions sought "theological clarification" for the pandemic and in the process evoked violence towards the LGBTIQ+ community who were held responsible. In order to engage critically and creatively with these life-denying realities and to search for impulses of hope and life, an episode from the Joseph narrative found in Gen 37 has been appropriated as a reflective surface in the development process of Contextual Bible Study resources engaging the African faith and sexuality landscape. Building on insights gained from employing the tools of Queer Biblical Hermeneutics to read Gen 37, the final part of the essay describes the Contextual Bible Study developed jointly by the Ujamaa Centre at UKZN and Inclusive and Affirming Ministries and offers it as a resource for Izitabane to resist normalisation, correction and annihilation when the Biblical text is used in a life-denying manner.
Belief in Yhwh as Identity Marker in Pre-exilic Israel: An Identity-Oriented Reading of Deuteronomy 13
Authors: Campi Giorgio Paolo
Published: June 2022

This article outlines an identity-oriented reading of the so-called "apostasy series" (Deut 13) to explore the modes of articulation and construction of collective identity in pre-exilic Israel. Heuristically, the article integrates assumptions of the social constructionist approach and some points of Jan Assmann's model of "secondary religion". The reading of Deut 13 in this article highlights, on the one hand, how religious belief functions as a marker of collective identity in Deut 13, and on the other, how identity construction depends on inner social articulations within Israel rather than on subversive political or theological claims against the Assyrian power. Ultimately, Deut 13 frames the shaping of a self-articulation within Israel, which may be expressed as follows: belief in Yhwh as an identity marker allows the Israelite community to distance itself from one of its parts to define what Israel is and what it is not. The real tension felt in the passage is between a plural community and a collective that attempts to standardise plurality to define itself, that is, a tension between a real Israel and an ideal Israel, between the layers of historical reality and the normative abstractions that attempt to control them.
The Peti and the Power of Speech in Proverbs 1-9
Authors: Widder Wendy L.
Published: June 2022

Speech is a prominent theme throughout ancient Near Eastern wisdom literature. Accordingly, the proverbs in Proverbs 10-29 offer extensive instruction about the nature and power of speech. Speech also pervades Proverbs 1-9, a series of instructive lectures and interludes. However, speech is not primarily a topic of instruction; rather, it is a vehicle for instruction. Proverbs 1-9 puts speech on the lips of competing voices-the father, the gang, the seductress, Lady Wisdom, and Lady Folly-and admonishes, seduces, and encourages the son (the peti), who is presented with a choice: To whom will he listen? These chapters draw attention to what might be considered the most important trait of the peti: being a discerning listener. This article argues that, by prefacing the instruction of chapters 10-29 with chapters 1-9, the compiler of Proverbs sets discernment as the fundamental requirement for the instruction that follows. It surveys the topic of "speech" in ancient Egyptian wisdom literature; examines "speech" proverbs in Proverbs 10-29; and evaluates how "speech " in Proverbs 1-9 contributes to the portrayal of a teachable peti and one's approach to the rest of the book.
Reading gamefully: videogamification as multimodal pedagogy for high school setworks
Authors: de Jager Nic
Published: June 2022

This paper draws from multiple publications in the Literacy Studies, Game Studies and Multimodal fields to foreground the affordances of using modern video game aesthetics - particularly their user interfaces or screens - as learning scaffolds in the under-resourced English classroom context. Though this may be seen as a well-worn terrain for research today (nearly 30 years after the advent of Game Studies), it is argued that video games remain somewhat underrepresented in literacy education, with the Covid-19 pandemic and recurrent lockdowns even further cementing games technologies from learners' home domains as the new frontier in teaching and learning. The benefits of importing such technologies into the classroom is nothing new to the field. Yet, this study innovates by optimising the most accessible of graphological media (pencils, pens, paints and paper) during participants' transmodalisations of prescribed English literature - particularly Shakespeare's plays - into a range of video game screenshots, including character menus, maps, and heads-up-displays. The research site is a public high school in Johannesburg, South Africa, with five Grade 10-12 learners drawing the screenshots in response to an extracurricular, multimodal enrichment programme. The author contends that this programme (or similar pedagogies) may encourage future groups to delve further into the complexities of their school setworks, which may then be connected meaningfully to their own, increasingly digital life-worlds. Recognising game-making as an extraordinarily complex undertaking, the researcher then offers a fine-grained analysis of each participant's text-to-game re-genrefication. In this way, the powerful representational properties of the video game medium can come to light, reaffirming its importance as a semiotic resource and pedagogic tool.
The application of a molecular sieve for drying the insulation system of a
power transformer in terms of improving its perational reliability
Authors: Cybulski Mateusz , Przybyłek Piotr
Published: June 2022

The first research objective was the assessment of the selectivity of water and hydrogen
adsorption by the 3A molecular sieve (MS) in the terms of the safety of using the DGA
(Dissolved Gas Analysis) method as a diagnostic technique. The second objective was to
demonstrate the efficiency of drying the insulation system consisting of cellulose insulation
impregnated with various insulating liquids in terms of improving safety and extending the
service life of power transformers. In order to achieve the assumed objectives, measurement
systems were prepared and material tests were carried out, based mainly on chromatographic analyzes and measurements of water content using the Karl Fischer titration method. On
the basis of the obtained test results and the conducted physical analyzes, the selectivity of
MS sorption was shown and it was explained that the facilitator of the adsorption of water
molecules is their polar nature related to their large dipole moment. The high efficiency of
drying cellulose insulation impregnated with various insulating liquids using MS was also
demonstrated
conceptos científicos y sus implicaciones políticas en la gestión de las aguas transfronterizas México-Estados Unidos: ¿Acuífero transfronterizo o aguas subterráneas transfronterizas?
Authors: Hatch Kuri Gonzalo , Carrillo Rivera José Joel
Published: June 2022

El concepto "acuífero transfronterizo" previsto en la Resolución de Naciones Unidas 63/124 ha tenido un impacto significativo en la evaluación de los acuíferos transfronterizos. En el caso México-Estados Unidos, no ha sido posible determinar oficialmente el número total de acuíferos compartidos, mucho menos se ha podido evaluar el funcionamiento sistémico del agua subterránea, debido a una falta de claridad conceptual en el tema. Esta contribución efectúa un análisis basado en la revisión crítica de evidencia científica y legal para determinar la naturaleza de las discrepancias conceptuales entre las definiciones científicas de "Acuífero Transfronterizo" y "Agua Subterránea Transfronteriza", desde un enfoque interdisciplinario (geografía política e hidrogeología). Los resultados sugieren la necesidad de incorporar una visión sistémica en la gestión de las aguas subterráneas compartidas, así como la homologación de metodologías para garantizar una gestión integrada de las aguas transfronterizas entre ambos países.
Effects of two different inspiratory muscle training devices on respiratory muscle strength, cardiopulmonary functional capacity, and quality of life of COPD patients
Authors: Da Silva de Paula Carla Rafaela , Duarte Eltz Giovana , Dos Santos Rafaela Stefany , Silva Zuttin Roberta , Oliveira Kelly Cristina da Silva
Published: June 2022

Introduction: Inspiratory muscle weakness is one of the complications of chronic obstructive pulmonary disease (COPD). This condition has been minimized by inspiratory muscle training (IMT). Still, different IMT devices need to be compared to identify the best option. Objective: To evaluate the effects of two IMT devices on respiratory muscle strength, cardiorespiratory functional capacity, and life quality of COPD patients. Method: Before and after the IMT protocol, 17 patients were submitted to manovacuometry, six-minute walk test (6MWT), and questionnaire from the Saint George Hospital in Respiratory Disease (SGRQ) for inspiratory muscle strength, cardiorespiratory functional capacity, and life quality assessments, respectively. Afterwards, they were divided into two groups to perform IMT for four weeks, one by Threshold® IMT (IMTG) and the other by POWERbreathe® Classic (PG). Results: Maximal inspiratory pressure increased significantly from -62.00±16.03 to -75.55±22.84 cmH2O in IMTG, and from -56.00±22.01 to -71.25±27.67 cmH2O in PG. The distance covered in the 6MWT clinically increased with both IMTG and PG. The SGRQ answers showed improvements in all domains evaluated for both groups. However, the intergroup results showed no statistically significant differences. Conclusion: Both IMT devices increased inspiratory muscle strength, improved life quality, and optimized cardiorespiratory functionality in COPD patients. None was superior to the other for the analyzed parameters.
An Institution and a Symbol in Political Theology
Authors: Isaev I. A. , Rumyantseva V. G.
Published: June 2022

The paper is devoted to the historical and legal aspects of political theology: its civilizational path (in Modern and Contemporary Times), its impact on the legal understanding and implementation of ideas related to the concepts power, state, law. Political theology serves as the metaphysical foundation of political structures and institutions. The issues of political and religious areas, their mergers are investigated through the prism of dialectical development. Special attention is paid to the symbolic side of political theology. The category institutions is examined as a set of corporate, organic and instrumental entities that consolidate and fix the processes of legal genesis. The institution is associated with a number of political and legal conditions of a particular state and acquires its legal significance through the psychological accession of citizens by unanimous consent. The category symbol can be traced in the mythologized and religious perspective of power. The state power has always, since the most ancient times of the existence of mankind, required some kind of mysterious sacralization and worshiping. In general, the life of a social group organized and endowed with permanence, i.e. representing a stable institution, turns out to be included in the circle of symbolic procedures and rituals. Fundamental positions of Maurice Hauriou and Carl Schmitt had a great influence on the research. As for the views of outstanding jurists (set forth in the works “Fundamentals of Public Law” and “Political Theology”), it is important to emphasize their value for domestic science and practice. In the complex world realities of today, the theories that reveal the postulates of sociolaw and geolaw popular in Russia arouse unconditional interest among scholars and politicians.