Miracle-dz/UniFAR-FTUs huggingface dataset:
Metric-Affine Myrzakulov Gravity Theories

In this paper, we review the so-called Myrzakulov Gravity models (MG-N, with N = I, II, …, VIII) and derive their respective metric-affine generalizations (MAMG-N), discussing also their particular sub-cases. The field equations of the theories are obtained by regarding the metric tensor and the general affine connection as independent variables. We then focus on the case in which the function characterizing the aforementioned metric-affine models is linear and consider a Friedmann-Lemaître–Robertson–Walker background to study cosmological aspects and applications. Historical motivation for this research is thoroughly reviewed and specific physical motivations are provided for the aforementioned family of alternative theories of gravity.
You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights

Hate speech is regarded as one of the crucial issues plaguing the online social media. The current literature on hate speech detection leverages primarily the textual content to find hateful posts and subsequently identify hateful users. However, this methodology disregards the social connections between users. In this paper, we run a detailed exploration of the problem space and investigate an array of models ranging from purely textual to graph based to finally semi-supervised techniques using Graph Neural Networks (GNN) that utilize both textual and graph-based features. We run exhaustive experiments on two datasets -- Gab, which is loosely moderated and Twitter, which is strictly moderated. Overall the AGNN model achieves 0.791 macro F1-score on the Gab dataset and 0.780 macro F1-score on the Twitter dataset using only 5% of the labeled instances, considerably outperforming all the other models including the fully supervised ones. We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures and the AGNN model benefits by paying attention to these signatures. This property, as we observe, also allows the model to generalize well across platforms in a zero-shot setting. Lastly, we utilize the best performing GNN model to analyze the evolution of hateful users and their targets over time in Gab.
Learning Embeddings that Capture Spatial Semantics for Indoor Navigation

Incorporating domain-specific priors in search and navigation tasks has shown promising results in improving generalization and sample complexity over end-to-end trained policies. In this work, we study how object embeddings that capture spatial semantic priors can guide search and navigation tasks in a structured environment. We know that humans can search for an object like a book, or a plate in an unseen house, based on the spatial semantics of bigger objects detected. For example, a book is likely to be on a bookshelf or a table, whereas a plate is likely to be in a cupboard or dishwasher. We propose a method to incorporate such spatial semantic awareness in robots by leveraging pre-trained language models and multi-relational knowledge bases as object embeddings. We demonstrate using these object embeddings to search a query object in an unseen indoor environment. We measure the performance of these embeddings in an indoor simulator (AI2Thor). We further evaluate different pre-trained embedding onSuccess Rate(SR) and success weighted by Path Length(SPL).
Chiral discrimination by polarization singularities of single metal sphere

The practical applications of chiral discrimination are usually limited by the weak chiral response of enantiomers and the high complexity of detection methods. Here, we propose to use the C lines (i.e., lines of polarization singularities) emerged in light scattering by a metal sphere to detect the chirality of small chiral particles. Using full-wave numerical simulations and analytical multipole expansions, we determined the absorption dissymmetry of the chiral particles at different positions on the C lines and found that it can be much larger than that induced by circularly polarized plane wave excitation. We uncover that the large dissymmetry factor is attributed to the asymmetric absorption of electric and magnetic dipoles induced by the C lines. The results can generate novel methods of chiral discrimination and may find applications in optical manipulations, optical sensing, and chiral quantum optics.
Multiplex Graph Networks for Multimodal Brain Network Analysis

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tensor representation into the multiplex GCN model to extract the latent structures of a set of multimodal brain networks, which allows an intuitive 'grasping' of the common space for multimodal data. Multimodal representations are then generated with multiplex GCNs to capture specific graph structures. We conduct classification task on two challenging real-world datasets (HIV and Bipolar disorder), and the proposed MGNet demonstrates state-of-the-art performance compared to competitive benchmark methods. Apart from objective evaluations, this study may bear special significance upon network theory to the understanding of human connectome in different modalities. The code is available at https://github.com/ZhaomingKong/MGNets.
I2V-GAN: Unpaired Infrared-to-Visible Video Translation

Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios. Thus, infrared cameras are often leveraged to help enhance the visual effects via detecting infrared radiation in the surrounding environment, but the infrared videos are undesirable due to the lack of detailed semantic information. In such a case, an effective video-to-video translation method from the infrared domain to the visible light counterpart is strongly needed by overcoming the intrinsic huge gap between infrared and visible fields. To address this challenging problem, we propose an infrared-to-visible (I2V) video translation method I2V-GAN to generate fine-grained and spatial-temporal consistent visible light videos by given unpaired infrared videos. Technically, our model capitalizes on three types of constraints: 1) adversarial constraint to generate synthetic frames that are similar to the real ones, 2) cyclic consistency with the introduced perceptual loss for effective content conversion as well as style preservation, and 3) similarity constraints across and within domains to enhance the content and motion consistency in both spatial and temporal spaces at a fine-grained level. Furthermore, the current public available infrared and visible light datasets are mainly used for object detection or tracking, and some are composed of discontinuous images which are not suitable for video tasks. Thus, we provide a new dataset for infrared-to-visible video translation, which is named IRVI. Specifically, it has 12 consecutive video clips of vehicle and monitoring scenes, and both infrared and visible light videos could be apart into 24352 frames. Comprehensive experiments on IRVI validate that I2V-GAN is superior to the compared state-of-the-art methods in the translation of infrared-to-visible videos with higher fluency and finer semantic details. Moreover, additional experimental results on the flower-to-flower dataset indicate I2V-GAN is also applicable to other video translation tasks. The code and IRVI dataset are available at https://github.com/BIT-DA/I2V-GAN.
Automated News Summarization Using Transformers

The amount of text data available online is increasing at a very fast pace hence text summarization has become essential. Most of the modern recommender and text classification systems require going through a huge amount of data. Manually generating precise and fluent summaries of lengthy articles is a very tiresome and time-consuming task. Hence generating automated summaries for the data and using it to train machine learning models will make these models space and time-efficient. Extractive summarization and abstractive summarization are two separate methods of generating summaries. The extractive technique identifies the relevant sentences from the original document and extracts only those from the text. Whereas in abstractive summarization techniques, the summary is generated after interpreting the original text, hence making it more complicated. In this paper, we will be presenting a comprehensive comparison of a few transformer architecture based pre-trained models for text summarization. For analysis and comparison, we have used the BBC news dataset that contains text data that can be used for summarization and human generated summaries for evaluating and comparing the summaries generated by machine learning models.
BigGraphVis: Leveraging Streaming Algorithms and GPU Acceleration for Visualizing Big Graphs

Graph layouts are key to exploring massive graphs. An enormous number of nodes and edges do not allow network analysis software to produce meaningful visualization of the pervasive networks. Long computation time, memory and display limitations encircle the software's ability to explore massive graphs. This paper introduces BigGraphVis, a new parallel graph visualization method that uses GPU parallel processing and community detection algorithm to visualize graph communities. We combine parallelized streaming community detection algorithm and probabilistic data structure to leverage parallel processing of Graphics Processing Unit (GPU). To the best of our knowledge, this is the first attempt to combine the power of streaming algorithms coupled with GPU computing to tackle big graph visualization challenges. Our method extracts community information in a few passes on the edge list, and renders the community structures using the ForceAtlas2 algorithm. Our experiment with massive real-life graphs indicates that about 70 to 95 percent speedup can be achieved by visualizing graph communities, and the visualization appears to be meaningful and reliable. The biggest graph that we examined contains above 3 million nodes and 34 million edges, and the layout computation took about five minutes. We also observed that the BigGraphVis coloring strategy can be successfully applied to produce a more informative ForceAtlas2 layout.
SumPubMed: Summarization Dataset of PubMed Scientific Articles

Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SumPubMed , using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SumPubMed . SumPubMed is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SumPubMed . Thus, SumPubMed opens new avenues for the future improvement of models as well as the development of new evaluation metrics.
RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions

A major challenge in the field of education is providing review schedules that present learned items at appropriate intervals to each student so that memory is retained over time. In recent years, attempts have been made to formulate item reviews as sequential decision-making problems to realize adaptive instruction based on the knowledge state of students. It has been reported previously that reinforcement learning can help realize mathematical models of students learning strategies to maintain a high memory rate. However, optimization using reinforcement learning requires a large number of interactions, and thus it cannot be applied directly to actual students. In this study, we propose a framework for optimizing teaching strategies by constructing a virtual model of the student while minimizing the interaction with the actual teaching target. In addition, we conducted an experiment considering actual instructions using the mathematical model and confirmed that the model performance is comparable to that of conventional teaching methods. Our framework can directly substitute mathematical models used in experiments with human students, and our results can serve as a buffer between theoretical instructional optimization and practical applications in e-learning systems.
Physics-Informed Super-Resolution for Real-Time Prediction of Urban Micrometeorology

The present paper proposes a physics-informed super-resolution (SR) model based on a convolutional neural network and applies it to the near-surface temperature in urban areas with the scaling factor of 4. The SR model incorporates a skip connection, a channel attention mechanism, and separated feature extractors for the inputs of temperature, building height, downward shortwave radiation, and horizontal velocity. We train the SR model with sets of low-resolution (LR) and high-resolution (HR) images from building-resolving large-eddy simulations (LESs) in an urban city. The generalization capability of the SR model is conﬁrmed with LESs in another city. The estimated HR temperature ﬁelds are more accurate than those of the bicubic interpolation and image SR model that takes only the temperature as input. Except for the temperature input, the building height is the most important to reconstruct the HR temperature and enables the SR model to reduce errors in temperature near building boundaries. The analysis of attention weights indicates that the importance of building height increases as the downward shortwave radiation becomes larger. The contrast between sun and shade is strengthened with the increase in solar radiation, which may aﬀect the tem-1
Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System

Latest CNN-based object detection models are quite accurate but require a high-performance GPU to run in real-time. They still are heavy in terms of memory size and speed for an embedded system with limited memory space. Since the object detection for autonomous system is run on an embedded processor, it is preferable to compress the detection network as light as possible while preserving the detection accuracy. There are several popular lightweight detection models but their accuracy is too low for safe driving applications. Therefore, this paper proposes a new object detection model, referred as YOffleNet, which is compressed at a high ratio while minimizing the accuracy loss for real-time and safe driving application on an autonomous system. The backbone network architecture is based on YOLOv4, but we could compress the network greatly by replacing the high-calculation-load CSP DenseNet with the lighter modules of ShuffleNet. Experiments with KITTI dataset showed that the proposed YOffleNet is compressed by 4.7 times than the YOLOv4-s that could achieve as fast as 46 FPS on an embedded GPU system(NVIDIA Jetson AGX Xavier). Compared to the high compression ratio, the accuracy is reduced slightly to 85.8% mAP, that is only 2.6% lower than YOLOv4-s. Thus, the proposed network showed a high potential to be deployed on the embedded system of the autonomous system for the real-time and accurate object detection applications.
SwarmPlay: A Swarm of Nano-Quadcopters Playing Tic-tac-toe Board Game against a Human

We present a new paradigm of games, i.e. SwarmPlay, where each playing component is presented by an individual drone that has its own mobility and swarm intelligence to win against a human player. The motivation behind the research is to make the games with machines tangible and interactive. Although some research on the robotic players for board games already exists, e.g., chess, the SwarmPlay technology has the potential to offer much more engagement and interaction with a human as it proposes a multi-agent swarm instead of a single interactive robot. The proposed system consists of a robotic swarm, a workstation, a computer vision (CV), and Game Theory-based algorithms. A novel game algorithm was developed to provide a natural game experience to the user. The preliminary user study revealed that participants were highly engaged in the game with drones (69% put a maximum score on the Likert scale) and found it less artificial compared to the regular computer-based systems (77% put maximum score). The affection of the user’s game perception from its outcome was analyzed and put under discussion. User study revealed that SwarmPlay has the potential to be implemented in a wider range of games, significantly improving human-drone interactivity.
Green Supply Chain Pricing and Performance: a Fairness Preferences and Green Marketing Perspective

This study investigates optimal decisions in a green supply chain consisting of a manufacturer and a leading retailer considering the green marketing and fairness preferences of member firms. Four Stackelberg game decision models are constructed in which the manufacturer and the re-tailer engage in green marketing separately when the manufacturer has no and has fairness preferences. The impacts of fairness preferences and green marketing on the optimal decision in the green supply chain are comparatively analyzed. The study finds that member firms perform green marketing regardless of the presence or absence of fairness preferences and that such be-havior increases the wholesale price, retail price, and market demand of low-carbon products as well as the profits of member firms and the supply chain. A more interesting finding is that the profit growth of member firms and the supply chain due to the manufacturer’s green marketing is more pronounced than that due to the retailer’s green marketing. When the retailer and the manufacturer engage in conduct green marketing, the manufacturer's fairness preferences have different effects on the wholesale price, retail price, market demand, level of green marketing efforts, member enterprises and profits of supply chain. Therefore, firms should consider the impact of green marketing and fairness preferences to make pricing and performance decisions, so as to achieve efficient operation of the whole supply chain and avoid double marginal effects. Finally, the above conclusions are verified through numerical simulation, providing a reference for the decision-making of member firms in the green supply chain.
Ontology Service Center: a datahub for ontology application

With the growth of data-oriented research in humanities, a large number of research datasets have been created and published through web services. However, how to discover, integrate and reuse these distributed heterogeneous research datasets is a challenging task. Ontology is the soul between series digital humanities resources, which provides a good way for people to discover and understand these datasets. With the release of more and more linked open data and knowledge bases, a large number of ontologies have been produced at the same time. These ontologies have different publishing formats, consumption patterns, and interactions ways, which are not conductive to the user’s understanding of the datasets and the reuse of the ontologies. The Ontology Service Center platform consists of Ontology Query Center and Ontology Validation Center, mainly using linked data and ontology-based technologies. The Ontology Query Center realizes the functions of ontology publishing, querying, data interaction and online browsing, while the Ontology Validation Center can verify the status of using certain ontologies in the linked datasets. The empirical part of the paper uses the Confucius portrait as an example of how OSC can be used in the semantic annotation of images. In a word, the purpose of this paper is to construct the applied ecology of ontology to promote the development of knowledge graphs and the spread of ontology.
Simulation of surgery for supratentorial gliomas in virtual reality using a 3D volume rendering technique: a poor man's neuronavigation.

OBJECTIVE Different techniques of performing image-guided neurosurgery exist, namely, neuronavigation systems, intraoperative ultrasound, and intraoperative MRI, each with its limitations. Except for ultrasound, other methods are expensive. Three-dimensional virtual reconstruction and surgical simulation using 3D volume rendering (VR) is an economical and excellent technique for preoperative surgical planning and image-guided neurosurgery. In this article, the authors discuss several nuances of the 3D VR technique that have not yet been described. METHODS The authors included 6 patients with supratentorial gliomas who underwent surgery between January 2019 and March 2021. Preoperative clinical data, including patient demographics, preoperative planning details (done using the VR technique), and intraoperative details, including relevant photos and videos, were collected. RadiAnt software was used for generating virtual 3D images using the VR technique on a computer running Microsoft Windows. RESULTS The 3D VR technique assists in glioma surgery with a preoperative simulation of the skin incision and craniotomy, virtual cortical surface marking and navigation for deep-seated gliomas, preoperative visualization of morbid cortical surface and venous anatomy in surfacing gliomas, identifying the intervenous surgical corridor in both surfacing and deep-seated gliomas, and pre- and postoperative virtual 3D images highlighting the exact spatial geometric residual tumor location and extent of resection for low-grade gliomas (LGGs). CONCLUSIONS Image-guided neurosurgery with the 3D VR technique using RadiAnt software is an economical, easy-to-learn, and user-friendly method of simulating glioma surgery, especially in resource-constrained countries where expensive neuronavigation systems are not readily available. Apart from cortical sulci/gyri anatomy, FLAIR sequences are ideal for the 3D visualization of nonenhancing diffuse LGGs using the VR technique. In addition to cortical vessels (especially veins), contrast MRI sequences are perfect for the 3D visualization of contrast-enhancing high-grade gliomas.