Miracle-dz/UniFAR-FTUs huggingface dataset:
Sliding Mode Attitude Maneuver Control for Rigid Spacecraft without Unwinding

In this paper, attitude maneuver control without unwinding phenomenon is investigated for rigid spacecraft. First, a novel switching function is constructed by a hyperbolic sine function. It is shown that the spacecraft system possesses the unwinding-free performance when the system states are on the sliding surface. Based on the designed switching function, a sliding mode controller is developed to ensure the robustness of the attitude maneuver control system. Another essential feature of the presented attitude control law is that a dynamic parameter is introduced to guarantee the unwinding-free performance when the system states are outside the sliding surface. The simulation results demonstrate that the unwinding phenomenon is avoided during the attitude maneuver of a rigid spacecraft by adopting the constructed switching function and the proposed attitude control scheme.
SportsXR - Immersive Analytics in Sports

We wish to thank Coach Kathy Delaney-Smith, Mike Roux, Mark Kaliris, and Lindsay Werner at Harvard Women’s Basketball, and Mike Sotsky and Casey Brinn at Harvard Men’s Basketball for their time and expertise. This research is supported in part by King Abdullah University of Science and Technology (KAUST) and the KAUST Office of Sponsored Research (OSR) award OSR-2015-CCF-2533-01.
Biophysically detailed mathematical models of multiscale cardiac active mechanics

We propose four novel mathematical models, describing the microscopic mechanisms of force generation in the cardiac muscle tissue, which are suitable for multiscale numerical simulations of cardiac electromechanics. Such models are based on a biophysically accurate representation of the regulatory and contractile proteins in the sarcomeres. Our models, unlike most of the sarcomere dynamics models that are available in the literature and that feature a comparable richness of detail, do not require the time-consuming Monte Carlo method for their numerical approximation. Conversely, the models that we propose only require the solution of a system of PDEs and/or ODEs (the most reduced of the four only involving 20 ODEs), thus entailing a significant computational efficiency. By focusing on the two models that feature the best trade-off between detail of description and identifiability of parameters, we propose a pipeline to calibrate such parameters starting from experimental measurements available in literature. Thanks to this pipeline, we calibrate these models for room-temperature rat and for body-temperature human cells. We show, by means of numerical simulations, that the proposed models correctly predict the main features of force generation, including the steady-state force-calcium and force-length relationships, the length-dependent prolongation of twitches and increase of peak force, the force-velocity relationship. Moreover, they correctly reproduce the Frank-Starling effect, when employed in multiscale 3D numerical simulation of cardiac electromechanics.
Neural Approaches for Data Driven Dependency Parsing in Sanskrit

Data-driven approaches for dependency parsing have been of great interest in Natural Language Processing for the past couple of decades. However, Sanskrit still lacks a robust purely data-driven dependency parser, probably with an exception to Krishna (2019). This can primarily be attributed to the lack of availability of task-specific labelled data and the morphologically rich nature of the language. In this work, we evaluate four different data-driven machine learning models, originally proposed for different languages, and compare their performances on Sanskrit data. We experiment with 2 graph based and 2 transition based parsers. We compare the performance of each of the models in a low-resource setting, with 1,500 sentences for training. Further, since our focus is on the learning power of each of the models, we do not incorporate any Sanskrit specific features explicitly into the models, and rather use the default settings in each of the paper for obtaining the feature functions. In this work, we analyse the performance of the parsers using both an in-domain and an out-of-domain test dataset. We also investigate the impact of word ordering in which the sentences are provided as input to these systems, by parsing verses and their corresponding prose order (anvaya) sentences.
Detailed 2D-3D Joint Representation for Human-Object Interaction

Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually utilized in HOI learning since its view-independence. However, rough 3D body joints just carry sparse body information and are not sufficient to understand complex interactions. Thus, we need detailed 3D body shape to go further. Meanwhile, the interacted object in 3D is also not fully studied in HOI learning. In light of these, we propose a detailed 2D-3D joint representation learning method. First, we utilize the single-view human body capture method to obtain detailed 3D body, face and hand shapes. Next, we estimate the 3D object location and size with reference to the 2D human-object spatial configuration and object category priors. Finally, a joint learning framework and cross-modal consistency tasks are proposed to learn the joint HOI representation. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images. Extensive experiments in large-scale HOI benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code and data are available at https://github.com/DirtyHarryLYL/DJ-RN.
Network Rewiring in the r-K Plane

We generate correlated scale-free networks in the configuration model through a new rewiring algorithm that allows one to tune the Newman assortativity coefficient r and the average degree of the nearest neighbors K (in the range −1≤r≤1, K≥〈k〉). At each attempted rewiring step, local variations Δr and ΔK are computed and then the step is accepted according to a standard Metropolis probability exp(±Δr/T), where T is a variable temperature. We prove a general relation between Δr and ΔK, thus finding a connection between two variables that have very different definitions and topological meaning. We describe rewiring trajectories in the r-K plane and explore the limits of maximally assortative and disassortative networks, including the case of small minimum degree (kmin≥1), which has previously not been considered. The size of the giant component and the entropy of the network are monitored in the rewiring. The average number of second neighbors in the branching approximation z¯2,B is proven to be constant in the rewiring, and independent from the correlations for Markovian networks. As a function of the degree, however, the number of second neighbors gives useful information on the network connectivity and is also monitored.
Enhancing Deep Neural Networks Against Adversarial Malware Examples

Machine learning based malware detection is known to be vulnerable to adversarial evasion attacks. The state-of-the-art is that there are no effective countermeasures against these attacks. Inspired by the AICS'2019 Challenge organized by the MIT Lincoln Lab, we systematize a number of principles for enhancing the robustness of neural networks against adversarial malware evasion attacks. Some of these principles have been scattered in the literature, but others are proposed in this paper for the first time. Under the guidance of these principles, we propose a framework for defending against adversarial malware evasion attacks. We validated the framework using the Drebin dataset of Android malware. We applied the defense framework to the AICS'2019 Challenge and won, without knowing how the organizers generated the adversarial examples. However, we see a ~22\% difference between the accuracy in the experiment with the Drebin dataset (for binary classification) and the accuracy in the experiment with respect to the AICS'2019 Challenge (for multiclass classification). We attribute this gap to a fundamental barrier that without knowing the attacker's manipulation set, the defender cannot do effective Adversarial Training.
The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition.

Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are difficult to train. This makes it challenging to adapt these models to changing conditions, a requirement from FER models given the subjective nature of affect perception and understanding. In this paper, we address this problem by formalising the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We perform a series of experiments on different benchmark datasets to demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER.
Learning-Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems

Millimeter Wave (mmWave) communications with full-duplex (FD) have the potential of increasing the spectral efficiency, relative to those with half-duplex. However, the residual self-interference (SI) from FD and high pathloss inherent to mmWave signals may degrade the system performance. Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the channel gain and mitigate interference with reasonable complexity. However, conventional HBF approaches for FD mmWave systems are based on optimization processes, which are either too complex or strongly rely on the quality of channel state information (CSI). We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and convolutional neural networks based HBF (CNN-HBF). Specifically, we first propose an alternating direction method of multipliers (ADMM) based algorithm to achieve SI cancellation beamforming, and then use a majorization-minimization (MM) based algorithm for joint transmitting and receiving HBF optimization. To train the learning networks, we simulate noisy channels as input, and select the hybrid beamformers calculated by proposed algorithms as targets. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides, the online prediction time of proposed learning based schemes is almost 20 times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is about 600 times faster than that of CNN-HBF with 64 transmitting and receiving antennas.
A Two-Stage Service Restoration Method for Electric Power Distribution Systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. The emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, although enhance the self-healing capability and allow faster recovery, pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. In this paper, we propose a novel two-stage restoration framework to generate restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and CLPU models. It is demonstrated using a multi-feeder test case that the proposed framework is effective in utilizing all available resources to quickly restore the service and generates an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.
Optimal Coverage Control for Swarm Robot Systems using a Mean Field Game

Swarm robot systems, in which many robots cooperate to perform one task, have attracted a great of attention in recent years. In controlling these systems, the trade-off between the global optimality and the scalability is a major challenge. In the present paper, we focus on the mean field game (MFG) as a possible control method for swarm robot systems. The MFG is a framework to deduce a macroscopic model for describing robot density profiles from the microscopic robot dynamics. For a coverage control problem aiming at uniformly distributing robots over space, we extend the original MFG in order to present two methods for optimally controlling swarm robots: the model predictive mean field game (MP-MFG) and the best reply strategy (BRS). Importantly, the MP-MFG converges to the BRS in the limit of prediction time going to zero, which is also confirmed by our numerical experiments. In addition, we show numerically that the optimal input is obtained in both the MP-MFG and the BRS, and widening the prediction time of the MP-MFG improves the control performance.
Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches

Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science‐Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space

Active learning for sentence understanding aims at discovering informative unlabeled data for annotation and therefore reducing the demand for labeled data. We argue that the typical uncertainty sampling method for active learning is time-consuming and can hardly work in real-time, which may lead to ineffective sample selection. We propose adversarial uncertainty sampling in discrete space (AUSDS) to retrieve informative unlabeled samples more efficiently. AUSDS maps sentences into latent space generated by the popular pre-trained language models, and discover informative unlabeled text samples for annotation via adversarial attack. The proposed approach is extremely efficient compared with traditional uncertainty sampling with more than 10x speedup. Experimental results on five datasets show that AUSDS outperforms strong baselines on effectiveness.
Lμ−Lτ
 effects to quarks and leptons from flavor unification

In the family grand unification models (fGUTs), we propose that gauge U(1)'s beyond the minimal GUT gauge group are family gauge symmetries. For the symmetry $L_\mu-L_\tau$, i.e. $Q_{2}-Q_{3}$ in our case, to be useful for the LHC anomaly, we discuss an SU(9) fGUT and also present an example in Georgi's SU(11) fGUT.
A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy

Recently, joint registration and segmentation has been formulated in a deep learning setting, by the definition of joint loss functions. In this work, we investigate joining these tasks at the architectural level. We propose a registration network that integrates segmentation propagation between images, and a segmentation network to predict the segmentation directly. These networks are connected into a single joint architecture via so-called cross-stitch units, allowing information to be exchanged between the tasks in a learnable manner. The proposed method is evaluated in the context of adaptive image-guided radiotherapy, using daily prostate CT imaging. Two datasets from different institutes and manufacturers were involved in the study. The first dataset was used for training (12 patients) and validation (6 patients), while the second dataset was used as an independent test set (14 patients). In terms of mean surface distance, our approach achieved $1.06 \pm 0.3$ mm, $0.91 \pm 0.4$ mm, $1.27 \pm 0.4$ mm, and $1.76 \pm 0.8$ mm on the validation set and $1.82 \pm 2.4$ mm, $2.45 \pm 2.4$ mm, $2.45 \pm 5.0$ mm, and $2.57 \pm 2.3$ mm on the test set for the prostate, bladder, seminal vesicles, and rectum, respectively. The proposed multi-task network outperformed single-task networks, as well as a network only joined through the loss function, thus demonstrating the capability to leverage the individual strengths of the segmentation and registration tasks. The obtained performance as well as the inference speed make this a promising candidate for daily re-contouring in adaptive radiotherapy, potentially reducing treatment-related side effects and improving quality-of-life after treatment.
ECCOLA - a Method for Implementing Ethically Aligned AI Systems

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Can We Use Stack Overflow as a Source of Explainable Bug-fix Data?

Bug-fix data sets are important for building various software engineering support tools, such as program repair or Application Programming Interface (API) misuse detection. These data sets are typically constructed from mining commit history in version-control systems. In this paper, we investigate whether Stack Overflow can be used as an additional source for bug-fix or code improvement data sets. Comments on Stack Overflow provide an effective way for developers to point out problems with existing answers, alternative solutions, or pitfalls. Given its crowd-sourced nature, answers are often updated to incorporate these suggestions. In this paper, we mine comment-edit pairs from Stack Overflow and investigate their potential usefulness for constructing the above data sets. These comment-edit pairs have the added benefit of having concrete descriptions/explanations of why the change is needed as well as potentially having less tangled changes to deal with. We first design a technique to extract related comment-edit pairs and then qualitatively and quantitatively investigate the nature of these pairs. We find that the majority of comment-edit pairs are not tangled, but find that only 27% of the studied pairs are potentially useful for the above applications. We categorize the types of mined pairs and find that the highest ratio of useful pairs come from those categorized as Correction, Flaw, and Obsolete. Our work is the first to investigate Stack Overflow comment-edit pairs and opens the door for future work in this direction. Based on our findings and observations, we provide concrete suggestions on how to potentially identify a larger set of useful comment-edit pairs, which can also be facilitated by our shared data.
DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator

Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog. Existing systems for this task employ the transformers or recurrent neural network-based architecture with the encoder-decoder framework. Even though these techniques show superior performance for this task, they have significant limitations: the model easily overfits only to memorize the grammatical patterns; the model follows the prior distribution of the vocabularies in a dataset. To alleviate the problems, we propose a Multimodal Semantic Transformer Network. It employs a transformer-based architecture with an attention-based word embedding layer that generates words by querying word embeddings. With this design, our model keeps considering the meaning of the words at the generation stage. The empirical results demonstrate the superiority of our proposed model that outperforms most of the previous works for the AVSD task.
Recommendation system using a deep learning and graph analysis approach

When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold‐start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this article, we have proposed a novel recommendation method based on matrix factorization and graph analysis methods, namely Louvain for community detection and HITS for finding the most important node within the trust network. In addition, we leverage deep autoencoders to initialize users and items latent factors, and the Node2vec deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on Ciao and Epinions standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state‐of‐the‐art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements, that is, 15.56% RMSE improvement for Epinions and 18.41% RMSE improvement for Ciao.