Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak Measure-Valued Solutions of a Nonlinear Hyperbolic Conservation Law

We revisit a well-established model for highly re-entrant semi-conductor manufacturing systems, and analyze it in the setting of states, in- and outfluxes being Borel measures. This is motivated by the lack of optimal solutions in the L1-setting for transitions from a smaller to a larger equilibrium with zero backlog. Key innovations involve dealing with discontinuous velocities in the presence of point masses, and a finite domain with in- and outfluxes. Taking a Lagrangian point of view, we establish existence and uniqueness of solutions, and formulate a notion of weak solution. We prove continuity of the flow with respect to time (and almost also with respect to the initial state). Due to generally discontinuous velocities, these delicate regularity results hold only with respect to carefully crafted semi-norms that are modifications of the flat norm. Generally. the solution is not continuous with respect to any norm on the space of measures.
Radial Evolution of Coronal Mass Ejections Between MESSENGER, Venus Express, STEREO, and L1: Catalog and Analysis

Our knowledge of the properties of coronal mass ejections (CMEs) in the inner heliosphere is constrained by the relative lack of plasma observations between the Sun and 1 AU. In this work, we present a comprehensive catalog of 47 CMEs measured in situ measurements by two or more radially aligned spacecraft (MESSENGER, Venus Express, STEREO, Wind/ACE). We estimate the CME impact speeds at Mercury and Venus using a drag‐based model and present an average propagation profile of CMEs (speed and deceleration/acceleration) in the inner heliosphere. We find that CME deceleration continues past Mercury's orbit but most of the deceleration occurs between the Sun and Mercury. We examine the exponential decrease of the maximum magnetic field strength in the CME with heliocentric distance using two approaches: a modified statistical method and analysis from individual conjunction events. Findings from both the approaches are on average consistent with previous studies but show significant event‐to‐event variability. We also find the expansion of the CME sheath to be well fit by a linear function. However, we observe the average sheath duration and its increase to be fairly independent of the initial CME speed, contradicting commonly held knowledge that slower CMEs drive larger sheaths. We also present an analysis of the 3 November 2011 CME observed in longitudinal conjunction between MESSENGER, Venus Express, and STEREO‐B focusing on the expansion of the CME and its correlation with the exponential falloff of the maximum magnetic field strength in the ejecta.
Congruence pairs of principal MS-algebras and perfect extensions

Abstract The notion of a congruence pair for principal MS-algebras, simpler than the one given by Beazer for K2-algebras [6], is introduced. It is proved that the congruences of the principal MS-algebras L correspond to the MS-congruence pairs on simpler substructures L°° and D(L) of L that were associated to L in [4]. An analogy of a well-known Grätzer’s problem [11: Problem 57] formulated for distributive p-algebras, which asks for a characterization of the congruence lattices in terms of the congruence pairs, is presented here for the principal MS-algebras (Problem 1). Unlike a recent solution to such a problem for the principal p-algebras in [2], it is demonstrated here on the class of principal MS-algebras, that a possible solution to the problem, though not very descriptive, can be simple and elegant. As a step to a more descriptive solution of Problem 1, a special case is then considered when a principal MS-algebra L is a perfect extension of its greatest Stone subalgebra LS. It is shown that this is exactly when de Morgan subalgebra L°° of L is a perfect extension of the Boolean algebra B(L). Two examples illustrating when this special case happens and when it does not are presented.
Repeating fast radio bursts with WSRT/Apertif

Context. Repeating fast radio bursts (FRBs) present excellent opportunities to identify FRB progenitors and host environments as well as to decipher the underlying emission mechanism. Detailed studies of repeating FRBs might also hold clues as to the origin of FRBs as a population. Aims. We aim to detect bursts from the first two repeating FRBs, FRB 121102 (R1) and FRB 180814.J0422+73 (R2), and to characterise their repeat statistics. We also want to significantly improve the sky localisation of R2 and identify its host galaxy. Methods. We used the Westerbork Synthesis Radio Telescope to conduct extensive follow-up of these two repeating FRBs. The new phased-array feed system, Apertif, allows one to cover the entire sky position uncertainty of R2 with fine spatial resolution in a single pointing. The data were searched for bursts around the known dispersion measures of the two sources. We characterise the energy distribution and the clustering of detected R1 bursts. Results. We detected 30 bursts from R1. The non-Poissonian nature is clearly evident from the burst arrival times, which is consistent with earlier claims. Our measurements indicate a dispersion measure (DM) of 563.5(2) pc cm−3, suggesting a significant increase in DM over the past few years. Assuming a constant position angle across the burst, we place an upper limit of 8% on the linear polarisation fraction for the brightest burst in our sample. We did not detect any bursts from R2. Conclusions. A single power-law might not fit the R1 burst energy distribution across the full energy range or widely separated detections. Our observations provide improved constraints on the clustering of R1 bursts. Our stringent upper limits on the linear polarisation fraction imply a significant depolarisation, either intrinsic to the emission mechanism or caused by the intervening medium at 1400 MHz, which is not observed at higher frequencies. The non-detection of any bursts from R2, despite nearly 300 h of observations, implies either a highly clustered nature of the bursts, a steep spectral index, or a combination of the two assuming that the source is still active. Another possibility is that R2 has turned off completely, either permanently or for an extended period of time.
On the Morality of Artificial Intelligence

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.
Equations Derivation of VINS-Mono

The VINS-Mono is a monocular visual-inertial 6 DOF state estimator proposed by Aerial Robotics Group at HKUST in 2017, which can be performed on MAVs, smartphones and many other intelligent platforms. It is a state-of-the-art visual-inertial odometry algorithms which has gained extensive attention worldwide. The main equations including IMU preintegration, visual/inertial co-initialization and tightly-coupled nonlinear optimization are derived and analyzed in this manuscript.
Anytime Planning: A Motion Planner for Dynamic Environment

Motion planning in the presence of multiple dynamic obstacles is an important research problem from the perspective of autonomous vehicles as well as space-constrained multi-robot work environment. In this paper, we address the motion planning problem for multiple dynamic obstacle rich environment and propose a probabilistically, complete novel motion planning algorithm. Our claim is that given a fixed path cost i.e. the Euclidean path length, the proposed algorithm plans a path with the least computational time as compared to the state-of-the-art techniques. At the same time, given the time duration for planning, it plans the minimum cost path. Dynamic constraints have been taken into consideration while designing the planner such that the optimal planned path is feasible for implementation. The results of extensive simulation experiments show that the proposed sequential BIT* outperforms the DOVS both in the planned path length as well as the path generation time.
Convolutional Quantum-Like Language Model with Mutual-Attention for Product Rating Prediction

Recommender systems are designed to help mitigate information overload users experience during online shopping. Recent work explores neural language models to learn user and item representations from user reviews and combines such representations with rating information. Most existing convolutional-based neural models take pooling immediately after convolution and loses the interaction information between the latent dimension of convolutional feature vectors along the way. Moreover, these models usually take all feature vectors at higher levels as equal and do not take into consideration that some features are more relevant to this specific user-item context. To bridge these gaps, this paper proposes a convolutional quantum-like language model with mutual-attention for rating prediction (ConQAR). By introducing a quantum-like density matrix layer, interactions between latent dimensions of convolutional feature vectors are well captured. With the attention weights learned from the mutual-attention layer, final representations of a user and an item absorb information from both itself and its counterparts for making rating prediction. Experiments on two large datasets show that our model outperforms multiple state-of-the-art CNN-based models. We also perform an ablation test to analyze the independent effects of the two components of our model. Moreover, we conduct a case study and present visualizations of the quantum probabilistic distributions in one user and one item review document to show that the learned distributions capture meaningful information about this user and item, and can be potentially used as textual profiling of the user and item.
Predicting Attributes of Nodes Using Network Structure

In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important task with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attribute values can be predicted by treating each node as a data point described by attributes and employing classification/regression algorithms. However, in social networks, there is complex interdependence between node attributes and pairwise interaction. For instance, attributes of nodes are influenced by their neighbors (social influence), and neighborhoods (friendships) between nodes are established based on pairwise (dis)similarity between their attributes (social selection). In this article, we establish that information in network topology is extremely useful in determining node attributes. In particular, we use self- and cross-proclivity measures (quantitative measures of how much a node attribute depends on the same and other attributes of its neighbors) to predict node attributes. We propose a feature map to represent a node with respect to a specific attribute a, using all attributes of its h-hop neighbors. Different classifiers are then learned on these feature vectors to predict the value of attribute a. We perform extensive experimentation on 10 real-world datasets and show that the proposed method significantly outperforms known approaches in terms of prediction accuracy.
Probabilistic Filtered Soft Labels for Domain Adaptation

Many domain adaptation (DA) methods aim to project the source and target domains into a common feature space, where the inter-domain distributional differences are reduced and some intra-domain properties preserved. Recent research obtains their respective new representations using some predefined statistics. However, they usually formulate the class-wise statistics using the pseudo hard labels due to no labeled target data, such as class-wise MMD and class scatter matrice. The probabilities of data points belonging to each class given by the hard labels are either 0 or 1, while the soft labels could relax the strong constraint of hard labels and provide a random value between them. Although existing work have noticed the advantage of soft labels, they either deal with thoes class-wise statistics inadequately or introduce those small irrelevant probabilities in soft labels. Therefore, we propose the filtered soft labels to discard thoes confusing probabilities, then both of the class-wise MMD and class scatter matrice are modeled in this way. In order to obtain more accurate filtered soft labels, we take advantage of a well-designed Graph-based Label Propagation (GLP) method, and incorporate it into the DA procedure to formulate a unified framework.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust Factorization and Projective Dictionary Learning (J-RFDL) model is presented. The setting of J-RFDL aims at improving the data representations by enhancing the robustness to outliers and noise in data, encoding the reconstruction error more accurately and obtaining hybrid salient coefficients with accurate reconstruction ability. Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient. To make the encoding process robust to noise in data, J-RFDL clearly uses sparse L2, 1-norm that can potentially minimize the factorization and reconstruction errors jointly by forcing rows of the reconstruction errors to be zeros. To deliver salient coefficients with good structures to reconstruct given data well, J-RFDL imposes the joint low-rank and sparse constraints on the embedded coefficients with a synthesis dictionary. Based on the hybrid salient coefficients, we also extend J-RFDL for the joint classification and propose a discriminative J-RFDL model, which can improve the discriminating abilities of learnt coefficients by minimizing the classification error jointly. Extensive experiments on public datasets demonstrate that our formulations can deliver superior performance over other state-of-the-art methods.
An Abstraction Model for Semantic Segmentation Algorithms

Semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, sematic segmentation is used in many tasks such as cancer detection, robot-assisted surgery, satellite image analysis, self-driving car control, etc. In this process, accuracy and efficiency are the two crucial goals for this purpose, and there are several state of the art neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and reducing the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researches to achieve a comprehensive view of the field. To offer a comprehensive view, in this paper, an abstraction model for the task of semantic segmentation is offered. The proposed framework consists of four general blocks that cover the majority of majority of methods that have been proposed for semantic segmentation. We also compare different approaches and consider the importance of each part in the overall performance of a method.
On the obstruction to the Hasse principle for multinorm equations

We investigate local-global principles for multinorm equations over a global field. To this extent, we generalize work of Drakokhrust and Platonov to provide explicit and computable formulae for the obstructions to the Hasse principle and weak approximation for multinorm equations. We illustrate the scope of this technique by extending results of Bayer-Fluckiger--Lee--Parimala, Demarche--Wei and Pollio.
Learn to Segment Retinal Lesions and Beyond

Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path redesigned to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side-attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large-scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading.
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study

The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI sequences using an input dropout layer, thus simulating the scenario of missing MRI data by training on the full set and all possible subsets of the input data. This retrospective, multi-center study, evaluated 165 patients with brain metastases. A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients. The segmentation results were compared with the performance of a state-of-the-art DeepLabV3 model. The MR sequences in the training set included pre- and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth were established by experienced neuroradiologists. The results were evaluated using precision, recall, Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989+-0.029 for the DropOut model and 0.989+-0.023 for the DeepLabV3 model (p=0.62). The DropOut model showed a significantly higher Dice score compared to the DeepLabV3 model (0.795+-0.105 vs. 0.774+-0.104, p=0.017), and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p<0.001) using a 10mm3 lesion-size limit. The DropOut model may facilitate accurate detection and segmentation of brain metastases on a multi-center basis, even when the test cohort is missing MRI input data.
Effective Data Augmentation with Multi-Domain Learning GANs

For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously high costs. In this work, we propose an effective data augmentation method based on generative adversarial networks (GANs), called Domain Fusion. Our key idea is to import the knowledge contained in an outer dataset to a target model by using a multi-domain learning GAN. The multi-domain learning GAN simultaneously learns the outer and target dataset and generates new samples for the target tasks. The simultaneous learning process makes GANs generate the target samples with high fidelity and variety. As a result, we can obtain accurate models for the target tasks by using these generated samples even if we only have an extremely low volume target dataset. We experimentally evaluate the advantages of Domain Fusion in image classification tasks on 3 target datasets: CIFAR-100, FGVC-Aircraft, and Indoor Scene Recognition. When trained on each target dataset reduced the samples to 5,000 images, Domain Fusion achieves better classification accuracy than the data augmentation using fine-tuned GANs. Furthermore, we show that Domain Fusion improves the quality of generated samples, and the improvements can contribute to higher accuracy.
Board 436 - Research Abstract Evaluation of Non-Technical Skills in Residency Candidates by Medical Students Versus Professional Facilitators (Submission #1284)

Introduction/Background Standardized patients in simulated scenarios can help to evaluate non-technical skills such as verbal and nonverbal communication, affective stability and ethical decision-making. The application of standardized patients is often limited by cost and time required for training. Additionally, academic faculty are required for verification and oversight, further adding to the manpower necessity of simulated scenarios. Medical students have been reliably utilized as a resource in simulation as evaluators using a checklist format. We sought to determine if third year medical students have the ability to evaluate non-technical skills in residency candidates as effectively as senior academic faculty. Methods Fifteen third year medical students were trained by a psychiatrist to elicit particular responses or behaviors in challenging simulated scenarios. Twenty two residency candidates participated in a total of fifteen scenarios over a two year period. A professional facilitator (MD or RN) with over twenty years of teaching experience in an academic medical setting observed each scenario. Both the student-actor and the facilitator evaluated all candidates. The ratings were correlated between the actor and the facilitator. Results The correlation of ratings from the first year of the study yields a slope of 0.704 ± 0.142 with y-intercept 6.29 ± 2.16 (r2=0.755). Data from the second year of the study yields a slope of 0.710 ± 0.139 with y-intercept 5.44 ± 2.29 (r2=0.722). The p-value between these two subsets is 0.981 and 0.144 for the slope and the y-intercept, respectively. These slopes are not statistically different; in fact they are the same. Conclusion Trained medical students can evaluate non-technical skills as effectively as senior academic faculty in simulated patient scenarios, as suggested through this repeatable correlation over a two year period. Although the average rating per candidate was higher when evaluated by a student actor versus a facilitator, this difference was statistically insignificant as evidenced by comparison of the y-intercept values. Medical students provide an available resource whose participation advances the awareness and competency of their own non-technical skills. The participation of medical students in our model broadens the availability of simulation as a resource in residency candidate evaluation. Disclosures None.