Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematically Characterizing Chemical Profile and Potential Mechanisms of Qingre Lidan Decoction Acting on Cholelithiasis by Integrating UHPLC-QTOF-MS and Network Target Analysis

Qingre Lidan Decoction (QRLDD), a classic precompounded prescription, is widely used as an effective treatment for cholelithiasis clinically. However, its chemical profile and mechanism have not been characterized and elucidated. In the present study, a rapid, sensitive, and reliable ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry method was established for comprehensively identifying the major constituents in QRLDD. Furthermore, a network pharmacology strategy based on the chemical profile was applied to clarify the synergetic mechanism. A total of 72 compounds containing flavonoids, terpenes, phenolic acid, anthraquinones, phenethylalchohol glycosides, and other miscellaneous compounds were identified, respectively. 410 disease genes, 432 compound targets, and 71 related pathways based on cholelithiasis-related and compound-related targets databases as well as related pathways predicted by the Kyoto Encyclopedia of Genes and Genomes database were achieved. Among these pathways and genes, pathway in cancer and MAPK signaling pathway may play an important role in the development of cholelithiasis. EGFR may be a crucial target in the conversion of gallstones to gallbladder carcinoma. Regulation of PRKCB/RAF1/MAP2K1/MAPK1 is associated with cell proliferation and differentiation. Thus, the fingerprint coupled with network pharmacology analysis could contribute to simplifying the complex system and providing directions for further research of QRLDD.
Projection Methods for Dynamical Low-Rank Approximation of High-Dimensional Problems

Abstract We consider dynamical low-rank approximation on the manifold of fixed-rank matrices and tensor trains (also called matrix product states), and analyse projection methods for the time integration of such problems. First, under suitable approximability assumptions, we prove error estimates for the explicit Euler method equipped with quasi-optimal projections to the manifold. Then we discuss the possibilities and difficulties with higher-order explicit methods. In particular, we discuss ways for limiting rank growth in the increments, and robustness with respect to small singular values.
A Method for Automatically Recreating the Horizontal Alignment Geometry of Existing Railways

Periodic recreation of existing railway horizontal alignment geometry is needed for smoothing the deviations arising from train operations. It is important for calibrating track and rebuilding existing railways to ensure safety and comfort. Track calibration repairs the existing distorted track centerline to match the smoothed recreated alignment, which may differ considerably from the originally designed track centerline. Identifying the boundaries of all the geometric elements including tangents, circular curves, and transition curves is the key problem. Existing methods recreate the horizontal alignment semi‐automatically and can only generate a locally optimized solution while considering a few constraints. Based on the principle that the attributions of all the measured points to geometric elements should be consistent with the ranges of recreated geometric elements (i.e., for points‐alignment consistency), a method called swing iterations is proposed to reclassify point placements and identify all the tangents, circular curves, and transition curves simultaneously. In a swing iteration, the boundary of a geometric element segment repeatedly changes from left to right, then from the right to left, and finally stabilizes. Before the swing iterations, preliminary tangents and curves are identified based on the heading gradient (i.e., the rate of change of heading), and are set as initial values for the swing iterations. A genetic algorithm is developed to further refine the entire recreated alignment after the swing iterations. In the above processes, multiple constraints are handled. Applications demonstrate that this method can identify all horizontal geometric elements automatically and generate an optimized recreated alignment geometry for an existing railway while satisfying all the applicable constraints.
Dialogue essai-calcul pour le pilotage en fissuration contrôlée du béton

Abstract: In order to experimentally validate damage and fracture models, a hybrid cracking test on a concrete sample with controlled stress intensity factors (SIFs) is currently developed. Starting from the tests performed by Nooru-Mohamed [1], new experiments are made. The loadings are applied with a hexapod testing machine [2] and the cracking state is analyzed with digital image correlation (DIC) [3]. The objective is to resolve an inverse problem, to determine the boundary conditions required to obtain a desired crack path. The presented numerical results validate the principle of the proposed hybrid test. The “real” cracking state is computed using a damage model [4], while the SIFs are obtained from a linear elastic fracture mechanics (LEFM) analysis. After each propagation step, the new data (assessed by DIC in the real test) are introduced in the LEFM model and the crack is reoriented by changing the loading conditions. To control the real tests two possibilities are proposed: (1) the crack tip position is evaluated using DIC [3] and the SIFs values and the new boundary conditions are obtained from an LEFM computation performed on a geometry having the crack introduced in the mesh; (2) the SIFs are directly obtained by DIC and only the SIF/boundary conditions relationship is computed using LEFM.
Short-Run Effects of Parental Job Loss on Child Health

Recent research suggests that parental job loss has negative effects on children's outcomes, including their academic achievement and long-run educational and labor market outcomes. In this paper we turn our attention to the effects of parental job loss on children's health. We combine health data from 16 waves of the Medical Expenditure Panel Survey, which allows us to use a fixed-effects specification and still have a large sample of parental job displacements. We find that paternal job loss is harmful to children's physical and mental health, particularly among children in low–socioeconomic status families. By contrast, we find that maternal job loss does not have detrimental effects on child health. Increases in public health insurance coverage compensate for close to half of the loss in private coverage that follows parental displacement, and we find no significant changes in medical-care utilization.
Recurrent Spatiotemporal Feature Learning for Action Recognition

Recurrent neural networks (RNNs) like Long Short-term Memory (LSTM) have shown excellent performance for a variety of sequence learning problems on language and speech processing. Previous works leveraging RNNs for action recognition mainly apply LSTM on the top of Convolutional Neural Networks (CNNs), feeding high level semantic feature to RNNs and neglecting to learn spatiotemporal features in video, which makes the models unable to capture complex action patterns and lead to inferior performance. In this work, instead of adopting RNNs as classifiers, we propose to learn spatiotemporal feature in a recurrent way by replacing intermediate convolutional layers in CNNs with recurrent layers to model spatiotemporal information. In order to learn discriminative spatiotemporal features, we extend the bottleneck structure of Residual Network (ResNet) to model the spatiotemporal information in action video. To fully utilize the pretrained CNN model, we also introduce approaches to transfer the weight of original convolutional layer to our proposed model. Our proposed architecture is end-to-end trainable, and of significant flexibility to be adapted in any CNN-based structure. Our model produces the state-of-the-art performance on two standard benchmark for action recognition over RNN-based approaches.
A Survey of Blockchain Applications in Different Domains

Blockchains have received much attention recently since they provide decentralized approaches to the creation and management of value. Many banks, Internet companies, car manufacturers, and even governments worldwide have incorporated or started considering blockchains to improve the security, scalability, and efficiency of their services. In this paper, we survey blockchain applications in different areas. These areas include cryptocurrency, healthcare, advertising, insurance, copyright protection, energy, and societal applications. Our work provides a timely summary for individuals and organizations interested in blockchains. We envision our study to motivate more blockchain applications.
Fully Dynamic 2-Hop Cover Labeling

The 2-hop Cover labeling of a graph is currently the best data structure for answering shortest-path distance queries on large-scale networks, since it combines low query times, affordable space occupancy, and reasonable preprocessing effort. Its main limit resides in not being suited for dynamic networks since, after a network change, (1) queries on the distance can return incorrect values and (2) recomputing the labeling from scratch yields unsustainable time overhead. In this article, we overcome this limit by introducing the first decremental algorithm able to update 2-hop Cover labelings under node/edge removals and edge weight increases. We prove the new algorithm to be (1) correct, i.e., after each update operation queries on the updated labeling return exact values; (2) efficient with respect to the number of nodes that change their distance as a consequence of a graph update; and (3) able to preserve the minimality of the labeling, a desirable property that impacts on both query time and space occupancy. Furthermore, we provide an extensive experimental study to demonstrate the effectiveness of the new method. We consider it both alone and in combination with the unique known incremental approach (Akiba et al. 2014), thus obtaining the first fully dynamic algorithm for updating 2-hop Cover labelings under general graph updates. Our experiments show that the new dynamic algorithms are orders of magnitude faster than the from-scratch approach while at the same time being able to preserve the quality of the labeling in terms of query time and space occupancy, thus allowing one to employ the 2-hop Cover labeling approach in dynamic networks with practical performance.
Learning to Schedule Communication in Multi-agent Reinforcement Learning

Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks. One way to accelerate the coordination effect is to enable multiple agents to communicate with each other in a distributed manner and behave as a group. In this paper, we study a practical scenario when (i) the communication bandwidth is limited and (ii) the agents share the communication medium so that only a restricted number of agents are able to simultaneously use the medium, as in the state-of-the-art wireless networking standards. This calls for a certain form of communication scheduling. In that regard, we propose a multi-agent deep reinforcement learning framework, called SchedNet, in which agents learn how to schedule themselves, how to encode the messages, and how to select actions based on received messages. SchedNet is capable of deciding which agents should be entitled to broadcasting their (encoded) messages, by learning the importance of each agent's partially observed information. We evaluate SchedNet against multiple baselines under two different applications, namely, cooperative communication and navigation, and predator-prey. Our experiments show a non-negligible performance gap between SchedNet and other mechanisms such as the ones without communication and with vanilla scheduling methods, e.g., round robin, ranging from 32% to 43%.
SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Visual localization has become a key enabling component of many place recognition and SLAM systems. Contemporary research has primarily focused on improving accuracy and precision-recall type metrics, with relatively little attention paid to a system's absolute storage scaling characteristics, its flexibility to adapt to available computational resources, and its longevity with respect to easily incorporating newly learned or hand-crafted image descriptors. Most significantly, improvement in one of these aspects typically comes at the cost of others: for example, a snapshot-based system that achieves sub-linear storage cost typically provides no metric pose estimation, or, a highly accurate pose estimation technique is often ossified in adapting to recent advances in appearance-invariant features. In this paper, we present a novel 6-DOF localization system that for the first time simultaneously achieves all the three characteristics: significantly sub-linear storage growth, agnosticism to image descriptors, and customizability to available storage and computational resources. The key features of our method are developed based on a novel adaptation of multiple-label learning, together with effective dimensional reduction and learning techniques that enable simple and efficient optimization. We evaluate our system on several large benchmarking datasets and provide detailed comparisons to state-of-the-art systems. The proposed method demonstrates competitive accuracy with existing pose estimation methods while achieving better sub-linear storage scaling, significantly reduced absolute storage requirements, and faster training and deployment speeds.
TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

We focus on the problem of interlinking Wikipedia tables with fine-grained table relations: equivalent and subPartOf. Such relations allow us to harness semantically related information by accessing related tables or facts therein. Determining the type of a relation is not trivial. Relations are dependent on the schemas, the cell-values, and the semantic overlap of the cell values in tables. We propose TableNet, an approach for interlinking tables with subPartOf and equivalent relations. TableNet consists of two main steps: (i) for any source table we provide an efficient algorithm to find candidate related tables with high coverage, and (ii) a neural based approach that based on the table schemas and data, determines with high accuracy the fine-grained relation. Based on an extensive evaluation with more than 3.2M tables, we show that TableNet retains more than 88% of relevant tables pairs, and assigns table relations with an accuracy of 90%.
MIMO Capacity with Average Total and Per-Antenna Power Constraints

MIMO capacity with a joint total and per-antenna average power constraint is considered in this work. The problem arises when, besides having a limited available power at the transmitter, also the individual antennas cannot radiate power beyond the limits of their corresponding RF chains. Closed-form results are illustrated in specific cases and, in particular, in the unit-rank channel matrix case. Lower-complexity optimization problems are derived for the other cases. Numerical complexity is derived in terms of the number of equivalent real variables for the optimization problem and a general formula is provided depending on the channel matrix rank. Numerical results are included to validate and illustrate the application of the proposed optimization algorithms and also to evaluate the time complexity of its implementation.
Relevance Factor VAE: Learning and Identifying Disentangled Factors

We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality. Our model, dubbed Relevance-Factor-VAE, leverages the total correlation (TC) in the latent space to achieve the disentanglement goal, but also addresses the key issue of existing approaches which cannot distinguish between meaningful and nuisance factors of latent variation, often the source of considerable degradation in disentanglement performance. We tackle this issue by introducing the so-called relevance indicator variables that can be automatically learned from data, together with the VAE parameters. Our model effectively focuses the TC loss onto the relevant factors only by tolerating large prior KL divergences, a desideratum justified by our semi-parametric theoretical analysis. Using a suite of disentanglement metrics, including a newly proposed one, as well as qualitative evidence, we demonstrate that our model outperforms existing methods across several challenging benchmark datasets.
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding has recently attracted much attention due to its effectiveness in dealing with the complex heterogeneous data. Meta path, which connects different object types with various semantic meanings, is widely used by existing HIN embedding works. However, several challenges have not been addressed so far. First, different meta paths convey different semantic meanings, while existing works assume that all nodes share same weights for meta paths and ignore the personalized preferences of different nodes on different meta paths. Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space. rTo tackle the above challenges, we propose aHierarchical Attentive Heterogeneous information network Embedding (HAHE) model to capture the personalized preferences on meta paths and path instances in each semantic space. As path instances are based on a particular meta path, a hierarchical attention mechanism is naturally utilized to model the personalized preference on meta paths and path instances. Extensive experiments on several real-world datasets show that our proposed \model model significantly outperforms the state-of-the-art methods in terms of various data mining tasks.
Some machine learning schemes for high-dimensional nonlinear PDEs

We propose new machine learning schemes for solving high dimensional nonlinear partial differential equations (PDEs). Relying on the classical backward stochastic differential equation (BSDE) representation of PDEs, our algorithms estimate simultaneously the solution and its gradient by deep neural networks. These approximations are performed at each time step from the minimization of loss functions defined recursively by backward induction. The methodology is extended to variational inequalities arising in optimal stopping problems. We analyze the convergence of the deep learning schemes and provide error estimates in terms of the universal approximation of neural networks. Numerical results show that our algorithms give very good results till dimension 50 (and certainly above), for both PDEs and variational inequalities problems. For the PDEs resolution, our results are very similar to those obtained by the recent method in \cite{weinan2017deep} when the latter converges to the right solution or does not diverge. Numerical tests indicate that the proposed methods are not stuck in poor local minima as it can be the case with the algorithm designed in \cite{weinan2017deep}, and no divergence is experienced. The only limitation seems to be due to the inability of the considered deep neural networks to represent a solution with a too complex structure in high dimension.
Accuracy Evaluation of Overlapping and Multi-Resolution Clustering Algorithms on Large Datasets

Performance of clustering algorithms is evaluated with the help of accuracy metrics. There is a great diversity of clustering algorithms, which are key components of many data analysis and exploration systems. However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datasets. In this paper, we first discuss existing metrics, how they satisfy a set of formal constraints, and how they can be applied to specific cases. Then, we propose several optimizations and extensions of these metrics. More specifically, we introduce a new indexing technique to reduce both the runtime and the memory complexity of the Mean F1 score evaluation. Our technique can be applied on large datasets and it is faster on a single CPU than state-of-the-art implementations running on high-performance servers. In addition, we propose several extensions of the discussed metrics to improve their effectiveness and satisfaction to formal constraints without affecting their efficiency. All the metrics discussed in this paper are implemented in C++ and are available for free as open-source packages that can be used either as stand-alone tools or as part of a benchmarking system to compare various clustering algorithms.
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting

We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed version of the policy class under no continuity assumptions, while the other requires standard Lipschitz assumptions. Both bounds exhibit data-dependent "zooming" behavior and, with no tuning, yield improved guarantees for benign problems. We also study adapting to unknown smoothness parameters, establishing a price-of-adaptivity and deriving optimal adaptive algorithms that require no additional information.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in three different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.
External Labeling Techniques: A Taxonomy and Survey

External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state‐of‐the‐art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.