Miracle-dz/UniFAR-FTUs huggingface dataset:
Preconditioning Richards Equations: spectral analysis and parallel solution at very large scale

We consider here a cell-centered finite difference approximation of the Richards equation in three dimensions, averaging for interface values the hydraulic conductivity K = K(p), a highly nonlinear function, by arithmetic, upstream and harmonic means. The nonlinearities in the equation can lead to changes in soil conductivity over several orders of magnitude and discretizations with respect to space variables often produce stiff systems of differential equations. A fully implicit time discretization is provided by backward Euler one-step formula; the resulting nonlinear algebraic system is solved by an inexact Newton Armijo-Goldstein algorithm, requiring the solution of a sequence of linear systems involving Jacobian matrices. We prove some new results concerning the distribution of the Jacobians eigenvalues and the explicit expression of their entries. Moreover, we explore some connections between the saturation of the soil and the ill conditioning of the Jacobians. The information on eigenvalues justifies the effectiveness of some preconditioner approaches which are widely used in the solution of Richards equation. We propose a new software framework to experiment with scalable and robust preconditioners suitable for efficient parallel simulations at very large scales. Performance results on a literature test case show that our framework is very promising in the advance towards realistic simulations at extreme scale. Preprint submitted to Elsevier December 10, 2021 ar X iv :2 11 2. 05 05 1v 1 [ m at h. N A ] 9 D ec 2 02 1
KGE-CL: Contrastive Learning of Knowledge Graph Embeddings

Learning the embeddings of knowledge graphs is vital in ar-tiﬁcial intelligence, and can beneﬁt various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding. However, most previous knowledge graph embedding methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efﬁcient contrastive learning framework for knowledge graph embeddings, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the expressiveness of knowledge graph embeddings. We evaluate our proposed method on three standard knowledge graph benchmarks. It is noteworthy that our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.
Revisting the limits of atmospheric temperature retrieval from cosmic-ray measurements

A priori, cosmic-ray measurements offer a unique capability to determine the vertical profile of atmospheric temperatures directly from ground. However, despite the increased understanding of the impact of the atmosphere on cosmic-ray rates, attempts to explore the technological potential of the latter for atmospheric physics remain very limited. In this paper we examine the intrinsic limits of the process of cosmic-ray data inversion for atmospheric temperature retrieval, by combining a detection station at ground with another one placed at an optimal depth, and making full use of the angular information. With that aim, the temperature-induced variations in c. r. rates have been simulated resorting to the theoretical temperature coefficients WT (h, θ, Eth) and the temperature profiles obtained from the ERA5 atmospheric reanalysis. Muon absorption and Poisson statistics have been included to increase realism. The resulting c.r. sample has been used as input for the inverse problem and the obtained temperatures compared to the input temperature data. Relative to early simulation works, performed without using angular information and relying on underground temperature coefficients from a sub-optimal depth, our analysis shows a strong improvement in temperature predictability for all atmospheric layers up to 50 hPa, nearing a factor 2 error reduction. Furthermore, the temperature predictability on 6 h-intervals stays well within the range 0.8-2.2 K. Most remarkably, we show that it can be achieved with small-area m-scale muon hodoscopes, amenable nowadays to a large variety of technologies. For mid-latitude locations, the optimal depth of the underground station is around 20 m. Plain Language Summary Cosmic rays (c.r.) are a form of natural radiation that comes from outer space and traverses the atmosphere. Analogously to X-ray radiation, we can extract information from the object they pass through, the atmosphere in this case. Cosmic radiation can be measured at the Earth’s surface using sophisticated instruments. In our work we examine in detail the possibility of retrieving the vertical profile of temperatures from c.r. measurements, by using two detection stations, one placed at the surface and another one at an optimal depth. We make full use of a feature which is characteristic of modern c.r. detectors, the angular information. This refers to the capability of measuring c.r. from different directions. To analyze the limits of the suggested approach, we estimate temperatures from simulated c.r. data that would be measured under realistic atmospheric conditions and compare them with the original ones. Relative to early simulation works, our method estimates temperatures with a greater accuracy at higher temporal resolutions and for atmospheric layers up to 20 km. Most remarkably, we show that this performance can be achieved with small and affordable detectors, bringing the possibility of complementing satellite-borne temperature retrieval with a technology cheaper to assemble and maintain.
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey

Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates, if the policy's effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue, and that proposes alternative estimators robust to heterogeneous effects. We use those alternative estimators to revisit Wolfers (2006).
Variational Regularization in Inverse Problems and Machine Learning

This paper discusses basic results and recent developments on variational regularization methods, as developed for inverse problems. In a typical setup we review basic properties needed to obtain a convergent regularization scheme and further discuss the derivation of quantitative estimates respectively needed ingredients such as Bregman distances for convex functionals. In addition to the approach developed for inverse problems we will also discuss variational regularization in machine learning and work out some connections to the classical regularization theory. In particular we will discuss a reinterpretation of machine learning problems in the framework of regularization theory and a reinterpretation of variational methods for inverse problems in the framework of risk minimization. Moreover, we establish some previously unknown connections between error estimates in Bregman distances and generalization errors.
Workload analysis of a two-queue fluid polling model

Abstract In this paper, we analyze a two-queue random time-limited Markov-modulated polling model. In the first part of the paper, we investigate the fluid version: fluid arrives at the two queues as two independent flows with deterministic rate. There is a single server that serves both queues at constant speeds. The server spends an exponentially distributed amount of time in each queue. After the completion of such a visit time to one queue, the server instantly switches to the other queue, i.e., there is no switch-over time. For this model, we first derive the Laplace–Stieltjes transform (LST) of the stationary marginal fluid content/workload at each queue. Subsequently, we derive a functional equation for the LST of the two-dimensional workload distribution that leads to a Riemann–Hilbert boundary value problem (BVP). After taking a heavy-traffic limit, and restricting ourselves to the symmetric case, the BVP simplifies and can be solved explicitly. In the second part of the paper, allowing for more general (Lévy) input processes and server switching policies, we investigate the transient process limit of the joint workload in heavy traffic. Again solving a BVP, we determine the stationary distribution of the limiting process. We show that, in the symmetric case, this distribution coincides with our earlier solution of the BVP, implying that in this case the two limits (stationarity and heavy traffic) commute.
Mutual Adversarial Training: Learning Together is Better Than Going Alone

Recent studies have shown that robustness to adversarial attacks can be transferred across deep neural networks. In other words, we can make a weak model more robust with the help of a strong teacher model. In this paper, we ask if models can “learn together” and “teach each other” to achieve better robustness instead of learning from a static teacher. We study how interactions among models enhance robustness via knowledge distillation. We propose mutual adversarial training (MAT), in which multiple models are trained together and share the knowledge of adversarial examples to achieve improved robustness. MAT allows robust models to explore a larger space of adversarial samples and find more robust feature spaces and decision boundaries. Through extensive experiments on the CIFAR-10, CIFAR-100, and mini-ImageNet datasets, we demonstrate that MAT can effectively improve model robustness and outperform state-of-the-art methods under white-box attacks. In addition, we show that MAT can also mitigate the robustness trade-off among different perturbation types. Specially, we train specialist models that learn to defend a specific perturbation type and a generalist model that learns to defend multiple perturbation types by learning from the specialists, which brings as much as 13.4% accuracy gain to AT baselines against the union of <inline-formula> <tex-math notation="LaTeX">$l_{\infty} $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$l_{2}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> attacks. Our results show the effectiveness of the proposed method and demonstrate that collaborative learning is an effective strategy for designing robust models.
DESIGN AND FABRICATION OF A ROLLER IMPRINTING DEVICE FOR MICROFLUIDIC DEVICE MANUFACTURING

Microfluidic devices are gaining popularity in a variety of applications, ranging from molecular biology to bio-defense. However, the widespread adoption of this technology is constrained by the lack of efficient and cost-effective manufacturing processes. This paper focuses on the roller imprinting process, which is being developed to rapidly and inexpensively fabricate micro-fluidic devices. In this process, a cylindrical roll with raised features on its surface creates imprints by rolling over a fixed workpiece substrate and mechanically deforming it. Roller imprinting aims to replace processes that were developed for laboratory scale prototyping which tend to not be scalable and have high equipment requirements and overheads. We discuss the limitations of PDMS soft lithography in large-scale manufacture of microfluidic devices. We also discuss the design, fabrication, and testing of a simple roller imprinting device. This imprinter has been developed based on the principles of precision machine design and is implemented using a three-axis machine tool for actuation and position measurement. A framework for the micromachining of precision imprint rolls is also presented.
How Not to Protect Your IP – An Industry-Wide Break of IEEE 1735 Implementations

Modern hardware systems are composed of a variety of third-party Intellectual Property (IP) cores to implement their overall functionality. Since hardware design is a globalized process involving various (untrusted) stakeholders, a secure management of the valuable IP between authors and users is inevitable to protect them from unauthorized access and modification. To this end, the widely adopted IEEE standard 1735-2014 was created to ensure confidentiality and integrity. In this paper, we outline structural weaknesses in IEEE 1735 that cannot be fixed with cryptographic solutions (given the contemporary hardware design process) and thus render the standard inherently insecure. We practically demonstrate the weaknesses by recovering the private keys of IEEE 1735 implementations from major Electronic Design Automation (EDA) tool vendors, namely Intel, Xilinx, Cadence, Siemens, Microsemi, and Lattice, while results on a seventh case study are withheld. As a consequence, we can decrypt, modify, and re-encrypt all allegedly protected IP cores designed for the respective tools, thus leading to an industry-wide break. As part of this analysis, we are the first to publicly disclose three RSA-based white-box schemes that are used in real-world products and present cryptanalytical attacks for all of them, finally resulting in key recovery.
A simulation based methodology for analyzing network-based intelligent vehicle control systems

There is a need for a generic computer network architecture that will provide flexibility in design and implementation of intelligent vehicle control systems (IVCS) in future generation automobiles. The paper presents the architecture and performance analysis of a network-based IVCS. The most interesting performance parameters of the IVCS network are statistics of induced delays and probability of data loss. Dynamic performance and stability robustness of the IVCS that is served by the network needs to be evaluated in the presence of uncertainties and disturbances in both vehicle dynamics and network operations. Results, derived from both off-line and real-time simulation experiments under different operational scenarios, are analyzed, and future research is recommended on this basis. <<ETX>>
TISSUE ENGINEERING FOR NERVE REPAIR

Nerve regeneration is a complex biological phenomenon. Once the nervous system is impaired, its recovery is difficult and malfunctions in other parts of the body may occur because mature neurons don't undergo cell division. To increase the prospects of axonal regeneration and functional recovery, researches have focused on designing “nerve guidance channels” or “nerve conduits”. For developing tissue engineered nerve conduits, four components come to mind, including a scaffold for axonal proliferation, supporting cells such as Schwann cells, growth factors, and extracelluar matrix. This article reviews the nervous system physiology, the factors that are critical for nerve repair, and the advanced technologies that are explored to fabricate nerve conduits. Furthermore, we also introduce a new method we developed to create longitudinally oriented channels within biodegradable polymers, Chitosan and PLGA, using a combined lyophilizing and wire-heating process. This innovative method using Ni-Cr wires as mandrels to create nerve guidance channels. The process is easy, straightforward, highly reproducible, and could easily be tailored to other polymer and solvent systems. These scaffolds could be useful for guided regeneration after transection injury in either the peripheral nerve or spinal cord.
A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction

Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these meth-ods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. To overcome this deficiency, we propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions. Concretely, we first extract all possible subject-object pairs from two paralleled directions. These two extraction directions are connected by a shared encoder component, thus the extraction features from one direction can flow to another direction and vice versa. By this way, the extractions of two directions can boost and complement each other. Next, we assign all possible relations for each entity pair by a biaffine model. During training, we observe that the share structure will lead to a convergence rate inconsistency issue which is harmful to performance. So we propose a share-aware learning mechanism to address it. We evaluate the proposed model on multiple benchmark datasets. Extensive experimental results show that the proposed model is very effective and it achieves state-of-the-art results on all of these datasets. Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods. The source code of our work is available at: https://github.com/neukg/BiRTE.
Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed. Thirty-two papers were identified, analyzed and summarized on the application of AI and ML domain and techniques which are most often used. Findings show that The current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. For collection management in libraries, several Ml techniques like logistic regression, KNN, AdaBoost have been widely used for Metadata generation, resource discovery; and Book acquisition. Whereas for circulation (book recommendation, user rating, bibliographic data etc.) recommender system, SVM, association rule have been utilized. Library in-house activities like; cataloguing, classification, indexing, document analysis, text recognition etc., have been supported by both AI and ML technologies. Some advanced AI and ML techniques like pattern recognition and MAS are also being used to ensure library security; user identification; book title recognition; RFID management, and other administration activities. Deep learning, neural network algorithms, convolutional neural networks have also been proved as powerful tools for scholarship, collections discovery, search and analysis. Besides, an artificially intelligent conversational agent or chatbot works as a virtual reference librarian. It enhances face-to-face human interaction for library web site tour guides, automated virtual reference assistants, readers’ advisory-librarians, and virtual story-tellers. This study could help in the development of new ideas and models or tools to support and enhance the existing service ecologies of libraries. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.
Design and Implementation of Real-Time Localization System (RTLS) Based on UWB and TDoA Algorithm

Nowadays, accurate localization plays an essential role in many fields, such as target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and the high cost. So, the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive ultra wide-band (UWB)-based real-time localization system (RTLS) is presented in this paper. We introduce the architecture of a real-time localization system, then propose a new wireless clock synchronization (WCS) scheme, and finally discuss the time difference of arrival (TDoA) algorithm. We define the time-base selection strategy for the TDoA algorithm, and we analyze the relationship between anchor deployment and positioning accuracy. The extended Kalman filter (EKF) method is presented for non-linear dynamic localization estimation, and it performs well in terms of stability and accuracy on moving targets.
Stochastic Optimization Model and O(N2) Solution Algorithm for Highway Investment Decision Making under Budget Uncertainty

One of the key tasks in highway investment decision making is to conduct project selection. The existing optimization models for project selection concerning an entire highway system do not address the issue of budget uncertainty, limiting their ability to produce truly optimal results. This paper presents a stochastic optimization model that explicitly addresses budget uncertainty in highway investment decision making. The model is formulated as the stochastic multichoice multidimensional Knapsack problem with Ω -stage budget recourses that facilitates the selection of a subset of candidate highway projects, proposed to address the needs of a highway system across a multiyear project selection and programming period, in the most cost-effective manner. An efficient solution algorithm with the computational complexity of O ( N2 ) is developed for the proposed model. A computational study is performed to compare the investment decision outcomes by applying the proposed model with and without budget uncertai.
Full-Depth Reclamation Process

Many roads and streets fail structurally after having performed well for several years. The standby treatment of a hot-mix overlay does not address the real problem with most of these roads—inadequate roadbase strength. Public agencies throughout the United States are making fulldepth reclamation (FDR) one of the most popular pavement rehabilitation methods. The FDR process allows complete reconstruction using 100 percent of the existing pavement materials, while correcting grade, cross slope, and underlying pavement problems. In FDR a new base is produced by pulverizing the existing asphalt pavement and mixing it with some of the underlying granular materials. Additional structural strength can be achieved by incorporating new crushed aggregates, asphalt emulsion, foamed asphalt, and chemical stabilizers. Laboratory testing of the reclaimed asphalt pavement (RAP), the subgrade soils, and a mixture of the two will determine whether an additive is needed. The RAP testing should include an extraction for asphalt content and an aggregate sieve analysis. For soils, the minimum testing would include a sieve analysis, sand equivalent, liquid limit, plastic limit, and plasticity index. Guidelines for selecting stabilizers on the basis of these test results are included. Additional strength tests on the stabilized materials can be used in structural design of the pavement. A thorough project evaluation is essential to ensure success. This includes a pavement condition survey, a traffic study, and structural design, in addition to the testing. Proper construction procedures and quality control plus an experienced contractor are necessary.