Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems

There hardly exists a general solver that is efficient for scheduling problems due to their diversity and complexity. In this study, we develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together to efficiently deal with complex scheduling problems. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. This offers a novel and general paradigm that combines RL with OR approaches to solving scheduling problems, which leverages the respective strengths of RL and OR: The MDP narrows down the search space of the original problem through an RL method, while the mixed-integer programming process is settled by an OR algorithm. These two stages are performed iteratively and interactively until the termination criterion has been met. Under this idea, two implementation versions of the combination methods of RL and OR are put forward. The agile Earth observation satellite scheduling problem is selected as an example to demonstrate the effectiveness of the proposed scheduling framework and methods. The convergence and generalization capability of the methods are verified by the performance of training scenarios, while the efficiency and accuracy are tested in 50 untrained scenarios. The results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems. In addition, it can be found that RL-based optimization algorithms have stronger scalability than non-learning algorithms. This work reveals the advantage of combining reinforcement learning methods with heuristic methods or mathematical programming methods for solving complex combinatorial optimization problems.
Shapes enhancing the propulsion of multiflagellated helical microswimmers

In this paper we are interested in optimizing the shape of multi-ﬂagellated helical microswimmers. Mimicking the propagation of helical waves along the ﬂagella, they self-propel by rotating their tails. The swimmer’s dynamics is computed using the Boundary Element Method, implemented in the open source Matlab library Gypsilab . We exploit a Bayesian optimization algorithm to maximize the swimmer’s speeds through their shape optimization. Our results show that the optimal tail shapes are helices with large wavelength, such that the shape periodicity is disregarded. Moreover, the best propulsion speed is achieved for elongated heads when the swimmer has one or two ﬂagella. Surprisingly, a round head is obtained when more ﬂagella are considered. Our results indicate that the position and number of ﬂagella modify the propulsion pattern and play a signiﬁcant role in the optimal design of the head. It appears that Bayesian optimization is a promising method for performance improvement in microswimming.
Reservoir Engineering in Coal Seams: Part 2-Observations of Gas Movement in Coal Seams

This paper, the second of two concerning the movement of gas in coal seams, covers observations of seam fluid pressures and flows in mines in northern and central Queensland, Australia. Techniques based primarily on underground measurement rather than measurements from surface boreholes were used to gain information on the seams. The techniques used for in-seam studies are described because they differ substantially from conventional oil and gas surface borehole techniques. The paper demonstrates the importance of cleats and joints in the control of fluid movement and records flow increases consistent with increasing permeability with production.
Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid

The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for the spatio-temporal properties of voltage phasor measurements, by showing how the well-known power systems modeling supports a generative low-pass graph filter model for the state variables, namely the voltage phasors. Using the model we formalize the empirical observation that voltage phasor measurement data lie in a low-dimensional subspace and tie their spatio-temporal structure to generator voltage dynamics. The Grid-GSP generative model is then successfully employed to investigate the problems, pertaining to the grid, of data sampling and interpolation, network inference, detection of anomalies and data compression. Numerical results on a large synthetic grid that mimics the real-grid of the state of Texas, ACTIVSg2000, and on real-world measurements from ISO-New England verify the efficacy of applying Grid-GSP methods to electric grid data.
The m/n=1/1 Instabilities in An EAST NBI Discharge

An analysis of precession fishbone, diamagnetic fishbone and internal kink mode in Tokamak plasmas are presented via solving the fishbone dispersion relation. Applying the dispersion relation to EAST discharge experiment, excitation of precession fishbone due to Neutral Beam Injection is successfully explained. Real frequency and growth rate of diamagnetic fishbone and internal kink mode are calculated.
Physics performance for Dark Matter searches at $\sqrt{s}=$ 3 TeV at CLIC using mono-photons and polarised beams

At colliders, Weakly Interacting Massive Particles (WIMPs) are candidates for dark matter (DM) and can be searched for using as tag a photon from initial state radiation. The potential for detecting DM at the Compact Linear Collider (CLIC) is investigated at √ s = 3 TeV. The sensitivity of the search is estimated by computing the 95% confidence level upper limit cross section as a function of the dark matter mass. Left-handed (right-handed) polarised beams increase (decrease) respectively the Standard Model backgrounds and are essential to characterize the WIMPs properties and control the systematic errors. Using right-handed polarised beams is decreasing significantly the 95% confidence level cross section. Using the ratio of the energy distributions for left-handed and right-handed polarised beams, systematic errors cancel out. Computing the 95% confidence level upper limit cross section using the ratio requires a model assumption to compute the expected number of signal events. Exclusion limits for dark matter are derived using dark matter Simplified Models for two values of the e-e-mediator vertex coupling, a mediator width of 10 GeV and for a fixed value of the mediator-DM-DM coupling. For a mediator mass of 3.5 TeV, the measurement of the differential distribution of the significance as a function of the photon energy for the process → X X allows the discrimination between different dark matter mediators and the measurement of the WIMP mass to nearly half the centre-of-mass energy. For a 1 TeV WIMP, the mass is determined with a 1% accuracy. This work was carried out in the framework of the CLICdp Collaboration © 2021 CERN for the benefit of the CLICdp Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license. jean-jacques.blaising@cern.ch ar X iv :2 10 3. 06 00 6v 1 [ he pex ] 1 0 M ar 2 02 1 2 Dark Matter exclusion limits at 3 TeV
Effectively Counting s-t Simple Paths in Directed Graphs

An important tool in analyzing complex social and information networks is s − t simple path counting, which is known to be # P-complete. In this paper, we study efﬁcient s − t simple path counting in directed graphs. For a given pair of vertices s and t in a directed graph, ﬁrst we propose a pruning technique that can efﬁciently and considerably reduce the search space. Then, we discuss how this technique can be adjusted with exact and approximate algorithms, to improve their efﬁciency. In the end, by performing extensive experiments over several networks from different domains, we show high empirical efﬁciency of our proposed technique. Our algorithm is not a competitor of existing methods, rather, it is a friend that can be used as a fast pre-processing step, before applying any existing algorithm.
Exploiting Asynchronous Priority Scheduling in Parallel Eikonal Solvers

Numerical solutions to the Eikonal equation are computed using variants of the fast marching method, the fast sweeping method, and the fast iterative method. In this paper, we provide a unified view of these algorithms that highlights their similarities and suggests a wider class of Eikonal solvers. We then use this framework to justify applying concurrent priority scheduling techniques to Eikonal solvers. We demonstrate that doing so results in good parallel performance for a problem from seismology. We explain why existing Eikonal solvers may produce different results despite using the same differencing scheme and demonstrate techniques to address these discrepancies. These techniques allow us to obtain deterministic output from our asynchronous fine-grained parallel Eikonal solver.
Sim2Real 3D Object Classification using Spherical Kernel Point Convolution and a Deep Center Voting Scheme

While object semantic understanding is essential for most service robotic tasks, 3D object classification is still an open problem. Learning from artificial 3D models alleviates the cost of annotation necessary to approach this problem, but most methods still struggle with the differences existing between artificial and real 3D data. We conjecture that the cause of those issue is the fact that many methods learn directly from point coordinates, instead of the shape, as the former is hard to center and to scale under variable occlusions reliably. We introduce spherical kernel point convolutions that directly exploit the object surface, represented as a graph, and a voting scheme to limit the impact of poor segmentation on the classification results. Our proposed approach improves upon state-of-the-art methods by up to 36% when transferring from artificial objects to real objects.
Clustered star formation toward Berkeley 87/ON2

Context. Disentangling line-of-sight alignments of young stellar populations is crucial for observational studies of star-forming complexes. This task is particularly problematic in a Cygnus-X subregion where several components, located at different distances, are overlapped: the Berkeley 87 young massive cluster, the poorly-known [DB2001] Cl05 embedded cluster, and the ON2 star-forming complex, in turn composed of several Hii regions. Aims. To provide a methodology for building an exhaustive census of young objects that can consistently deal with large differences in both extinction and distance. Methods. OMEGA2000 near-infrared observations of the Berkeley 87 / ON2 field are merged with archival data from Gaia, Chandra, Spitzer, and Herschel, as well as cross-identifications from the literature. To address the incompleteness effects and selection biases that arise from the line-of-sight overlap, we adapt existing methods for extinction estimation and young object classification, and we define the intrinsic reddening index, Rint, a new tool to separate intrinsically red sources from those whose infrared color excess is caused by extinction. We also introduce a new method to find young stellar objects based on Rint. Results. We find 571 objects whose classification is related to recent or ongoing star formation. Together with other point sources with individual estimates of distance or extinction, we compile a catalog of 3005 objects to be used for further membership work. A new distance for Berkeley 87, (1673 ± 17) pc, is estimated as a median of 13 spectroscopic members with accurate Gaia EDR3 parallaxes. Conclusions. The flexibility of our approach, especially regarding the Rint definition, allows to overcome photometric biases caused by large extinction and distance variations, in order to obtain homogeneous catalogs of young sources. The multi-wavelength census that results from applying our methods to the Berkeley 87 / ON2 field will serve as a basis for disentangling the overlapped populations.
Co-design of Control and Scheduling in Networked Systems under Denial-of-Service attacks

We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems. A sensor takes measurements of the system output and forwards its dynamic state estimates to a remote controller over a packet-dropping link. The controller determines the optimal control law for the process using the estimates it receives. An attacker aims at degrading the control performance by increasing the packet-dropout rate with a DoS attack towards the sensor-controller channel. Assume both the controller and the attacker are rational in a game-theoretic sense. We establish a partially observable stochastic game to derive the optimal joint design of scheduling and control. Using dynamic programming we prove that the control and scheduling policies can be designed separately without sacrificing optimality, making the problem equivalent to a complete information game. We employ Nash Q-learning to solve the problem and prove that the solution is guaranteed to constitute an $\epsilon$-Nash equilibrium. Numerical examples are provided to illustrate the tradeoffs between control performance and communication cost.
Kibble-Zurek mechanism in polariton graphene

We study the formation of topological defects (quantum vortices) in a 2D polariton condensate at the Γ point of a honeycomb lattice via the Kibble-Zurek mechanism. We demonstrate that the vortices can be pinned to the lattice, which prevents their recombination and allows their observation and counting in cw experiments. A scaling exponent of 0.95 ± 0.05 is observed, in agreement with the mean-field predictions for a 2D system with lattice-induced linear dispersion.
Cybersecurity in Robotics: Challenges, Quantitative Modeling, and Practice

Robotics is becoming more and more ubiquitous, but the pressure to bring systems to market occasionally goes at the cost of neglecting security mechanisms during the development, deployment or while in production. As a result, contemporary robotic systems are vulnerable to diverse attack patterns, and an a posteriori hardening is at least challenging, if not impossible at all. This book aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-benefit tradeoff that can otherwise be an inhibitor for the fast development of affordable systems. We advocate quantitative methods of security management and -design, covering vulnerability scoring systems tailored to robotic systems, and accounting for the highly distributed nature of robots as an interplay of potentially very many components. A powerful quantitative approach to model-based security is offered by game theory, providing a rich spectrum of techniques to optimize security against various kinds of attacks. Such a multiperspective view on security is necessary to address the heterogeneity and complexity of robotic systems. This book is intended as an accessible starter for the theoretician and practitioner working in the field.
Towards Connecting Use Cases and Methods in Interpretable Machine Learning

Despite increasing interest in the ﬁeld of Interpretable Machine Learning (IML), a signiﬁcant gap persists between the technical objectives targeted by researchers’ methods and the high-level goals of consumers’ use cases . In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workﬂow to better enable researchers and consumers to work together to discover what types of methods are useful for what use cases. Eventually, by building on the results generated from this workﬂow, a more complete version of the taxonomy will increasingly allow consumers to ﬁnd relevant methods for their target use cases and researchers to identify applicable use cases for their proposed methods.
Hybrid Model with Time Modeling for Sequential Recommender Systems

Deep learning based methods have been used successfully in recommender system problems. Approaches using recurrent neural networks, transformers, and attention mechanisms are useful to model users' long- and short-term preferences in sequential interactions. To explore different session-based recommendation solutions, Booking.com recently organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. This study presents our approach to this challenge. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Further, we proposed some changes to Neural Attentive Recommendation Machine (NARM), adapted its architecture for the challenge objective, and implemented training approaches that can be used in any session-based model to improve accuracy. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.
BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

Collaborative Filtering (CF) based recommendation methods have been widely studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning tries to learn a common low dimensional space for the representations of users and items. In this case, a user and item match better if they have higher similarity in that common space. Matching function learning tries to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the representation learning resorts to applying a dot product which has limited expressiveness on the latent features of users and items, while the matching function learning has weakness in capturing low-rank relations. To overcome such flaws, we propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet), which has the strengths of the two types of methods. In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network. Furthermore, a balance module is designed to alleviate the over-fitting issue in DNNs. Extensive experiments on eight real-world datasets demonstrate the effectiveness of the proposed model.
Radiation grafting of acrylic acid onto poly(ethylene terephthalate) fabric

Radiation grafting of acrylic acid onto poly(ethylene terephthalate) (PET) knittings was investigated by preirradiation technique. The influence of the grafting conditions, such as reaction time, ferrous sulfate concentration, preirradiation dose, organic additives, monomer concentration, and reaction temperature on the degree of grafting was investigated. Precise control over the degree of grafting was achieved by proper selection of the reaction conditions. The organic solvents such as methanol, acetone, isopropanol, THF, and MEK when used as sole medium do not allow the grafting to proceed, probably due to the inhibitory action. However, reasonable grafting proceeded when a mixture of water and organic solvent was used. Attenuated total reflectance-FTIR spectroscopy analysis of the grafted knittings confirmed the existence of carboxylic acid groups in the knittings. The surface morphology, as studied by scanning electron microscopy (SEM), was significantly influenced by the addition of organic solvents in the reaction medium. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Promoting Learning for Students Experiencing Adversity and Trauma

This study explored elementary teachers’ strategies for promoting learning for their students experiencing adversity and trauma. Recent data suggest that nearly half of all elementary school students in the United States have experienced at least one potentially traumatic event, which may have significant and negative consequences in the classroom. However, teachers are not routinely taught trauma-informed practices. Qualitative interviews were conducted with a purposeful sample of 10 elementary teachers. Thematic analysis revealed six themes: teachers prioritize relationships with and between students; allocate time to teach self-regulation and social skills; provide and advocate for academic, social, and emotional supports; practice cultural humility and responsiveness; strive to ally with parents; and engage in ongoing learning and reflection. Teachers also described tensions they experience while engaging in this work. Implications for educator professional development and policy are discussed.
Direct and Indirect Effects of Nesting Site Characteristics for a Cliff-Nesting Raptor in Western Alaska

Abstract. Habitat suitability for breeding birds is defined at scales ranging from the landscape to individual nesting sites. Nesting site characteristics that govern exposure to inclement weather may affect breeding success, although attempts to understand this effect for Arctic breeding raptors have yielded ambiguous results. Further, breeding adults incur substantial costs from incubating eggs and brooding nestlings, and it is possible that greater site exposure results in increased nest attendance rates, increasing their cost of breeding. We quantified nesting site characteristics of Gyrfalcons (Falco rusticolus) and assessed how breeding parameters and nest attendance rates varied by protective site qualities on Alaska's Seward Peninsula, 2014–2019. The degree of physical exposure in the horizontal plane correlated negatively with the probability of hatching and fledging (provided hatch occurred), as well as overall productivity. The negative effect of horizontal exposure on the probability of fledging and productivity was greatest at nesting sites that were also more exposed in the vertical plane, although this interaction did not affect the probability of hatching. Early breeding pairs had higher productivity and tended to select more protected nesting sites. Additionally, nest attendance rates were higher in more horizontally exposed nesting sites, particularly when nestlings were approximately 2 wk old. The increased nest attendance and concurrent decreased productivity associated with greater nesting site exposure demonstrated that nesting site characteristics can have direct and indirect effects on Arctic breeding raptors and also highlight the importance of small-scale variables when evaluating habitat suitability.