Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mean Field Limit of Stochastic Differential Equation Systems Modeling Grid Cells

Several differential equation models have been proposed to explain the formation of patterns characteristic of the grid cell network. Understanding the robustness of these patterns with respect to noise is one of the key open questions in computational neuroscience. In the present work, we analyze a family of stochastic differential systems modelling grid cell networks. Furthermore, the well-posedness of the associated McKean--Vlasov and Fokker--Planck equations, describing the average behavior of the networks, is established. Finally, we rigorously prove the mean field limit of these systems and provide a sharp rate of convergence for their empirical measures.
Spontaneous anomalous Hall effect arising from antiparallel magnetic order in a semiconductor

Recently, a distinct mechanism of spontaneous anomalous Hall effect, the so-called crystal anomalous Hall effect, was predicted to arise from antiparallel magnetic moments surrounded by cages of nonmagnetic atoms [Šmejkal et al., Sci. Adv.6, eaaz8809(2020)]. Here, we observe this spontaneous anomalous Hall effect in thin films of semiconducting MnTe with well-established room-temperature collinear antiparallel ordering. We show that this Hall signal is consistent with the measured experimental magnetic easy axis and with the calculated unconventional time-reversal symmetry broken band structure. The anomalous Hall effect (AHE) is one of the most prominent phenomena of relativistic spintronics. Usually, it refers to voltage generated transverse to electric current and the pseudovector related to magnetic order[1, 2]. Unlike the ordinary Hall effect, which is proportional to the external magnetic field, the AHE is determined by the magnetic order and, therefore, can be saturated. Consequently it can have finite value at zero magnetic field and exhibit hysteresis[1]. This spontaneous character of the AHE makes it also distinct from the ordinary Hall effect. From a symmetry point of view, the AHE relies on breaking of time reversal symmetry (TRS) in the band structure. Therefore, until recently, it was commonly believed that the AHE could be found exclusively in ferromagnets[2]. It was however predicted, based on symmetry considerations[3, 4], that certain non-collinear antiferromagnets could also exhibit AHE and estimated to be measurable based on calculations of momentum space Berry curvature contributions to the effect. This was experimentally confirmed [5, 6], triggering a fast development in the field when effects such as anomalous Nernst effect [7, 8], magneto-optical Kerr effect [9] or magnetic spin Hall effect [10] were observed in non-collinear antiferromagnets. The anomalous Hall effect was also observed in other metallic antiferromagnets with multiple magnetic sublattices (≥ 3) and its relation to magnetic multipoles was studied [11–15]. Recently, a distinct mechanism of spontaneous AHE was predicted to arise in compensated antiparallel ordered collinear magnets[16]. The mechanism relies on TRS broken band structure due to the combined effect of antiparallel magnetic sublattices and nonmagnetic atoms. The two antiparallel magnetic sublattices surrounded by nonmagnetic atoms cages are then related by crystal-rotation symmetries[17, 18]. These compensated magnets were delimited via nonrelativsitic spin symmetries as separate magnetic phase, so
Infinite Horizon Optimal Control Problems for a Class of Semilinear Parabolic Equations

Infinite horizon open loop optimal control problems for semilinear parabolic equations are investigated. The controls are subject to a cost-functional which promotes sparsity in time. The focus is put on deriving first order optimality conditions. This is achieved without relying on a well-defined control-to-state mapping in a neighborhood of minimizers. The technique of proof is based on the approximation of the original problem by a family of finite horizon problems. The optimality conditions allow to deduce sparsity properties of the optimal controls in time. AMS classification: 35K58, 49J20, 49J52, 49K20.
Sequential Break-Point Detection in Stationary Time Series: An Application to Monitoring Economic Indicators

Monitoring economic conditions and financial stability with an early warning system serves as a prevention mechanism for unexpected economic events. In this paper, we investigate the statistical performance of sequential break-point detectors for stationary time series regression models with extensive simulation experiments. We employ an online sequential scheme for monitoring economic indicators from the European as well as the American financial markets that span the period during the 2008 financial crisis. Our results show that the performance of these tests applied to stationary time series regressions such as the AR(1) as well as the AR(1)-GARCH(1,1) depend on the severity of the break as well as the location of the breakpoint within the out-of-sample period. Consequently, our study provides some useful insights to practitioners for sequential break-point detection in economic and financial conditions.
Attention based Broadly Self-guided Network for Low light Image Enhancement

During the past years,deep convolutional neural networks have achieved impressive success in low-light Image Enhancement.Existing deep learning methods mostly enhance the ability of feature extraction by stacking network structures and deepening the depth of the network.which causes more runtime cost on single image.In order to reduce inference time while fully extracting local features and global features.Inspired by SGN,we propose a Attention based Broadly self-guided network (ABSGN) for real world low-light image Enhancement.such a broadly strategy is able to handle the noise at different exposures.The proposed network is validated by many mainstream benchmark.Additional experimental results show that the proposed network outperforms most of state-of-the-art low-light image Enhancement solutions.
Large-scale GPU-based network analysis of the human T-cell receptor repertoire

Understanding the structure of the human T-cell receptor repertoire is a crucial precondition to understand the ability of the immune system to recognize and respond to antigens. T cells are often compared via the complementarity determining region 3 (CDR3) of their respective T-cell receptor β chains. Nevertheless, previous studies often simply compared if CDR3β sequences were equal, while network theory studies were usually limited to several ten thousand sequences due to the high computational effort of constructing the network. To overcome that hurdle, we introduce the GPU-based algorithm TCR-NET to construct large-scale CDR3β similarity networks using model-generated and empirical sequence data with up to 800,000 CDR3β sequences on a normal computer for the first time. Using network analysis methods we study the structural properties of these networks and conclude that (i) the fraction of public TCRs depends on the size of the TCR repertoire, along with the exact (not unified) definition of “public” sequences, (ii) the TCR network is assortative with the average neighbor degree being proportional to the squareroot of the degree of a node and (iii) the repertoire is robust against losses of TCRs. Moreover, we analyze the networks of antigen-specific TCRs for different antigen families and find differing clustering coefficients and assortativities. TCR-NET offers better access to assess large-scale TCR repertoire networks, opening the possibility to quantify their structure and quantitatively distinguish their ability to react to antigens, which we anticipate to become a useful tool in a time of increasingly large amounts of repertoire sequencing data becoming available.
Centroid-UNet: Detecting Centroids in Aerial Images

ABSTRACT: In many applications of aerial/satellite image analysis (remote sensing), the generation of exact shapes of objects is a cumbersome task. In most remote sensing applications such as counting objects requires only location estimation of objects. Hence, locating object centroids in aerial/satellite images is an easy solution for tasks where the object’s exact shape is not necessary. Thus, this study focuses on assessing the feasibility of using deep neural networks for locating object centroids in satellite images. Name of our model is Centroid-UNet. The Centroid-UNet model is based on classic U-Net semantic segmentation architecture. We modified and adapted the U-Net semantic segmentation architecture into a centroid detection model preserving the simplicity of the original model. Furthermore, we have tested and evaluated our model with two case studies involving aerial/satellite images. Those two case studies are building centroid detection case study and coconut tree centroid detection case study. Our evaluation results have reached comparably good accuracy compared to other methods, and also offer simplicity. The code and models developed under this study are also available in the Centroid-UNet GitHub repository: https://github.com/gicait/centroid-unet.
Competitive Car Racing with Multiple Vehicles using a Parallelized Optimization with Safety Guarantee

—This paper presents a novel planning and control strategy for competing with multiple vehicles in a car racing scenario. The proposed racing strategy switches between two modes. When there are no surrounding vehicles, a learning-based model predictive control (MPC) trajectory planner is used to guarantee that the ego vehicle achieves better lap timing. When the ego vehicle is competing with other surrounding vehicles to overtake, an optimization-based planner generates multiple dynamically-feasible trajectories through parallel computation. Each trajectory is optimized under a MPC formulation with different homotopic Bezier-curve reference paths lying laterally between surrounding vehicles. The time-optimal trajectory among these different homotopic trajectories is selected and a low-level MPC controller with control barrier function constraints for obstacle avoidance is used to guarantee system safety-critical performance. The proposed algorithm has the capability to generate collision-free trajectories and track them while enhancing the lap timing performance with steady low computational complexity, outperforming existing approaches in both timing and performance for a car racing environment. To demonstrate the performance of our racing strategy, we simulate with multiple randomly generated moving vehicles on the track and test the ego vehicle’s overtake maneuvers.
UPV at TREC Health Misinformation Track 2021 Ranking with SBERT and Quality Estimators

Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.
Linear and nonlinear excitation of TAE modes by external electromagnetic perturbations using ORB5

The excitation of toroidicity-induced Alfvén eigenmodes (TAEs) using prescribed external electromagnetic perturbations (hereafter ‘antenna’) acting on a confined toroidal plasma, as well as its nonlinear couplings to other modes in the system, is studied. The antenna is described by an electrostatic potential resembling the target TAE mode structure along with its corresponding parallel electromagnetic potential computed from Ohm’s law. Numerically stable long-time linear simulations are achieved by integrating the antenna within the framework of a mixed representation and pullback scheme (Mishchenko et al 2019 Comput. Phys. Commun. 238 194). By decomposing the plasma electromagnetic potential into symplectic and Hamiltonian parts and using Ohm’s law, the destabilizing contribution of the potential gradient parallel to the magnetic field is cancelled in the equations of motion. Besides evaluating the frequencies and the growth/damping rates of excited modes compared to referenced TAEs, we study the interaction of antenna-driven modes with fast particles and indicate their margins of instability. Furthermore, we show the first nonlinear simulations in the presence of a TAE-like antenna exciting other TAE modes, as well as global Alfvén eigenmodes with different toroidal wave numbers from that of the antenna.
On the Stability, Economic Efficiency and Incentive Compatibility of Electricity Market Dynamics

This paper focuses on the operation of an electricity market that accounts for participants that bid at a sub-minute timescale. To that end, we model the market-clearing process as a dynamical system, called market dynamics, which is temporally coupled with the grid frequency dynamics and is thus required to guarantee system-wide stability while meeting the system operational constraints. We characterize participants as pricetakers who rationally update their bids to maximize their utility in response to real-time schedules of prices and dispatch. For two common bidding mechanisms, based on quantity and price, we identify a notion of alignment between participants’ behavior and planners’ goals that leads to a saddle-based design of the market that guarantees convergence to a point meeting all operational constraints. We further explore cases where this alignment property does not hold and observe that misaligned participants’ bidding can destabilize the closed-loop system. We thus design a regularized version of the market dynamics that recovers all the desirable stability and steady-state performance guarantees. Numerical tests validate our results on the IEEE 39-bus system.
Bayesian Persuasion for Algorithmic Recourse

When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain competitive advantage. The resulting opacity forces the decision subjects to rely on incomplete information when making strategic feature modifications. We capture such settings as a game of Bayesian persuasion, in which the decision maker offers a form of recourse to the decision subject by providing them with an action recommendation (or signal) to incentivize them to modify their features in desirable ways. We show that when using persuasion, the decision maker and decision subject are never worse off in expectation, while the decision maker can be significantly better off. While the decision maker's problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules. While this reformulation simplifies the problem dramatically, solving the linear program requires reasoning about exponentially-many variables, even in relatively simple cases. Motivated by this observation, we provide a polynomial-time approximation scheme that recovers a near-optimal signaling policy. Finally, our numerical simulations on semi-synthetic data empirically demonstrate the benefits of using persuasion in the algorithmic recourse setting.
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
Self-lensing flares from black hole binaries: General-relativistic ray tracing of black hole binaries

The self-lensing of a massive black hole binary (MBHB), which occurs when the two BHs are aligned close to the line of sight, is expected to produce periodic, short-duration flares. Here we study the shapes of self-lensing flares (SLFs) via general-relativistic ray tracing in a superimposed binary BH metric, in which the emission is generated by geometrically thin accretion flows around each component. The suite of models covers eccentric binary orbits, black hole spins, unequal mass binaries, and different emission model geometries. We explore the above parameter space, and report how the light curves change as a function of, e.g., binary separation, inclination, and eccentricity. We also compare our light curves to those in the microlensing approximation, and show how strong deflections, as well as time-delay effects, change the size and shape of the SLF. If gravitational waves (GWs) from the inspiraling MBHB are observed by LISA, SLFs can help securely identify the source and localizing it on the sky, and to constrain the graviton mass by comparing the phasing of the SLFs and the GWs. Additionally, when these systems are viewed edge-on the SLF shows a distinct dip that can be directly correlated with the BH shadow size. This opens a new way to measure BH shadow sizes in systems that are unresolvable by current VLBI facilities.
Semi-supervised contrastive learning for remote sensing: identifying ancient urbanization in the south-central Andes

ABSTRACT Archaeology has long faced fundamental issues of sampling and scalar representation. Traditionally, the local-to-regional-scale views of settlement patterns are produced through systematic pedestrian surveys. Recently, systematic manual survey of satellite and aerial imagery has enabled continuous distributional views of archaeological phenomena at interregional scales. However, such ‘brute force’ manual imagery survey methods are both time- and labour-intensive, as well as prone to inter-observer differences in sensitivity and specificity. The development of self-supervised learning methods (e.g. contrastive learning) offers a scalable learning scheme for locating archaeological features using unlabelled satellite and historical aerial images. However, archaeological features are generally only visible in a very small proportion relative to the landscape, while the modern contrastive-supervised learning approach typically yields an inferior performance on highly imbalanced datasets. In this work, we propose a framework to address this long-tail problem. As opposed to the existing contrastive learning approaches that typically treat the labelled and unlabelled data separately, our proposed method reforms the learning paradigm under a semi-supervised setting in order to fully utilize the precious annotated data (<7% in our setting). Specifically, the highly unbalanced nature of the data is employed as the prior knowledge in order to form pseudo negative pairs by ranking the similarities between unannotated image patches and annotated anchor images. In this study, we used 95,358 unlabelled images and 5,830 labelled images in order to solve the issues associated with detecting ancient buildings from a long-tailed satellite image dataset. From the results, our semi-supervised contrastive learning model achieved a promising testing balanced accuracy of 79.0%, which is a 3.8% improvement as compared to other state-of-the-art approaches.
Geotextile Enabled Smart Monitoring Solutions for Safe and Effective Management of Tailings and Waste Sites Two case studies: Volgermeerpolder (The Netherlands) and Suncor (Canada)"

Numerous accidents at tailing disposal sites have occurred due to dam failure and geotechnical instability. The uncontrolled spreading of pollution into the surrounding environment, illustrates over and over again the need for safe and effective management of these kinds of sites. Safe and effective management measures can only be implemented on the basis of a thorough understanding of the physical, chemical and biological processes taking place at the tailing facilities. After implementation of the necessary measures, ongoing monitoring can verify the proper functioning of the rehabilitated tailing disposal sites. Assessment and evaluation of historic tailing facilities has shown a need for improved monitoring concepts and techniques. In this paper the authors present a new monitoring technique which meets the needs for safe and effective monitoring and can be applied within a smart monitoring concept.
One Size Fits All: A Conceptual Data Model for Any Approach to Terminology

In this paper, we want to speculate about the possibility to model all the currently known/proposed approaches to terminology into a single schema. We will use the Entity-Relationship (ER) diagram as our tool for the conceptual data model of the problem and to express the associations between the objects of the study. We will analyse the onomasiological and semasiological approaches, the ontoterminology paradigm, and the frame-based model, and we will draw the consequences in terms of the conceptual data model. The result of this discussion will be used as the basis of the next step of the data organization in terms of standardized terminological records and Linked Data.