Miracle-dz/UniFAR-FTUs huggingface dataset:
Dune Erosion During Storm Conditions

Since in the Netherlands large areas of low lands are protected against the sea by coastal dunes only, it is very important to know how far, during a storm surge, the erosion of these dunes will proceed. In order to obtain an answer to this question, the cross-section of the coast has been studied. The shape of the cross-section of a sandy coast is mainly caused by displacements of sand perpendicular to the coast-line. Obviously this transport is mainly caused by waves. Since our knowledge of the very intricate physical processes, governing the sand transport by waves is, even now, still very incomplete, we can, perhaps, obtain an insight into the behaviour of a sandy coast, if we start from some rather simplified basic assumptions which are roughly in accordance with observations in nature.
Ruling out the class of statistical processes involving two noninteracting identical particles in two modes

In the framework of Generalized probabilistic theories (GPT), we illustrate a class of statistical processes in case of two noninteracting identical particles in two modes that satisfies a well motivated notion of physicality conditions namely the double stochasticity and the no-interaction condition proposed by Karczewski et. al. (Phys. Rev. Lett. 120, 080401 (2018)), which can not be realized through a quantum mechanical process. This class of statistical process is ruled out by an additional requirement called the evolution condition imposed on two particle evolution. We also show that any statistical process of two noninteracting identical particles in two modes that satisfies all of the three physicality conditions can be realized within quantum mechanics using the beam splitter operation.
Molecular complexity in the interstellar medium

Abstract The search for complex organic molecules in the interstellar medium (ISM) has revealed species of ever greater complexity. This search relies on the progress made in the laboratory to characterize their rotational spectra. Our understanding of the processes that lead to molecular complexity in the ISM builds on numerical simulations that use chemical networks fed by laboratory and theoretical studies. The advent of ALMA and NOEMA has opened a new door to explore molecular complexity in the ISM. Their high angular resolution reduces the spectral confusion of star-forming cores and their increased sensitivity allows the detection of low-abundance molecules that could not be probed before. The complexity of the recently-detected molecules manifests itself not only in terms of number of atoms but also in their molecular structure. We discuss these developments and report on ReMoCA, a new spectral line survey performed with ALMA toward the high-mass star-forming region Sgr B2(N).
Connectivity-Preserving Swarm Teleoperation Over A Tree Network With Time-Varying Delays

A teleoperated swarm must follow the unpredictable commands of its human operator while remaining connected. When the swarm communications are limited by distance and affected by delays, both the user input and the transmission delays endanger the connectivity of the swarm. This paper presents a constructive control strategy that overcomes both threats. The strategy modulates the intra-swarm couplings and the damping injected to each slave in the swarm based on a customized potential. Lyapunov-based set invariance analysis proves that the proposed explicit gain updating law limits the impact of the operator input and preserves the initial tree connectivity of a delay-free swarm. Further augmentation with stricter selection of control gains robustifies the design to time-varying delays in intra-swarm communications. The paper also establishes the input-to-state stability of a teleoperated time-delay swarm under the proposed dynamic control. Experiments validate connectivity maintenance and synchronization during time-delay swarm teleoperation with the proposed control.
Evolutionary Ecology of Bacterial Viruses

Host-phage interactions provide a tractable model system for linking data and theory at multiple scales and could help in developing a new theoretical foundation for microbial ecology. This relatively simple system could prove to be a benchmark for microbiologists who are probing more complex cellular and ecosystem processes with increasingly finer resolution, producing an expanding surplus of data through a varied means of powerful experimental techniques. Recently, applications of mathematical concepts to microbiology have found successes in efforts to organize and reduce the complexity of high-throughput data. Of course, relying on quantitative methods to parse data cannot replace the hard thinking and additional theoretical tools needed to uncover meaningful biological insights.
Stability selection enables robust learning of partial differential equations from limited noisy data

We present a statistical learning framework for robust identification of partial differential equations from noisy spatiotemporal data. Extending previous sparse regression approaches for inferring PDE models from simulated data, we address key issues that have thus far limited the application of these methods to noisy experimental data, namely their robustness against noise and the need for manual parameter tuning. We address both points by proposing a stability-based model selection scheme to determine the level of regularization required for reproducible recovery of the underlying PDE. This avoids manual parameter tuning and provides a principled way to improve the method's robustness against noise in the data. Our stability selection approach, termed PDE-STRIDE, can be combined with any sparsity-promoting penalized regression model and provides an interpretable criterion for model component importance. We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise. We illustrate the performance of our approach on a wide range of noise-corrupted simulated benchmark problems, including 1D Burgers, 2D vorticity-transport, and 3D reaction-diffusion problems. We demonstrate the practical applicability of our method on real-world data by considering a purely data-driven re-evaluation of the advective triggering hypothesis for an embryonic polarization system in C. ~elegans. Using fluorescence microscopy images of C. ~elegans zygotes as input data, our framework is able to recover the PDE model for the regulatory reaction-diffusion-flow network of the associated proteins.
Peculiar motions of the gas at the centre of the barred galaxy UGC 4056

We derive the circular velocity curves of the gaseous and stellar discs of UGC 4056, a giant barred galaxy with an active galactic nucleus (AGN). We analyse UGC 4056 using the 2D spectroscopy obtained within the framework of the Mapping Nearby Galaxies at APO (MaNGA) survey. Using images and the colour index g − r from the Sloan Digital Sky Survey (SDSS), we determined the tilt of the galaxy, which allows us to conclude that the galaxy rotates clockwise with trailing spiral arms. We found that the gas motion at the central part of the UGC 4056 shows peculiar features. The rotation velocity of the gaseous disc shows a bump within around three kiloparsecs while the rotation velocity of the stellar disc falls smoothly to zero with decreasing galactocentric distance. We demonstrate that the peculiar radial velocities in the central part of the galaxy may be caused by the inflow of the gas towards the nucleus of the galaxy. The unusual motion of the gas takes place at the region with the AGN-like radiation and can be explained by the gas response to the bar potential.
Cross-Media Usage of Social Big Data for Emergency Services and Volunteer Communities: Approaches, Development and Challenges of Multi-Platform Social Media Services

The use of social media is ubiquitous and nowadays well-established in our everyday life, but increasingly also before, during or after emergencies. The produced data is spread across several types of social media and can be used by different actors, such as emergency services or volunteer communities. There are already systems available that support the process of gathering, analysing and distributing information through social media. However, dependent on the goal of analysis, the analysis methods and available systems are limited based on technical or business-oriented restrictions. This paper presents the design of a cross-platform Social Media API, which was integrated and evaluated within multiple emergency scenarios. Based on the lessons learned, we outline the core challenges from the practical development and theoretical findings, focusing (1) cross-platform gathering and data management, (2) trustability and information quality, (3) tailorability and adjustable data operations, and (4) queries, performance, and technical development.
Density of states measurements for the heavy subband of holes in HgTe quantum wells

Narrow HgTe quantum wells are remarkable by presence of poorly conductive two-dimensional heavy hole subband (located in the local valleys) states atop of well-conductive Dirac-like light holes at the $\Gamma$ point. Here we propose and employ two methods to measure the density of states for these heavy holes. The first method uses the gate-recharging technique to measure thermodynamical entropy per particle. As the Fermi level is tuned with gate voltage from light to heavy subband, the entropy increases dramatically, and the value of this increase gives the estimate for the density of states. The second method determines the density of states for heavy holes indirectly from the gate voltage dependence of the period of the Shubnikov-de Haas oscillations for light holes. The results obtained by both methods are in the reasonable agreement with each other. Our approaches can be applied to measure large effective carrier masses in other two-dimensional gated systems.
Global Nonlinear Stability of Geodesic Solutions of Evolutionary Faddeev Model

In this paper, for evolutionary Faddeev model corresponding to maps from the Minkowski space $\mathbb{R}^{1+n}$ to the unit sphere $\mathbb{S}^2$, we show the global nonlinear stability of geodesic solutions, which are a kind of nontrivial and large solutions.
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this article, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED, and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering

Machine learning recently has been used to identify the governing equations for dynamics in physical systems. The promising results from applications on systems such as fluid dynamics and chemical kinetics inspire further investigation of these methods on complex engineered systems. Dynamics of these systems play a crucial role in design and operations. Hence, it would be advantageous to learn about the mechanisms that may be driving the complex dynamics of systems. In this work, our research question was aimed at addressing this open question about applicability and usefulness of novel machine learning approach in identifying the governing dynamical equations for engineered systems. We focused on distillation column which is an ubiquitous unit operation in chemical engineering and demonstrates complex dynamics i.e. it's dynamics is a combination of heuristics and fundamental physical laws. We tested the method of Sparse Identification of Non-Linear Dynamics (SINDy) because of it's ability to produce white-box models with terms that can be used for physical interpretation of dynamics. Time series data for dynamics was generated from simulation of distillation column using ASPEN Dynamics. One promising result was reduction of number of equations for dynamic simulation from 1000s in ASPEN to only 13 - one for each state variable. Prediction accuracy was high on the test data from system within the perturbation range, however outside perturbation range equations did not perform well. In terms of physical law extraction, some terms were interpretable as related to Fick's law of diffusion (with concentration terms) and Henry's law (with ratio of concentration and pressure terms). While some terms were interpretable, we conclude that more research is needed on combining engineering systems with machine learning approach to improve understanding of governing laws for unknown dynamics.
Awakening of the Fast-spinning Accreting Be/X-Ray Pulsar A0538–66

A0538−66 is a Be/X-ray binary (Be/XRB) hosting a 69 ms pulsar It emitted bright X-ray outbursts with peak luminosity up to ∼1039 erg s−1 during the first years after its discovery in 1977. Since then, it was always seen in quiescence or during outbursts with Lx ≲ 4 × 1037 erg s−1. In 2018 we carried out XMM-Newton observations of A0538−66 during three consecutive orbits when the pulsar was close to periastron. In the first two observations we discovered a remarkable variability, with flares of typical durations between ∼2 and 50 s and peak luminosities up to ∼4 × 1038 erg s−1 (0.2–10 keV). Between the flares the luminosity was ∼2 × 1035 erg s−1. The flares were absent in the third observation, during which A0538−66 had a steady luminosity of 2 × 1034 erg s−1. In all observations, the X-ray spectra consist of a softer component, well described by an absorbed power law with photon index Γ1 ≈ 2–4 and NH ≈ 1021 cm−2, plus a harder power-law component (Γ2 ≈ 0–0.5) dominating above ∼2 keV. The softer component shows larger flux variations than the harder one, and a moderate hardening correlated with the luminosity. The fast flaring activity seen in these observations was never observed before in A0538−66, nor, to the best of our knowledge, in other Be/XRBs. We explore the possibility that during our observations the source was accreting in a regime of nearly spherically symmetric inflow. In this case, an atmosphere can form around the neutron star magnetosphere and the observed variability can be explained by transitions between the accretion and supersonic propeller regimes.
Revisiting a negative cosmological constant in light of low-redshift data

Persisting tensions between high-redshift and low-redshift cosmological observations suggest the dark energy sector of the Universe might be more complex than the positive cosmological constant of the $\Lambda$CDM model. Motivated by string theory, wherein symmetry considerations make consistent AdS backgrounds (\textit (i.e.) maximally symmetric spacetimes with a negative cosmological constant) ubiquitous, we explore a scenario where the dark energy sector consists of two components: a negative cosmological constant, with a dark energy component with equation of state $w_{\phi}$ on top. We test the consistency of the model against low-redshift Baryon Acoustic Oscillation and Type Ia Supernovae distance measurements, assessing two alternative choices of distance anchors: the sound horizon at baryon drag determined by the \textit{Planck} collaboration, and the Hubble constant determined by the SH0ES program. We find no evidence for a negative cosmological constant, and mild indications for an effective phantom dark energy component on top. A model comparison analysis reveals the $\Lambda$CDM model is favoured over our negative cosmological constant model. While our results are inconclusive, should low-redshift tensions persist with future data, it would be worth reconsidering and further refining our toy negative cosmological constant model by considering realistic string constructions.
The homology of groups, profinite completions, and echoes of Gilbert Baumslag

There is a finitely presented acyclic group $U$ such that $U$ has no proper subgroups of finite index and every finitely presented group can be embedded in $U$. There is no algorithm that can determine whether or not a finitely presentable subgroup of a residually finite, biautomatic group is perfect. For every recursively presented abelian group $A$ there exists a pair of groups $i:P_A\hookrightarrow G_A$ such that $i$ induces an isomorphism of profinite completions, where $G_A$ is a torsion-free biautomatic group that is residually finite and superperfect, while $P_A$ is a finitely generated group with $H_2(P_A,\mathbb{Z})\cong A$.
Data Association Algorithm Based on Fuzzy Synthetic Evaluation of Multiple Features in Multi-Target Passive Tracking

A new data association algorithm based on fuzzy synthetic evaluation of multiple features in multi-target passive tracking is proposed in this paper. Based on the association factor models, the fuzzy association degree of all the effective observations within the single tracking gate is calculated by fuzzy evaluation of multiple features. Further, the fuzzy association degree of all the targets is calculated by fuzzy synthetic evaluation. At last, the decision of data association is made. Because of the utilization of more features of the target, the proposed algorithm is superior to the NN method, and the association error ratio is decreased about 15%, which is proven with the help of computer simulation.
Jo: The Smart Journal

We introduce Jo, a mobile application that attempts to improve user's well-being. Jo is a journaling application--users log their important moments via short texts and optionally an attached photo. Unlike a static journal, Jo analyzes these moments and helps users take action towards increased well-being. For example, Jo annotates each moment with a set of values (e.g., family, socialization, mindfulness), thereby giving the user insights about the balance in their lives. In addition, Jo helps the user create reminders that enable them to create additional happy moments. We describe the results of fielding Jo in a study of 39 participants. The results illustrate the promise of a journaling application that provides personalized feedback, and points at further research.
Distributed Hypothesis Testing: Cooperation and Concurrent Detection

A single-sensor two-detectors system is considered where the sensor communicates with both detectors and Detector 1 communicates with Detector 2, all over noise-free rate-limited links. The sensor and both detectors observe discrete memoryless source sequences whose joint probability mass function depends on a binary hypothesis. The goal at each detector is to guess the binary hypothesis in a way that, for increasing observation lengths, the probability of error under one of the hypotheses decays to zero with largest possible exponential decay, whereas the probability of error under the other hypothesis can decay to zero or to a small positive number arbitrarily slow. For the setting with positive communication rates from the sensor to the detectors and when both detectors are interested in maximizing the error exponent under the same hypothesis, we characterize the set of all possible exponents in a special case of testing against independence. In this case the cooperation link allows Detector 2 to increase its Type-II error exponent by an amount that is equal to the exponent attained at Detector 1. We also provide a general inner bound on the set of achievable error exponents that shows a tradeoff between the exponents at the two detectors in most cases. When the two detectors aim at maximizing the error exponent under different hypotheses and the distribution at the Sensor is different under the two hypotheses, then we show that such a tradeoff does not exist. We propose a general scheme that allows each detector to attain the same exponent as if it was the only detector in the system. For the setting with zero-rate communication on both links, we exactly characterize the set of possible exponents and the gain brought up by cooperation, in function of the number of bits that are sent over the two links. Notice that, for this setting, tradeoffs between the exponents achieved at the two detectors arise only in few particular cases. In all other cases, each detector achieves the same performance as if it were the only detector in the system.
Counting single-qubit Clifford equivalent graph states is #P-Complete

Graph states, which include for example Bell states, GHZ states and cluster states, form a well-known class of quantum states with applications ranging from quantum networks to error-correction. Deciding whether two graph states are equivalent up to single-qubit Clifford operations is known to be decidable in polynomial time and have been studied both in the context of producing certain required states in a quantum network but also in relation to stabilizer codes. The reason for the latter this is that single-qubit Clifford equivalent graph states exactly corresponds to equivalent stabilizer codes. We here consider the computational complexity of, given a graph state |G>, counting the number of graph states, single-qubit Clifford equivalent to |G>. We show that this problem is #P-Complete. To prove our main result we make use of the notion of isotropic systems in graph theory. We review the definition of isotropic systems and point out their strong relation to graph states. We believe that these isotropic systems can be useful beyond the results presented in this paper.
A Survey of Preferences in Virtual Machine Placement

With the rapid developments of virtualization techniques, cloud data centers have had enabled cost effective, flexible, and customizable deployments of applications in virtualized infrastructure. Virtual machine placement problem is a problem of paramount importance to the design of cloud data centers. It aims to assign each virtual machine to a server in the cloud environment. Typically, the problem involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different parties including cloud administrators and customers that we need to consider their preferences while opting for a solution. Thus, it is significant to not only return an optimized solution to the underlying placement problem but also a solution that reflects the given preferences of these parties. In this paper, we provide a detailed review on the role of preferences in the current literature of the virtual machine placement problem. We further discuss some challenges and identify possible research opportunities to better incorporate preferences within the problem.