cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Self-attending RNN for Speech Enhancement to Improve Cross-corpus Generalization
Creators: name: Pandey, Ashutosh
name: Wang, DeLiang
Publisher: name: arXiv
Year: 2021
Subjects: subject: Sound (cs.SD)
subject: Audio and Speech Processing (eess.AS)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Deep neural networks (DNNs) represent the mainstream methodology for supervised speech enhancement, primarily due to their capability to model complex functions using hierarchical representations. However, a recent study revealed that DNNs trained on a single corpus fail to generalize to untrained corpora, especially in low signal-to-noise ratio (SNR) conditions. Developing a noise, speaker, and corpus independent speech enhancement algorithm is essential for real-world applications. In this study, we propose a self-attending recurrent neural network, or attentive recurrent network (ARN), for time-domain speech enhancement to improve cross-corpus generalization. ARN comprises of recurrent neural networks (RNNs) augmented with self-attention blocks and feedforward blocks. We evaluate ARN on different corpora with nonstationary noises in low SNR conditions. Experimental results demonstrate that ARN substantially outperforms competitive approaches to time-domain speech enhancement, such as RNNs and dual-path ARNs. Additionally, we report an important finding that the two popular approaches to speech enhancement: complex spectral mapping and time-domain enhancement, obtain similar results for RNN and ARN with large-scale training. We also provide a challenging subset of the test set used in this study for evaluating future algorithms and facilitating direct comparisons.
description: Accepted for publication in IEEE/ACM Transactions on Audio, Speech and Language Processing
Reentrant Transitions in a Mixture of Small and Big Particles Interacting via Soft Repulsive Potential
Creators: name: Azizi, Itay
name: Grosberg, Alexander Y.
name: Rabin, Yitzhak
Publisher: name: arXiv
Year: 2021
Subjects: subject: Soft Condensed Matter (cond-mat.soft)
subject: FOS: Physical sciences

description: We report the first observation of temperature-controlled reentrant transition in simulations of mixtures of small and big particles interacting via soft repulsive potential in 2D. As temperature increases, the system passes from a fluid mixture, to a crystal of big particles in a fluid of small particles and back to a fluid mixture. Solidification is driven by entropy gain of small particles which overcomes the free energy cost of confining big ones. Melting results from enhanced interpenetration of particles at high temperature which reduces the entropic forces that stabilize the crystal.
Determination of atmospheric parameters from globally crowdsourced GNSS data
Creators: name: Soja, Benedikt
name: Navarro, Vicente
name: Klopotek, Grzegorz
name: Pan, Yuanxin
name: Crocetti, Laura
name: Awadaljeed, Mudathir
name: Rothacher, Markus
name: See, Linda
name: Sturn, Tobias
name: Weinacker, Rudi
name: McCallum, Ian
Publisher: name: ETH Zurich
Year: 2022

description: Accessibility to raw GNSS measurements in Android smartphones has opened new potential applications in geosciences. Nowadays, there are more than 3 billion Android devices in use, with the most recent ones equipped with multi-constellation and multi-frequency GNSS receivers. Exploiting such smartphone GNSS data on a global scale has so far not been possible due to a lack of suitable data sets. 

In the framework of the CAMALIOT project (Application of Machine Learning Technology for GNSS IoT Data Fusion), a crowdsourcing campaign was launched in March 2022 to collect GNSS data from smartphones globally. By July 2022, more than 11000 users from 142 countries have contributed over 100 billion individual measurements, resulting in a unique data set for assessing the potential of citizen science for GNSS data collection. 

In this contribution, we investigate the suitability of the crowdsourced GNSS data for atmospheric parameter determination. First, the data is filtered using a machine learning approach that separates the good from the bad quality data. Furthermore, we concentrate on data that has been collected in the vicinity of geodetic stations for comparison purposes. From the remaining smartphone data, differential tropospheric delays are estimated with baseline processing. Code-to-carrier leveling is used to determine ionospheric total electron content. The atmospheric parameters from smartphones and geodetic stations are then compared in order to assess their quality. Finally, we investigate the potential of integrating the smartphone data into global tropospheric and ionospheric models alongside the geodetic GNSS data. 

This work is funded by the NAVISP Element 1 program of ESA.
description: AGU Fall Meeting Abstracts
"Trotz mancher Schwierigkeiten". Zu den Auslandsreisen deutscher Geisteswissenschaftler zwischen 1933 und 1945
Creators: name: Albrecht, Andrea
name: Klausnitzer, Ralf
Publisher: name: Humboldt-Universität zu Berlin
Year: 2020
Subjects: subject: Nationalsozialismus
subject: Internationalität
subject: Kulturaustausch
subject: Geisteswissenschaften
subject: 900 Geschichte und Geografie

description: In the years of National Socialism, and even during World War II, German scientists traveled abroad extensively. While international travels by natural scientists were studied in some detail, travels by scholars in the humanities have been studied to a much lesser degree, even though travel documents offer valuable insights into the regulated internationality of National Socialism. – We provide a first overview of international travels of scholars in the humanities between 1933 and 1945. The examples demonstrate how travelling academics dealt with conflicting expectations, justified their intentions, articulated disappointment, offered pragmatic advice for the further shaping of contacts abroad, and in this way became active participants of the resource ensemble of academia and politics. The study is based on extensive material from the political archive of the Auswärtiges Amt and the archive of the Reichsministerium für Wissenschaft, Erziehung und Volksbildung from the Bundesarchiv which to this date have been hardly explored.
A proof of Taylor scaling for curvature-driven dislocation motion through random arrays of obstacles
Creators: name: Courte, Luca
name: Dondl, Patrick
name: Ortiz, Michael
Publisher: name: arXiv
Year: 2021
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics
subject: 35R60, 35D40, 74A40, 74C99

description: We prove Taylor scaling for dislocation lines characterized by line-tension and moving by curvature under the action of an applied shear stress in a plane containing a random array of obstacles. Specifically, we show--in the sense of optimal scaling--that the critical applied shear stress for yielding, or percolation-like unbounded motion of the dislocation, scales in proportion to the square root of the obstacle density. For sufficiently small obstacle densities, Taylor scaling dominates the linear-scaling that results from purely energetic considerations and, therefore, characterizes the dominant rate-limiting mechanism in that regime.
description: 21 pages, 3 figures
PRINS: Scalable Model Inference for Component-based System Logs
Creators: name: Shin, Donghwan
name: Bianculli, Domenico
name: Briand, Lionel
Publisher: name: arXiv
Year: 2021
Subjects: subject: Software Engineering (cs.SE)
subject: FOS: Computer and information sciences

description: Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite state models from execution logs. However, existing techniques do not scale well when processing very large logs that can be commonly found in practice. In this paper, we address the scalability problem of inferring the model of a component-based system from large system logs, without requiring any extra information. Our model inference technique, called PRINS, follows a divide-and-conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the flow of events across components, as reflected in the logs. We evaluated PRINS in terms of scalability and accuracy, using nine datasets composed of logs extracted from publicly available benchmarks and a personal computer running desktop business applications. The results show that PRINS can process large logs much faster than a publicly available and well-known state-of-the-art tool, without significantly compromising the accuracy of inferred models.
Culture-independent study of bacterial communities in tropical river sediment
Creators: name: Honglada Thoetkiattikul
name: Wuttichai Mhuantong
name: Onruthai Pinyakong
name: Worachart Wisawapipat
name: Yamazoe, Atsushi
name: Fujita, Nobuyuki
name: Eurwilaichitr, Lily
name: Verawat Champreda
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Microbiology
subject: FOS: Biological sciences
subject: Evolutionary Biology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Ecology
subject: 69999 Biological Sciences not elsewhere classified
subject: Inorganic Chemistry

description: Ubiquitous microbial communities in river sediments actively govern organic matter decomposition, nutrient recycling, and remediation of toxic compounds. In this study, prokaryotic diversity in two major rivers in central Thailand, the Chao Phraya (CP) and the Tha Chin (TC) distributary was investigated. Significant differences in sediment physicochemical properties, particularly silt content, were noted between the two rivers. Tagged 16S rRNA sequencing on a 454 platform showed that the sediment microbiomes were dominated by Gammaproteobacteria and sulfur/sulfate reducing Deltaproteobacteria, represented by orders Desulfobacteriales and Desulfluromonadales together with organic degraders Betaproteobacteria (orders Burkholderiales and Rhodocyclales) together with the co-existence of Bacteroidetes predominated by Sphingobacteriales. Enrichment of specific bacterial orders was found in the clayey CP and silt-rich TC sediments, including various genera with known metabolic capability on decomposition of organic matter and xenobiotic compounds. The data represent one of the pioneered works revealing heterogeneity of bacteria in river sediments in the tropics. Analysis of bacterial diversity by tagged 16S rRNA sequencing revealed enrichment of specific taxa in CP and TC rivers.
Subdivision-Based Mesh Convolution Networks
Creators: name: Hu, Shi-Min
name: Liu, Zheng-Ning
name: Guo, Meng-Hao
name: Cai, Jun-Xiong
name: Huang, Jiahui
name: Mu, Tai-Jiang
name: Martin, Ralph R.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Graphics (cs.GR)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences
subject: I.3.5

description: Convolutional neural networks (CNNs) have made great breakthroughs in 2D computer vision. However, their irregular structure makes it hard to harness the potential of CNNs directly on meshes. A subdivision surface provides a hierarchical multi-resolution structure, in which each face in a closed 2-manifold triangle mesh is exactly adjacent to three faces. Motivated by these two observations, this paper presents SubdivNet, an innovative and versatile CNN framework for 3D triangle meshes with Loop subdivision sequence connectivity. Making an analogy between mesh faces and pixels in a 2D image allows us to present a mesh convolution operator to aggregate local features from nearby faces. By exploiting face neighborhoods, this convolution can support standard 2D convolutional network concepts, e.g. variable kernel size, stride, and dilation. Based on the multi-resolution hierarchy, we make use of pooling layers which uniformly merge four faces into one and an upsampling method which splits one face into four. Thereby, many popular 2D CNN architectures can be easily adapted to process 3D meshes. Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach. Extensive evaluation and various applications demonstrate SubdivNet's effectiveness and efficiency.
description: Codes are available in https://github.com/lzhengning/SubdivNet
Morse Index Stability for Critical Points to Conformally invariant Lagrangians
Creators: name: Da Lio, Francesca
name: Gianocca, Matilde
name: Rivière, Tristan
Publisher: name: ETH Zurich
Year: 2022

description: We prove the upper-semi-continuity of the Morse index plus Nullity of critical points to general conformally invariant lagrangians in dimension 2 under weak convergence. Precisely we establish that the Morse Indices plus the Nullity of an arbitrary sequence of weakly converging critical points to a general conformally invariant lagrangians of maps from an arbitrary closed surface into an arbitrary closed smooth manifold passes to the limit in the following sense : it is asymptotically bounded from above by the sum of the Morse Indices plus the nullity of the weak limit and the bubbles while it was well known that the sum of the Morse Index of the weak limit with the Morse indices of the bubbles is asymptotically bounded from above by the Morse indices of the weakly converging sequence .
description: arXiv
The effects of LMC-mass environments on their dwarf satellite galaxies in the FIRE simulations
Creators: name: Jahn, Ethan D.
name: Sales, Laura V.
name: Wetzel, Andrew
name: Samuel, Jenna
name: El-Badry, Kareem
name: Boylan-Kolchin, Michael
name: Bullock, James S.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: Characterizing the predicted environments of dwarf galaxies like the Large Magellanic Cloud (LMC) is becoming increasingly important as next generation surveys push sensitivity limits into this low-mass regime at cosmological distances. We study the environmental effects of LMC-mass halos ($M_{200m} \sim 10^{11}$ M$_\odot$) on their populations of satellites ($M_\star \geq 10^4$ M$_\odot$) using a suite of zoom-in simulations from the Feedback In Realistic Environments (FIRE) project. Our simulations predict significant hot coronas with $T\sim10^5$ K and $M_\text{gas}\sim10^{9.5}$ M$_\odot$. We identify signatures of environmental quenching in dwarf satellite galaxies, particularly for satellites with intermediate mass ($M_\star = 10^{6-7}$ M$_\odot$). The gas content of such objects indicates ram-pressure as the likely quenching mechanism, sometimes aided by star formation feedback. Satellites of LMC-mass hosts replicate the stellar mass dependence of the quiescent fraction found in satellites of MW mass hosts (i.e. that the quiescent fraction increases as stellar mass decreases). Satellites of LMC-mass hosts have a wider variety of quenching times when compared to the strongly bi-modal distribution of quenching times of nearby centrals. Finally, we identify significant tidal stellar structures around four of our six LMC-analogs, suggesting that stellar streams may be common. These tidal features originated from satellites on close orbits, extend to $\sim$80 kpc from the central galaxy, and contain $\sim10^{6-7}$ M$_\odot$~of stars.
description: 14 pages, 10 figures, submitted to MNRAS
Phylogenesys and homology modeling in Zika virus epidemic: food for thought
Creators: name: Angeletti, Silvia
name: Presti, Alessandra Lo
name: Giovanetti, Marta
name: Grifoni, Alba
name: Amicosante, Massimo
name: Ciotti, Marco
name: Luiz-Carlos J. Alcantara
name: Cella, Eleonora
name: Ciccozzi, Massimo
Publisher: name: Taylor & Francis
Year: 2016
Subjects: subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Molecular Biology
subject: Evolutionary Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: 60506 Virology

description: Zika virus (ZIKV) is an emerging Flavivirus that have recently caused an outbreak in Brazil and rapid spread in several countries. In this study, the consequences of ZIKV evolution on protein recognition by the host immune system have been analyzed. Evolutionary analysis was combined with homology modeling and T-B cells epitope predictions. Two separate clades, the African one with the Uganda sequence, as the most probable ancestor, and the second one containing all the most recent sequences from the equatorial belt were identified. Brazilian strains clustered all together and closely related to the French Polynesia isolates. A strong presence of a negatively selected site in the envelope gene (Env) protein was evidenced, suggesting a probable purging of deleterious polymorphisms in functionally important genes. Our results show relative conservancy of ZIKV sequences when envelope and other non-structural proteins (NS3 and NS5) are analyzed by homology modeling. However, some regions within the consensus sequence of NS5 protein and to a lesser extent in the envelope protein, show localized high mutation frequency corresponding to a considerable alteration in protein stability. In terms of viral immune escape, envelope protein is under a higher selective pressure than NS5 and NS3 proteins for HLA class I and II molecules. Moreover, envelope mutations that are not strictly related to T-cell immune responses are mostly located on the surface of the protein in putative B-cell epitopes, suggesting an important contribution of B cells in the immune response as well.
Ten dimensional symmetry of N=4 SYM correlators
Creators: name: Caron-Huot, Simon
name: Coronado, Frank
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: We consider four-point correlation functions of protected single-trace scalar operators in planar N = 4 supersymmetric Yang-Mills (SYM). We conjecture that all loop corrections derive from an integrand which enjoys a ten-dimensional symmetry. This symmetry combines spacetime and R-charge transformations. By considering a 10D light-like limit, we extend the correlator/amplitude duality by equating large R-charge octagons with Coulomb branch scattering amplitudes. Using results from integrability, this predicts new finite amplitudes as well as some Feynman integrals.
description: 34 pages + appendices, 16 figures
Revisiting moment tensors in Switzerland: Unraveling source characteristics in Central Alps and their foreland
Creators: name: Mesimeri, Maria
name: Diehl, Tobias
name: Clinton, John Francis
name: Herwegh, Marco
name: Wiemer, Stefan
Publisher: name: ETH Zurich
Year: 2022

description: Studies on moment tensors (MT) and focal mechanisms are of great importance for assessing regional and local seismotectonic processes, especially when a high-quality, dense network is in operation. However, common MT inversion methods are largely restricted to magnitudes > 3.5. In order to lower the completeness of MT catalogs, improved Green’s functions and/or hybrid inversion techniques are needed. In this study, we revisit small-to-moderate earthquakes, which occurred in Switzerland and surrounding regions by means of various MT inversion methods and assess the potential to improve completeness of MT catalogs in Central Alps region. To accomplish this, we implement state-of the art methods for MT inversion using either full waveform data or combinations of first-motion polarities with amplitudes and amplitude ratios. Methods based on full waveform inversion considered in this study are ISOLA (Sokos & Zahradnik 2013) and Grond (Heimann et al. 2018), as well as techniques based on amplitudes and/or polarities (HybridMT (Kwiatek et al. 2016), MTfit (Pugh & White 2018)), which can solve MTs for smaller magnitude earthquakes. Hence, the combination of multiple techniques allows to compute full or deviatoric MTs for a broader range of magnitudes and enrich the existing catalogs.

We first apply these methods to recent earthquake sequences occurred in the Central Alps between 2019 and 2021. During that period, several earthquake sequences, like the one associated with the 2021 M4.1 Arolla earthquake, occurred and show complexity on the waveforms, due to their shallow focal depths. In addition, several of the standard MT solutions calculated by the Swiss Seismological Service (SED) for these earthquakes indicate complex moment tensors with unusually high percentage of the CLVD component. To check whether such CLVD component is real and not an artifact caused, for instance, by unmodeled heterogeneities, we invert for full and deviatoric MTs using multiple 1D velocity models and algorithms. Additionally, we perform MT inversions for several earthquakes either within selected earthquake sequences or regional background seismicity. The resulting MT solutions are compared to existing high-quality focal mechanisms computed using first motion polarities as well as to high-precision double difference locations. Uncertainties of MT solutions are estimated using bootstrap-based methods. This work contributes towards an enriched high-quality focal mechanisms database for Switzerland, which could be used to revisit the regional to local stress field at unprecedented resolution and provides new insights into the complexities of active fault systems in the Central Alps region.
description: EGUsphere
On Exact Solvability of $\mathcal N$=4 super Yang-Mills
Creators: name: Popov, Alexander D.
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: We consider the ambitwistor description of $\mathcal N$=4 supersymmetric extension of U($N$) Yang-Mills theory on Minkowski space $\mathbb R^{3,1}$. It is shown that solutions of super-Yang-Mills equations are encoded in real analytic U($N$)-valued functions on a domain in superambitwistor space ${\mathcal L}_{\mathbb R}^{5|6}$ of real dimension $(5|6)$. This leads to a procedure for generating solutions of super-Yang-Mills equations on $\mathbb R^{3,1}$ via solving a Riemann-Hilbert-type factorization problem on two-spheres in $\mathcal L_{\mathbb R}^{5|6}$.
description: 23 pages, v2: clarifying comments added, published version
An Ordinal Latent Variable Model of Conflict Intensity
Creators: name: Stoehr, Niklas
name: Torroba Hennigen, Lucas
name: Valvoda, Josef
name: West, Robert
name: Cotterell, Ryan
name: Schein, Aaron
Publisher: name: ETH Zurich
Year: 2022

description: For the quantitative monitoring of international relations, political events are extracted from the news and parsed into "who-did-what-to-whom" patterns. This has resulted in large data collections which require aggregate statistics for analysis. The Goldstein Scale is an expert-based measure that ranks individual events on a one-dimensional scale from conflictual to cooperative. However, the scale disregards fatality counts as well as perpetrator and victim types involved in an event. This information is typically considered in qualitative conflict assessment. To address this limitation, we propose a probabilistic generative model over the full subject-predicate-quantifier-object tuples associated with an event. We treat conflict intensity as an interpretable, ordinal latent variable that correlates conflictual event types with high fatality counts. Taking a Bayesian approach, we learn a conflict intensity scale from data and find the optimal number of intensity classes. We evaluate the model by imputing missing data. Our scale proves to be more informative than the original Goldstein Scale in autoregressive forecasting and when compared with global online attention towards armed conflicts.
description: arXiv
Robust detection and attribution of climate change under interventions
Creators: name: Székely, Enikő
name: Sippel, Sebastian
name: Meinshausen, Nicolai
name: Obozinski, Guillaume
name: Knutti, Reto
Publisher: name: ETH Zurich
Year: 2022
Subjects: Detection and attribution
Distributional robustness
Anchor regression
Interventions
Mean independence

description: Fingerprints are key tools in climate change detection and attribution (D&A) that are used to determine whether changes in observations are different from internal climate variability (detection), and whether observed changes can be assigned to specific external drivers (attribution). We propose a direct D&A approach based on supervised learning to extract fingerprints that lead to robust predictions under relevant interventions on exogenous variables, i.e., climate drivers other than the target. We employ anchor regression, a distributionally-robust statistical learning method inspired by causal inference that extrapolates well to perturbed data under the interventions considered. The residuals from the prediction achieve either uncorrelatedness or mean independence with the exogenous variables, thus guaranteeing robustness. We define D&A as a unified hypothesis testing framework that relies on the same statistical model but uses different targets and test statistics. In the experiments, we first show that the CO2 forcing can be robustly predicted from temperature spatial patterns under strong interventions on the solar forcing. Second, we illustrate attribution to the greenhouse gases and aerosols while protecting against interventions on the aerosols and CO2 forcing, respectively. Our study shows that incorporating robustness constraints against relevant interventions may significantly benefit detection and attribution of climate change.
description: arXiv
Nanosatellites: The next big chapter in atmospheric tomography
Creators: name: Moeller, Gregor
name: Ao, Chi
Publisher: name: ETH Zurich
Year: 2022
Subjects: GNSS
Radio Occulation
Nanosatellites
Tomography
Water vapor density

description: Nanosatellite technology opens up new possibilities for Earth observation. We expect that in the next decade large satellite constellations will arise with hundred, up to thousand satellites in low Earth orbit. While most constellations will be dedicated to internet of things and global communication, a larger number of satellites will be also equipped with rather low-cost sensors, such as GNSS receivers, suited for monitoring of the Earth’s atmosphere. However, future evolution of atmospheric science leans not only on densified observing systems but also on new, more complex analysis methods. In this respect, tomographic principles provide a unique opportunity for sensor fusion and therefore, contribute to a better understanding of the specific sensor characteristics and to exploit their full potential for sensing the three-dimensional atmospheric state. One difficulty in performing the conversion of integral measurements into three-dimensional images is that the signal ray path is not a straight line but rather dependent on the object properties along the signal path. Another challenge is related to the limited number of radio sources and detectors with respect to the size of the object of interest. Therefore, the inverse problem is either solved linearly or iterative non-linear. In this chapter, the individual solving techniques for the tomographic problem are presented, including strategies for removing deficiencies of the ill-posed problem by using truncated singular value decomposition and L-curve technique. Applied to dense nanosatellite formations, a new quality in the reconstruction of the 3D water vapor distribution is obtained, which has the potential for leading to further advances in atmospheric science.
description: ISSN:https://zenodo.org/communities/iag-comm4-symp-2022
Discriminating same-mass Neutron Stars and Black Holes gravitational wave-forms
Creators: name: Coupechoux, J. -F.
name: Arbey, A.
name: Chierici, R.
name: Hansen, H.
name: Margueron, J.
name: Sordini, V.
Publisher: name: arXiv
Year: 2021
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: Gravitational wave-forms from coalescences of binary black hole systems and binary neutron star systems with low tidal effects can hardly be distinguished if the two systems have similar masses. In the absence of discriminating power based on the gravitational wave-forms, the classification of sources into binary neutron stars, binary black holes and mixed systems containing a black hole and a neutron star can only be unambiguous when assuming the standard model of stellar evolution and using the fact that there exists a mass gap between neutron stars and black holes. This approach is however limited by its own assumptions: for instance the 2.6 solar mass object detected in the GW190814 event remains unclassified, and models of new physics can introduce new compact objects, like primordial black holes, which may have masses in the same range as neutron stars. Then, without an electromagnetic counterpart (kilonova), classifying mergers of compact objects without mass gap criteria remains a difficult task, unless the source is close enough. In what follows we investigate a procedure to discriminate a model between binary neutron star merger and primordial binary black hole merger by using a Bayes factor in simulated wave-forms that we superimpose to realistic detector noise.
description: 18 pages, 7 figures
WimPyDD: an object-oriented Python code for the calculation of WIMP direct detection signals
Creators: name: Jeong, Injun
name: Kang, Sunghyun
name: Scopel, Stefano
name: Tomar, Gaurav
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: We introduce WimPyDD, a modular, object-oriented and customizable Python code that calculates accurate predictions for the expected rates in Weakly Interacting Massive Particle (WIMP) direct-detection experiments within the framework of Galilean-invariant non-relativistic effective theory in virtually any scenario, including inelastic scattering, an arbitrary WIMP spin and a generic WIMP velocity distribution in the Galactic halo. WimPyDD exploits the factorization of the three main components that enter in the calculation of direct detection signals: i) the Wilson coefficients that encode the dependence of the signals on the ultraviolet completion of the effective theory; ii) a response function that depends on the nuclear physics and on the main features of the experimental detector (acceptance, energy resolution, response to nuclear recoils); iii) a halo function that depends on the WIMP velocity distribution and that encodes the astrophysical inputs. In WimPyDD these three components are calculated and stored separately for later interpolation and combined together only as the last step of the signal evaluation procedure. This makes the phenomenological study of the direct detection scattering rate with WimPyDD transparent and fast also when the parameter space of the WIMP model has a large dimensionality.
description: 33 pages, 4 figure, 4 tables. WimPyDD can be downloaded from https://wimpydd.hepforge.org/
Beyond Mass: Detecting Secondary Halo Properties with Galaxy-Galaxy Lensing
Creators: name: Xhakaj, Enia
name: Leauthaud, Alexie
name: Lange, Johannes
name: Hearin, Andrew
name: Diemer, Benedikt
name: Dalal, Neal
Publisher: name: arXiv
Year: 2021
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: Secondary halo properties beyond mass, such as the mass accretion rate (MAR), concentration, and the half mass scale, are essential in understanding the formation of large-scale structure and dark matter halos. In this paper, we study the impact of secondary halo properties on the galaxy-galaxy lensing observable, $ΔΣ$. We build an emulator trained on N-body simulations to model $ΔΣ$ and quantify the impact of different secondary parameters on the $ΔΣ$ profile. We focus on the impact of MAR on $ΔΣ$. We show that a 3$σ$ detection of variations in MAR at fixed halo mass could be achieved with the Hyper Suprime Cam survey in combination with a proxy for MAR with scatter $σ_{Γ_\mathrm{dyn}|\mathrm{obs}}<1.5$. We show that the full radial profile of $ΔΣ$ depends on secondary properties at fixed halo mass. Consequently, an emulator that can perform full shape fitting yields better than 2 times improvement upon the constraints on MAR than only using the outer part of the halo. Finally, we highlight that miscentering and MAR impact the radial profile of $ΔΣ$ in a similar fashion, implying that miscentering and MAR need to be modeled jointly for unbiased estimates of both effects. We show that present-day lensing data sets have the statistical capability to place constraints on halo MAR. Our analysis opens up new possibilities for observationally measuring the assembly history of the dark matter halos that host galaxies and clusters.
Simulation-based design study for the passive shielding of the COSINUS dark matter experiment
Creators: name: Angloher, G.
name: Dafinei, I.
name: Di Marco, N.
name: Ferroni, F.
name: Fichtinger, S.
name: Filipponi, A.
name: Friedl, M.
name: Fuss, A.
name: Ge, Z.
name: Heikinheimo, M.
name: Huitu, K.
name: Maji, R.
name: Mancuso, M.
name: Pagnanini, L.
name: Petricca, F.
name: Pirro, S.
name: Pröbst, F.
name: Profeta, G.
name: Puiu, A.
name: Reindl, F.
name: Schäffner, K.
name: Schieck, J.
name: Schmiedmayer, D.
name: Schwertner, C.
name: Stahlberg, M.
name: Stendahl, A.
name: Wagner, F.
name: Yue, S.
name: Zema, V.
name: Zhu, Y.
name: Pandola, L.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Instrumentation and Detectors (physics.ins-det)
subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: FOS: Physical sciences

description: The COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches) experiment aims at the detection of dark matter-induced recoils in sodium iodide (NaI) crystals operated as scintillating cryogenic calorimeters. The detection of both scintillation light and phonons allows performing an event-by-event signal to background discrimination, thus enhancing the sensitivity of the experiment. The construction of the experimental facility is foreseen to start by 2021 at the INFN Gran Sasso National Laboratory (LNGS) in Italy. It consists of a cryostat housing the target crystals shielded from the external radioactivity by a water tank acting, at the same time, as an active veto against cosmic ray-induced events. Taking into account both environmental radioactivity and intrinsic contamination of materials used for cryostat, shielding and infrastructure, we performed a careful background budget estimation. The goal is to evaluate the number of events that could mimic or interfere with signal detection while optimising the geometry of the experimental setup. In this paper we present the results of the detailed Monte Carlo simulations we performed, together with the final design of the setup that minimises the residual amount of background particles reaching the detector volume.
description: 12 pages, 7 figures
FollicleFinder: automated three-dimensional segmentation of human ovarian follicles
Creators: name: Yamauchi, Kevin
name: Biniasch, Marieke
name: Franz, Leopold
name: Gómez, Harold
name: de Geyter, Christian
name: Iber, Dagmar
Publisher: name: ETH Zurich
Year: 2022

description: In vitro fertilization (IVF) treatment protocols require frequent monitoring of the ovarian follicle growth process. We report FollicleFinder, an open source pipeline for the automated, 3D segmentation of ovarian follicles. FollicleFinder also accurately measures clinically-relevant morphological properties such as diameter, surface area, and volume.
description: bioRxiv
Impact of cell size on morphogen gradient precision
Creators: name: Adelmann, Jan A.
name: Vetter, Roman
name: Iber, Dagmar
Publisher: name: ETH Zurich
Year: 2022

description: Patterning during embryonic development is remarkably precise and reproducible. How high patterning precision is achieved is still largely elusive. Here, we use a novel simulation framework to determine the impact of cell size on the variability of morphogen gradients and the positional information they convey. We find that both variability in the cell diameter and spatial averaging of the local concentration affect the sensed gradient variability only little. In contrast, the mean cell diameter has a profound impact on gradient variability and on the resulting positional accuracy. We reveal the corresponding scaling relationships between positional error, cell diameter and gradient length. Consistent with this observation, apical cell areas are small in tissues that are patterned by morphogen gradients. We conclude that tissues achieve higher patterning precision with small cross-sectional cell areas.
description: bioRxiv
Super-resolving star clusters with sheaves
Creators: name: Robinson, Michael
name: Capraro, Christopher
Publisher: name: arXiv
Year: 2021
Subjects: subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Computational Engineering, Finance, and Science (cs.CE)
subject: Metric Geometry (math.MG)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences
subject: FOS: Mathematics
subject: I.4.8
subject: 85-08

description: This article explains an optimization-based approach for counting and localizing stars within a small cluster, based on photon counts in a focal plane array. The array need not be arranged in any particular way, and relatively small numbers of photons are required in order to ensure convergence. The stars can be located close to one another, as the location and brightness errors were found to be low when the separation was larger than $0.2$ Rayleigh radii. To ensure generality of our approach, it was constructed as a special case of a general theory built upon topological signal processing using the mathematics of sheaves.
description: arXiv admin note: text overlap with arXiv:2106.04445
Long Dead Winos
Creators: name: Goodsell, Mark D.
name: Priya, Lakshmi
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: We describe a new code and approach using particle-level information to recast the recent CMS disappearing track searches including all run 2 data. Notably, the simulation relies on knowledge of the detector geometry, and we also include the simulation of pileup events directly rather than as an efficiency function. We validate it against provided acceptances and cutflows, and use it in combination with heavy stable charged particle searches to place limits on winos with any proper decay length above a centimetre. We also provide limits for a simple model of a charged scalar that is only produced in pairs, that decays to electrons plus an invisible fermion.
description: 25 pages, 5 figures, 10 tables. Code available at the LLPrecasting repository https://github.com/llprecasting/recastingCodes/ and at https://goodsell.pages.in2p3.fr/hackanalysis/
Dry Cutting Experiments Database Ti6Al4V and Ck45
Creators: name: Klippel, Hagen
name: Süssmaier, Stefan
name: Kuffa, Michal
name: Wegener, Konrad
Publisher: name: ETH Zurich
Year: 2022
Subjects: Material characterization
Metal cutting
Orthogonal cutting
Dry cutting
Ck45
AISI 1045
Ti6Al4V
Process forces
Cutter geometry
Chip shape
Kinezle coefficients
info:eu-repo/classification/ddc/670
Manufacturing

description: The numerical simulation of metal cutting processes requires material data for constitutive equations, which cannot be obtained with standard material testing procedures. Instead, inverse identifications of material parameters within numerical simulation models of the cutting experiment itself are necessary. The intention of the present report is the provision of results of a large scale experimental study of dry orthogonal cutting experiments of Ti6Al4V (3.7165 Grade 5) and Ck45 (AISI 1045) along with their documentation and interpretation. The process forces are evaluated and each cutting insert geometry has been measured prior to the experiments to determine the cutting edge radii for each experiment. The resulting chip forms are analysed and the averaged chip thicknesses are determined. A material characterization is performed, which includes microstructural investigations on the raw materials and is reported together with tensile test results. The assembled data set can be used for parameter identification when the experimental conditions are reproduced in numerical simulations. The cutting test results are finally used to derive coefficients for Kienzle's force model. The data is stored in the pCloud and contains process force measurement data, cutting edge radii scans, pictures of chip geometries and etched chips.
description: arXiv
State-of-the-art generalisation research in NLP: a taxonomy and review
Creators: name: Hupkes, Dieuwke
name: Giulianelli, Mario
name: Dankers, Verna
name: Artetxe, Mikel
name: Elazar, Yanai
name: Pimentel, Tiago
name: Christodoulopoulos, Christos
name: Lasri, Karim
name: Saphra, Naomi
name: Sinclair, Arabella
name: Ulmer, Dennis
name: Schottmann, Florian
name: Batsuren, Khuyagbaatar
name: Sun, Kaiser
name: Sinha, Koustuv
name: Khalatbari, Leila
name: Ryskina, Maria
name: Frieske, Rita
name: Cotterell, Ryan
name: Jin, Zhijing
Publisher: name: ETH Zurich
Year: 2022

description: The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what `good generalisation' entails and how it should be evaluated is not well understood, nor are there any common standards to evaluate it. In this paper, we aim to lay the ground-work to improve both of these issues. We present a taxonomy for characterising and understanding generalisation research in NLP, we use that taxonomy to present a comprehensive map of published generalisation studies, and we make recommendations for which areas might deserve attention in the future. Our taxonomy is based on an extensive literature review of generalisation research, and contains five axes along which studies can differ: their main motivation, the type of generalisation they aim to solve, the type of data shift they consider, the source by which this data shift is obtained, and the locus of the shift within the modelling pipeline. We use our taxonomy to classify over 400 previous papers that test generalisation, for a total of more than 600 individual experiments. Considering the results of this review, we present an in-depth analysis of the current state of generalisation research in NLP, and make recommendations for the future. Along with this paper, we release a webpage where the results of our review can be dynamically explored, and which we intend to up-date as new NLP generalisation studies are published. With this work, we aim to make steps towards making state-of-the-art generalisation testing the new status quo in NLP.
description: arXiv
Exponential asymptotics for steady parasitic capillary ripples on steep gravity waves
Creators: name: Shelton, Josh
name: Trinh, Philippe H.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Fluid Dynamics (physics.flu-dyn)
subject: FOS: Physical sciences

description: In this paper we develop an asymptotic theory for steadily travelling gravity-capillary waves under the small-surface tension limit. In an accompanying work [Shelton et al. (2021), J. Fluid Mech., vol 922] it was demonstrated that solutions associated with a perturbation about a leading-order gravity wave (a Stokes wave) contain surface-tension-driven parasitic ripples with an exponentially-small amplitude. Thus a naive Poincaré expansion is insufficient for their description. Here, we shall develop specialised methodologies in exponential asymptotics for derivation of the parasitic ripples on periodic domains. The ripples are shown to arise in conjunction with Stokes lines and the Stokes phenomenon. The analysis relies crucially upon the derivation and analysis of singularities in the analytic continuation of the classic Stokes wave. A solvability condition is derived, showing that solutions of this type do not exist at certain values of the Bond number. The asymptotic results are compared to full numerical solutions and show excellent agreement. The work provides corrections and insight of a seminal theory on parasitic capillary waves first proposed by Longuet-Higgins [J. Fluid Mech., vol. 16 (1), 1963, pp. 138-159].
description: 35 pages, 13 figures, 1 table
Gravitational Waves from Global Cosmic Strings and Cosmic Archaeology
Creators: name: Chang, Chia-Feng
name: Cui, Yanou
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: General Relativity and Quantum Cosmology (gr-qc)
subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: Global cosmic strings are predicted in many motivated extensions to the Standard Model of particle physics, with close connections to axion dark matter physics. Recent studies suggest that, although subdominant relative to Goldstone emission, gravitational wave (GW) signals from global strings can be detectable with current and planned GW detectors such as LIGO, LISA, DECIGO/BBO, ET/CE and AEDGE/AION, as well as pulsar timing arrays such as PPTA, NANOGrav and SKA. This work is an extensive, updated study on GWs from a global cosmic string network, taking into account of the most recent developments related to the subject. The main analysis is based on the analytical Velocity-dependent One-Scale (VOS) model calibrated with recent simulation results, which provides a generic protocol for such calculations with details given. We also demonstrate how the GW signal can be influenced with variations to the baseline model: this includes considering the uncertainties of model parameters and the potential deviation from the conventional VOS model prediction (i.e. the scaling behavior) as suggested by some of the recent simulation results. Furthermore, we investigated in detail the effect of a non-standard cosmology (e.g. early matter domination or kination) or new particle species on the GW signals from global strings. We demonstrate that the frequency spectrum of GW background from global cosmic strings can be used to probe the cosmic history prior to the Big Bang nucleosynthesis (BBN) (i.e. the primordial dark age) up to a temperature of $T\sim 10^8$ GeV.
description: 34 pages, 16 figures, JHEP published version
Sparse Random Block Matrices
Creators: name: Cicuta, Giovanni M.
name: Pernici, Mario
Publisher: name: arXiv
Year: 2021
Subjects: subject: Mathematical Physics (math-ph)
subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Combinatorics (math.CO)
subject: Probability (math.PR)
subject: FOS: Physical sciences
subject: FOS: Mathematics

description: The spectral moments of ensembles of sparse random block matrices are analytically evaluated in the limit of large order. The structure of the sparse matrix corresponds to the Erdös-Renyi random graph. The blocks are i.i.d. random matrices of the classical ensembles GOE or GUE. The moments are evaluated for finite or infinite dimension of the blocks. The correspondences between sets of closed walks on trees and classes of irreducible partitions studied in free probability together with functional relations are powerful tools for analytic evaluation of the limiting moments. They are helpful to identify probability laws for the blocks and limits of the parameters which allow the evaluation of all the spectral moments and of the spectral density.
description: 31 pages