Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing noise reduction techniques into the OpenQ*D package

Lucius Bushnaq, f ,∗ Isabel Campos, Marco Catillo, Alessandro Cotellucci, Madeleine Dale, Jens Lücke, Marina Krstić Marinković, Agostino Patella, Mike Peardon f and Nazario Tantalo Institut für Theoretische Physik, ETH Zürich, Wolfgang-Pauli-Str. 27, 8093 Zürich, Switzerland Instituto de Física de Cantabria & IFCA-CSIC, Avda. de Los Castros s/n, 39005 Santander, Spain Humboldt Universität zu Berlin, Institut für Physik & IRIS Adlershof, Zum Großen Windkanal 6, 12489 Berlin, Germany Università di Roma Tor Vergata, Dip. di Fisica, Via della Ricerca Scientifica 1, 00133 Rome, Italy INFN, Sezione di Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy f School of Mathematics, Trinity College Dublin, College Green, Dublin 2, Ireland DESY, Platanenallee 6, D-15738 Zeuthen, Germany Department of Physics, University of Cyprus, 1 Panepistimiou Street, 2109 Aglantzia, Nicosia, Cyprus E-mail: bushnaql@tcd.ie, isabel@campos-it.es,
Deciphering dynamics of recent COVID-19 outbreak in India: An age-structured modeling (preprint)/ en

Infectious disease transmission dynamics are particularly sensitive to social contact patterns, and the precautions people take to limit disease transmission. It depends on the age distribution of the community. Thus, knowing the age$-$specific prevalence and incidence of infectious diseases is critical for predicting future disease burden and the efficacy of interventions like immunization. This study uses an SEIR age$-$structured multi-group epidemic model to understand how social contact affects disease control. We created location$-$specific social contact matrices in the community to see how social mixing has affected illness spread. We estimated the basic reproduction number $(R_0)$ of the system and plotted its global behavior in terms of $R_0$. Optimal control for the problem has also been established quantitatively. The proposed model's transmission rate for India from September 1, 2020, to December 31, 2020, has also been estimated. We replicated the lifting of non-pharmaceutical therapies by allowing participants to return to work in phases and studied the impact of this. Our findings imply that identifying symptomatic sick people aged $20-49$ can help lower the number of infected people when schools are closed. When some schools are partially open, awareness of symptomatic infected persons helps reduce cases. The simulation results also suggest that limiting contact at school and other meeting areas could significantly lower the number of instances. Using the least square approach, it was discovered that the time$-$dependent transmission rate is more realistic than the constant spread rate for COVID$-19$ in India. To reduce the COVID$-19$ in burden, we found that gradually loosening control methods could flatten and lower other peaks. Our findings may help health policymakers decide on timely age$-$based immunization distribution strategies and hence control the disease.
Is the Hubble Crisis Connected with the Extinction of Dinosaurs?

It has recently been suggested that a gravitational transition of the effective Newton’s constant Geff by about 10%, 50–150 Myrs ago could lead to the resolution of both the Hubble crisis and the growth tension of the standard ΛCDM model. Hints for such an abrupt transition with weaker gravity at times before the transition, have recently been identified in Tully–Fisher galactic mass-velocity data, and also in Cepheid SnIa calibrator data. Here we use Monte-Carlo simulations to show that such a transition could significantly increase (by a factor of 3 or more) the number of long period comets (LPCs) impacting the solar system from the Oort cloud (semi-major axis of orbits ≳104AU). This increase is consistent with observational evidence from the terrestrial and lunar cratering rates, indicating that the impact flux of kilometer sized objects increased by at least a factor of 2 over that last 100 Myrs compared to the long term average. This increase may also be connected with the Chicxulub impactor event that produced the Cretaceous–Tertiary (K-T) extinction of 75% of life on Earth (including dinosaurs) about 66 Myrs ago. We use Monte-Carlo simulations to show that for isotropic Oort cloud comet distribution with initially circular orbits, random velocity perturbations (induced e.g., by passing stars and/or galactic tidal effects), lead to a deformation of the orbits that increases significantly when Geff increases. A 10% increase in Geff leads to an increase in the probability of the comets to enter the loss cone and reach the planetary region (pericenter of less than 10 AU) by a factor that ranges from 5% (for velocity perturbation much smaller than the comet initial velocity) to more than 300% (for total velocity perturbations comparable with the initial comet velocity).
Automated Heart and Lung Auscultation in Robotic Physical Examinations

This letter presents the first implementation of autonomous robotic auscultation of heart and lung sounds. To select auscultation locations that generate high-quality sounds, a Bayesian Optimization (BO) formulation leverages visual anatomical cues to predict where high-quality sounds might be located, while using auditory feedback to adapt to patient-specific anatomical qualities. Sound quality is estimated online using machine learning models trained on a database of heart and lung stethoscope recordings. Experiments on 4 human subjects show that our system autonomously captures heart and lung sounds of similar quality compared to tele-operation by a human trained in clinical auscultation. Surprisingly, one of the subjects exhibited a previously unknown cardiac pathology that was first identified using our robot, which demonstrates the potential utility of autonomous robotic auscultation for health screening.
How to scale hyperparameters for quickshift image segmentation

Quickshift is a popular algorithm for image segmentation, used as a preprocessing step in many applications. Unfortunately, it is quite challenging to understand the hyperparameters' influence on the number and shape of superpixels produced by the method. In this paper, we study theoretically a slightly modified version of the quickshift algorithm, with a particular emphasis on homogeneous image patches with i.i. d. pixel noise and sharp boundaries between such patches. Leveraging this analysis, we derive a simple heuristic to scale quickshift hyperparameters with respect to the image size, which we check empirically.
Beamforming and Power Allocation for Double-RIS-aided Two-way Directional Modulation Network

To improve the information exchange rate between Alice and Bob in traditional two-way directional modulation (TWDM) network, a new double-reconfigurable intelligent surface (RIS)-aided TWDM network is proposed. To achieve the low-complexity transmitter design, two analytical precoders, one closed-form method of adjusting the RIS phase-shifting matrices, and semi-iterative power allocation (PA) strategy of maximizing secrecy sum rate (SSR) are proposed. First, the geometric parallelogram (GPG) criterion is employed to give the phase-shifting matrices of RISs. Then, two precoders, called maximizing singular value (Max-SV) and maximizing signal-to-leakage-noise ratio (Max-SLNR), are proposed to enhance the SSR. Evenly, the maximizing SSR PA with hybrid iterative closed-form (HICF) is further proposed to improve the SSR and derived to be one root of a sixth-order polynomial computed by: (1) the Newton-Raphson algorithm is repeated twice to reduce the order of the polynomial from six to four; (2) the remaining four feasible solutions can be directly obtained by the Ferrari's method. Simulation results show that using the proposed Max-SV and Max-SLNR, the proposed GPG makes a significant SSR improvement over random phase and no RIS. Given GPG, the proposed Max-SV outperforms the proposed leakage for small-scale or medium-scale RIS. Particularly, the proposed HICF PA stragey shows about ten percent performance gain over equal PA.
Estimation and Hypothesis Testing of Strain-Specific Vaccine Efficacy with Missing Strain Types, with Applications to a COVID-19 Vaccine Trial (preprint)/ en

Statistical methods are developed for analysis of clinical and virus genetics data from phase 3 randomized, placebo-controlled trials of vaccines against novel coronavirus COVID-19. Vaccine efficacy (VE) of a vaccine to prevent COVID-19 caused by one of finitely many genetic strains of SARS-CoV-2 may vary by strain. The problem of assessing differential VE by viral genetics can be formulated under a competing risks model where the endpoint is virologically confirmed COVID-19 and the cause-of-failure is the infecting SARS-CoV-2 genotype. Strain-specific VE is defined as one minus the cause-specific hazard ratio (vaccine/placebo). For the COVID-19 VE trials, the time to COVID-19 is right-censored, and a substantial percentage of failure cases are missing the infecting virus genotype. We develop estimation and hypothesis testing procedures for strain-specific VE when the failure time is subject to right censoring and the cause-of-failure is subject to missingness, focusing on $J \ge 2$ discrete categorical unordered or ordered virus genotypes. The stratified Cox proportional hazards model is used to relate the cause-specific outcomes to explanatory variables. The inverse probability weighted complete-case (IPW) estimator and the augmented inverse probability weighted complete-case (AIPW) estimator are investigated. Hypothesis tests are developed to assess whether the vaccine provides at least a specified level of efficacy against some viral genotypes and whether VE varies across genotypes, adjusting for covariates. The finite-sample properties of the proposed tests are studied through simulations and are shown to have good performances. In preparation for the real data analyses, the developed methods are applied to a pseudo dataset mimicking the Moderna COVE trial.
RISING a new framework for few-view tomographic image reconstruction with deep learning

This paper proposes a new two-step procedure for sparse-view tomographic image reconstruction. It is called RISING, since it combines an early-stopped Rapid Iterative Solver with a subsequent Iteration Network-based Gaining step. So far, regularized iterative methods have widely been used for X-ray computed tomography image reconstruction from low-sampled data, since they converge to a sparse solution in a suitable domain, as upheld by the Compressed Sensing theory. Unfortunately, their use is practically limited by their high computational cost which imposes to perform only a few iterations in the available time for clinical exams. Data-driven methods, using neural networks to post-process a coarse and noisy image obtained from geometrical algorithms, have been recently studied and appreciated for both their computational speed and accurate reconstructions. However, there is no evidence, neither theoretically nor numerically, that neural networks based algorithms solve the mathematical inverse problem modeling the tomographic reconstruction process. In our two-step approach, the first phase executes very few iterations of a regularized model-based algorithm whereas the second step completes the missing iterations by means of a neural network. The resulting hybrid deep-variational framework preserves the convergence properties of the iterative method and, at the same time, it exploits the computational speed and flexibility of a data-driven approach. Experiments performed on a simulated and a real data set confirm the numerical and visual accuracy of the reconstructed RISING images in short computational times.
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches, especially deep neural networks. Recently, the Graph Neural Networks (GNNs) based methods have achieved excellent performance for spatio-temporal forecasting. However, the canonical GNNs-based methods only individually model the local graph of meteorological variables per station or the global graph of whole stations, lacking information interaction between meteorological variables in different stations. In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations. An adaptive graph learning layer and spatial graph convolution are employed to construct self-learning graph and study hidden dependency among nodes of variable-level and station-level graph. For capturing temporal pattern, the dilated inception as the backbone of gate temporal convolution is designed to model long and various meteorological trends. Moreover, a dynamic interaction learning is proposed to build bidirectional information passing in hierarchical graph. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method.
Stochastic asymptotical regularization for linear inverse problems

We introduce stochastic asymptotical regularization (SAR) methods for the uncertainty quantification of the stable approximate solution of ill-posed linear-operator equations, which are deterministic models for numerous inverse problems in science and engineering. We demonstrate that SAR can quantify the uncertainty in error estimates for inverse problems. We prove the regularizing properties of SAR with regard to mean-square convergence. We also show that SAR is an order-optimal regularization method for linear ill-posed problems provided that the terminating time of SAR is chosen according to the smoothness of the solution. This result is proven for both a priori and a posteriori stopping rules under general range-type source conditions. Furthermore, some converse results of SAR are verified. Two iterative schemes are developed for the numerical realization of SAR, and the convergence analyses of these two numerical schemes are also provided. A toy example and a real-world problem of biosensor tomography are studied to show the accuracy and the advantages of SAR: compared with the conventional deterministic regularization approaches for deterministic inverse problems, SAR can provide the uncertainty quantification of the quantity of interest, which can in turn be used to reveal and explicate the hidden information about real-world problems, usually obscured by the incomplete mathematical modeling and the ascendence of complex-structured noise.
Cobol2Vec: Learning Representations of Cobol code

There has been a steadily growing interest in development of novel methods to learn a representation of a given input data and subsequently using them for several downstream tasks. The field of natural language processing has seen a significant improvement in different tasks by incorporating pre-trained embeddings into their pipelines. Recently, these methods have been applied to programming languages with a view to improve developer productivity. In this paper, we present an unsupervised learning approach to encode old mainframe languages into a fixed dimensional vector space. We use COBOL as our motivating example and create a corpus and demonstrate the efficacy of our approach in a code-retrieval task on our corpus.
Supported Sets - A New Foundation For Nominal Sets And Automata

The present work proposes and discusses the category of supported sets which provides a uniform foundation for nominal sets of various kinds, such as those for equality symmetry, for the order symmetry, and renaming sets. We show that all these differently flavoured categories of nominal sets are monadic over supported sets. Thus, supported sets provide a canonical finite way to represent nominal sets and the automata therein, e.g. register automata. Name binding in supported sets is modelled by a functor following the idea of de Bruijn indices. This functor lifts to the well-known abstraction functor in nominal sets. Together with the monadicity result, this gives rise to a transformation process that takes the finite representation of a register automaton in supported sets and transforms it into its configuration automaton in nominal sets.
A Channel to Form Fast-spinning Black Hole–Neutron Star Binary Mergers as Multimessenger Sources

After the successful detection of a gravitational-wave (GW) signal and its associated electromagnetic (EM) counterparts from GW170817, neutron star–black hole (NSBH) mergers have been highly expected to be the next type of multimessenger source. However, despite the detection of several NSBH merger candidates during the GW third observation run, no confirmed EM counterparts from these sources have been identified. The most plausible explanation is that these NSBH merger candidates were plunging events mainly because the primary black holes (BHs) had near-zero projected aligned spins based on GW observations. In view of the fact that neutron stars (NSs) can be easily tidally disrupted by BHs with high projected aligned spins, we study an evolution channel to form NSBH binaries with fast-spinning BHs, the properties of BH mass and spin, and their associated tidal disruption probability. We find that if the NSs are born first, the companion helium stars would be tidally spun up efficiently, and would thus finally form fast-spinning BHs. If BHs do not receive significant natal kicks at birth, these NSBH binaries that can merge within Hubble time would have BHs with projected aligned spins χ z ≳ 0.8 and, hence, can certainly allow tidal disruption to happen. Even if significant BH kicks are considered for a small fraction of NSBH binaries, the projected aligned spins of BHs are χ z ≳ 0.2. These systems can still be disrupted events unless the NSs are very massive. Thus, NS-first-born NSBH mergers would be promising multimessenger sources. We discuss various potential EM counterparts associated with these systems and their detectability in the upcoming fourth observation run.
Sketch2PQ: Freeform Planar Quadrilateral Mesh Design via a Single Sketch

The freeform architectural modeling process often involves two important stages: concept design and digital modeling. In the first stage, architects usually sketch the overall 3D shape and the panel layout on a physical or digital paper briefly. In the second stage, a digital 3D model is created using the sketch as a reference. The digital model needs to incorporate geometric requirements for its components, such as the planarity of panels due to consideration of construction costs, which can make the modeling process more challenging. In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes. Our system allows the user to sketch the surface boundary and contour lines under axonometric projection and supports the sketching of occluded regions. In addition, the user can sketch feature lines to provide directional guidance to the PQ mesh layout. Given the 2D sketch input, we propose a deep neural network to infer in real-time the underlying surface shape along with a dense conjugate direction field, both of which are used to extract the final PQ mesh. To train and validate our network, we generate a large synthetic dataset that mimics architect sketching of freeform quadrilateral patches. The effectiveness and usability of our system are demonstrated with quantitative and qualitative evaluation as well as user studies.
DuVisor: a User-level Hypervisor Through Delegated Virtualization

Today's mainstream virtualization systems comprise of two cooperative components: a kernel-resident driver that accesses virtualization hardware and a user-level helper process that provides VM management and I/O virtualization. However, this virtualization architecture has intrinsic issues in both security (a large attack surface) and performance. While there is a long thread of work trying to minimize the kernel-resident driver by offloading functions to user mode, they face a fundamental tradeoff between security and performance: more offloading may reduce the kernel attack surface, yet increase the runtime ring crossings between the helper process and the driver, and thus more performance cost. This paper explores a new design called delegated virtualization, which completely separates the control plane (the kernel driver) from the data plane (the helper process) and thus eliminates the kernel driver from runtime intervention. The resulting user-level hypervisor, called DuVisor, can handle all VM operations without trapping into the kernel once the kernel driver has done the initialization. DuVisor retrofits existing hardware virtualization support with a new delegated virtualization extension to directly handle VM exits, configure virtualization registers, manage the stage-2 page table and virtual devices in user mode. We have implemented the hardware extension on an open-source RISC-V CPU and built a Rust-based hypervisor atop the hardware. Evaluation on FireSim shows that DuVisor outperforms KVM by up to 47.96\% in a variety of real-world applications and significantly reduces the attack surface.
Spectroscopic Observations of V455 Andromedae Superoutburst in 2007: the Most Exotic Spectral Features in Dwarf Nova Outbursts

We present our spectroscopic observations of V455 Andromedae during the 2007 superoutburst. Our observations cover this superoutburst from around the optical peak of the outburst to the post-superoutburst stage. During the early superhump phase, the emission lines of Balmer series, He I, He II, Bowen blend, and C IV / N IV blend were detected. He II 4686 line exhibited a double-peaked emission profile, where Balmer emission lines were single-peaked, which is unexpected from its high inclination. In the ordinary superhump phase, Balmer series transitioned to double-peaked emission profiles, and high-ionization lines were significantly weakened. These transitions of the line profiles should be related to the structural transformation of the accretion disk, as expected between the early and ordinary superhump transition in the thermal-tidal instability model. The Doppler map of H$\alpha$ during the early superhump phase exhibits a compact blob centered at the primary white dwarf. In analogy to SW Sex-type cataclysmic variables, this feature could emerge from the disk wind and/or the mass accretion column onto the magnetized white dwarf. The Doppler map of He II 4686 \AA~ is dominated by the ring-like structure and imposed two flaring regions with the velocity of $\sim$300 km/s, which is too slow for a Keplerian accretion disk. The phase of the flaring regions was coincident with the inner spiral arm structure identified during the early superhump phase. Our disk wind model with the enhanced emission from the inner arm structure successfully reproduced the observed properties of He II 4686 \AA. Therefore, V455 And is the first case in dwarf nova outbursts that the presence of the disk wind is inferred from an optical spectrum.