Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotics of twisted Alexander polynomials and hyperbolic volume

For a hyperbolic knot and a natural number n, we consider the Alexander polynomial twisted by the n-th symmetric power of a lift of the holonomy. We establish the asymptotic behavior of these twisted Alexander polynomials evaluated at unit complex numbers, yielding the volume of the knot exterior. More generally, we prove the asymptotic behavior for cusped hyperbolic manifolds of finite volume. The proof relies on results of Muller, and Menal-Ferrer and the last author. Using the uniformity of the convergence, we also deduce a similar asymptotic result for the Mahler measures of those polynomials.
Modeling Neural Architecture Search Methods for Deep Networks

There are many research works on the designing of architectures for the deep neural networks (DNN), which are named neural architecture search (NAS) methods. Although there are many automatic and manual techniques for NAS problems, there is no unifying model in which these NAS methods can be explored and compared. In this paper, we propose a general abstraction model for NAS methods. By using the proposed framework, it is possible to compare different design approaches for categorizing and identifying critical areas of interest in designing DNN architectures. Also, under this framework, different methods in the NAS area are summarized; hence a better view of their advantages and disadvantages is possible.
Moduli problems for operadic algebras

A theorem of Pridham and Lurie provides an equivalence between formal moduli problems and Lie algebras in characteristic zero. We prove a generalization of this correspondence, relating formal moduli problems parametrized by algebras over a Koszul operad to algebras over its Koszul dual operad. In particular, when the Lie algebra associated to a deformation problem is induced from a pre‐Lie structure, it corresponds to a permutative formal moduli problem. As another example, we obtain a correspondence between operadic formal moduli problems and augmented operads.
Search Based Repair of Deep Neural Networks

Deep Neural Networks (DNNs) are being adopted in various domains, including safety critical ones. The wide-spread adoption also calls for ways to guide the testing of their accuracy and robustness, for which various test adequacy criteria and input generation methods have been recently introduced. In this paper, we explore the natural subsequent step: given an input that reveals unexpected behaviour in a trained DNN, we propose to repair the DNN using input-output pairs as a specification. This paper introduces Arachne, a novel program repair technique for DNNs. Arachne first performs sensitivity based fault localisation to limit the number of neural weights it has to modify. Subsequently, Arachne uses Particle Swarm Optimisation (PSO) to directly optimise the localised neural weights until the behaviour is corrected. An empirical study using three different benchmark datasets shows that Arachne can reduce the instances of the most frequent misclassification type committed by a pre-trained CIFAR-10 classifier by 27.5%, without any need for additional training data. Patches generated by Arachne tend to be more focused on the targeted misbehaviour than DNN retraining, which is more disruptive to non-targeted behaviour. The overall results suggest the feasibility of patching DNNs using Arachne until they can be retrained properly.
Robust Cross-lingual Embeddings from Parallel Sentences

Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation. However, these approaches assume word embedding spaces are isomorphic between different languages, which has been shown not to hold in practice (Sogaard et al., 2018), and fundamentally limits their performance. This motivates investigating joint learning methods which can overcome this impediment, by simultaneously learning embeddings across languages via a cross-lingual term in the training objective. We propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word and sentence representations. Our approach significantly improves cross-lingual sentence retrieval performance over all other approaches while maintaining parity with the current state-of-the-art methods on word-translation. It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task, requiring far fewer computational resources for training and inference. As an additional advantage, our bilingual method leads to a much more pronounced improvement in the the quality of monolingual word vectors compared to other competing methods.
Horizonless ultracompact objects and dark matter in quadratic gravity

We show that in quadratic gravity sufficiently light objects must be horizonless and construct explicit analytic examples of horizonless ultracompact objects (UCOs), which are more compact than Schwarzschild black holes. Due to the quadratic terms, gravity becomes soft and eventually vanishes in the high-energy limit leading to a “linearization mechanism”: light objects can be described by the linearized theory when their Schwarzschild radius is smaller than the Compton wavelength of the new gravitational degrees of freedom. As a result, we can analytically describe UCOs with a mass-to-radius ratio higher than for a Schwarzschild black hole. The corresponding spacetime is regular everywhere. We show that the Ostrogradsky instabilities can be avoided and discuss the relation with the Higgs vacuum metastability. Due to the lack of a horizon, light UCOs do not evaporate. Therefore, they may play the role of dark matter. We briefly discuss their phenomenology.
I'm a doctor, not a mathematician! Homeostasis as a proportional-integral control system

The distinction between "healthy" and "unhealthy" patients is commonly based on single, discrete values taken at an isolated point in time (e.g., blood pressure or core temperature). Perhaps a more robust and insightful diagnosis can be obtained by studying the functional interdependence of such indicators and the homeostasis that controls them. This requires quasi-continuous measurements and a procedure to map the data onto a parsimonious control model with a degree of universality. The current research illustrates this approach using glucose homeostasis as a target. Data were obtained from 41 healthy subjects wearing over-the-counter glucose monitors, and projected onto a simple proportional-integral (PI) controller, widely used in engineering applications. The indicators quantifying the control function are clustered for the great majority of subjects, while a few outliers exhibit less efficient homeostasis. Practical implications for healthcare and education are further discussed.
Iridium 
5d
-electron driven superconductivity in 
ThIr3

Polycrystalline sample of superconducting ThIr$_{3}$ was obtained by arc-melting Th and Ir metals. Powder x-ray diffraction revealed that the compound crystalizes in a rhombohedral crystal structure (R-3m, s.g. no. 166) with the lattice parameters: a = 5.3394(1) $A$ and c = 26.4228(8) $A$. Normal and superconducting states were studied by magnetic susceptibility, electrical resistivity and heat capacity measurements. The results showed that ThIr$_{3}$ is a type II superconductor (Ginzburg-Landau parameter $\kappa$ = 38) with the critical temperature T$_{c}$ = 4.41 K. The heat capacity data yielded the Sommerfeld coefficient $\gamma$ = 17.6 mJ mol$^{-1}$ K$^{-2}$ and the Debye temperature $\Theta_{D}$ = 169 K. The ratio $\Delta$C / ($\gamma$ T$_{c}$) = 1.6, where $\Delta$C stands for the specific heat jump at T$_{c}$, and the electron-phonon coupling constant $\lambda_{e-p}$ = 0.74 suggest that ThIr$_{3}$ is a moderate-strength superconductor. The experimental studies were supplemented by band structure calculations, which indicated that the superconductivity in ThIr$_{3}$ is governed mainly by 5d states of iridium. The significantly smaller band-structure value of Sommerfeld coefficient as well as the experimentally observed quadratic temperature dependence of resistivity and enhanced magnetic susceptibility suggest presence of electronic interactions in the system, which compete with superconductivity.
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms

In recent years, as intrusion attacks on IoT networks have grown exponentially, there is an immediate need for sophisticated intrusion detection systems (IDSs). A vast majority of current IDSs are data-driven, which means that one of the most important aspects of this area of research is the quality of the data acquired from IoT network traffic. Two of the most cited intrusion detection datasets are the KDDCup99 and the NSL-KDD. The main goal of our project was to conduct a robust comparison of both datasets by evaluating the performance of various Machine Learning (ML) classifiers trained on them with a larger set of classification metrics than previous researchers. From our research, we were able to conclude that the NSL-KDD dataset is of a higher quality than the KDDCup99 dataset as the classifiers trained on it were on average 20.18% less accurate. This is because the classifiers trained on the KDDCup99 dataset exhibited a bias towards the redundancies within it, allowing them to achieve higher accuracies.
A second generation constrained acetabular component for treatment of complex hip replacement instability

Background:Hip instability can be anticipated in rare cases of primary hip replacement and is a common cause of failed total hip arthroplasty (THA). One approach to hip instability is THA using a constrained acetabular component. Methods:Twenty THAs were performed in 18 patients. A constrained acetabular liner (Trilogy® Longevity®, Zimmer Inc. Warsaw, IN) was implanted to treat anticipated or prior hip instability. Results:At an average of 28.1 mo, none of the patients had experienced a dislocation of the constrained implant. Conclusions:The Trilogy® constrained acetabular liner is a viable option to treat patients with recurrent dislocations or complex hip instability.
The Impact of Negation on the Complexity of the Shapley Value in Conjunctive Queries

The Shapley value is a conventional and well-studied function for determining the contribution of a player to the coalition in a cooperative game. Among its applications in a plethora of domains, it has recently been proposed to use the Shapley value for quantifying the contribution of a tuple to the result of a database query. In particular, we have a thorough understanding of the tractability frontier for the class of Conjunctive Queries (CQs) and aggregate functions over CQs. It has also been established that a tractable (randomized) multiplicative approximation exists for every union of CQs. Nevertheless, all of these results are based on the monotonicity of CQs. In this work, we investigate the implication of negation on the complexity of Shapley computation, in both the exact and approximate senses. We generalize a known dichotomy to account for negated atoms. We also show that negation fundamentally changes the complexity of approximation. We do so by drawing a connection to the problem of deciding whether a tuple is "relevant" to a query, and by analyzing its complexity.
Deligne–Lusztig varieties and basic EKOR strata

Abstract Using the axioms of He and Rapoport for the stratifications of Shimura varieties, we explain a result of Görtz, He, and Nie that the EKOR strata contained in the basic loci can be described as a disjoint union of Deligne–Lusztig varieties. In the special case of Siegel modular varieties, we compare their descriptions to that of Görtz and Yu for the supersingular Kottwitz-Rapoport strata and to the descriptions of Harashita and Hoeve for the supersingular Ekedahl–Oort strata.
Spiked sample covariance matrices with possibly multiple bulk components

In this paper, we study the convergent limits and rates of the eigenvalues and eigenvectors for spiked sample covariance matrices whose spectrum can have multiple bulk components. Our model is an extension of Johnstone’s spiked covariance matrix model. Based on our results, we can extend many statistical applications based on Johnstone’s spiked covariance matrix model.
A vector-valued almost sure invariance principle for random hyperbolic and piecewise-expanding maps

We obtain a quenched vector-valued almost sure invariance principle (ASIP) for large classes of random dynamical systems exhibiting some degree of hyperbolicity. More precisely, we consider random perturbations of a fixed Anosov diffeomorphism as well as random perturbations of a billiard map associated to the periodic Lorentz gas. We also deal with wide classes of piecewise expanding maps both in one and higher dimensions. Our proofs are based on a modification of the spectral method for establishing ASIP introduced by S. Gouezel. Finally, by using martingale techniques, we revisit the quenched scalar-valued ASIP for random piecewise-expanding dynamics and improve the known error estimates.
Field model for complex ionic fluids: analytical properties and numerical investigation

In this paper, we consider the field model for complex ionic fluids with an energy variational structure, and analyze the well-posedness to this model with regularized kernels. Furthermore, we deduce the estimate of the maximal density function to quantify the finite size effect. On the numerical side, we adopt a finite volume scheme to the field model, which satisfies the following properties: positivity-preserving, mass conservation and energy dissipation. Besides, series of numerical experiments are provided to demonstrate the properties of the steady state and the finite size effect by showing the equilibrium profiles with different values of the parameter in the kernel.
Monte Carlo Characterization of the Cosmic Ray Muon Flux in Subsurface Geological Repositories.

Recent challenges in monitoring subsurface geological repositories call for new, innovative concepts that are facility independent, cost-effective, passive, and reliable. Inspection and verification of future disposal facilities will exert significant pressure on the limited safeguards resources. Compared to aboveground facilities, subsurface geological repositories cannot be directly monitored. Once nuclear material is in place in these facilities, reverifying the inventory may no longer be feasible if continuity of knowledge is lost or updated safeguards information on the contents (or lack thereof) becomes available to inspectors. Using cosmic ray muons presents several potential advantages over conventional photon/neutron signatures, and their use in safeguards applications has only recently received attention. However, there have been limited efforts to explore the integration of cosmic ray muons into repository safeguards and study potential gains, risks, and costs. This paper presents a Monte Carlo-based methodology to characterize the cosmic ray muon flux, including muon angular and energy differential distributions at depths representative of geological repositories. This work discusses the feasibility of muon monitoring for detecting spent nuclear fuel disposal cask movement or for unauthorized excavation and rock removal. The objective is to develop useful parametrizations to provide a convenient tool for detector-specific and safeguards applications at any geological repository site. It is expected these results will provide a better understanding of how muons can be integrated into an existing geological repository safeguards framework.
Deformation monitoring and analysis of the thermal expansion of high-rise buildings in Shanghai Lujiazui using tomography-based persistent scatterer interferometry

Abstract. We utilize a tomography-based persistent scatterer interferometric synthetic aperture radar (Tomo-PSInSAR) approach for monitoring the deformation performance and analysis of the thermal expansion of 33 high-rise buildings in Shanghai Lujiazui. For the purpose of this study, we use 31 stripmap acquisitions from TerraSAR-X missions spanning from December 2009 to February 2013. Considering that thermal expansion, concrete creep, and shrinkage are three long-term movements that occur in high-rise buildings with concrete structures, we use an extended 4-D SAR phase model, and three parameters (height, deformation velocity, and thermal amplitude) are estimated simultaneously. Additionally, we apply a two-tier network strategy to detect single and double PSs with no need for preliminary removal of the atmospheric phase screen in the study area. The thermal amplitude map demonstrates that the derived thermal amplitude of the buildings is highly related to the building height due to the upward accumulative effect of thermal expansion. The deformation velocity map reveals that the Shanghai World Financial Center is subject to a great deal of deformation during the newly built period due to concrete creep and shrinkage effects, which are height-dependent downward movements. In addition, the deformation rates caused by concrete creep and shrinkage are largest at the beginning and gradually decrease, finally achieving a steady state as time goes to infinity. In contrast, the deformation velocity map shows that the Jin Mao Tower is almost stable during this period. We observe a negative correlation between deformation characteristics and the time of completion of high-rise buildings, inferring that the newly built concrete buildings are more sensitive to concrete creep and shrinkage effects than the older concrete buildings. Our study emphasizes the potential of the Tomo-PSInSAR solution for monitoring the deformation performance and analysis of the thermal expansion of high-rise buildings.
Assessment of MODIS thermal emissive bands calibration performance using deep convective clouds

Abstract. The core of deep convective clouds (DCC) is one of the most consistent Earth targets. When viewed from space, the measurements have minimal impact from water vapor and aerosols and have been used as invariant scenes for calibration assessment for reflectance solar bands. DCC is also one of the coldest targets and can be used for assessing the calibration and product stability for infrared thermal emissive bands (TEB). The stability from the years 2003 to 2019 is analyzed for both Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) long-wave infrared bands. Most bands exhibit very stable long-term trending. Terra band 30 shows the largest rate of change of 0.19 K / year. The Terra–Aqua difference is also analyzed. In general, for both Terra and Aqua, the long-wave infrared bands show good and stable detector uniformity. The impact of response change due to anomalies and events has been analyzed. Since the measurement bias over cold DCC is dominated by the offset term in the calibration equation, an assessment model is developed to estimate the offset bias between the mirror sides. The impact model is also developed to estimate the impact on higher brightness temperature measurements. The offset bias from the assessment modeling in this analysis is used as the input for impact modeling. The measurements over Dome-C, Ocean, and desert scenes are used to verify the impact model. These assessments are very helpful for MODIS TEB calibration and look-up table update procedure improvements.
Temperature dependence of quantum efficiency enhancement using plasmonic gratings on nBn detectors with thin absorbers

Abstract. For diffusion-limited nBn detectors, using an absorption layer much thinner than the optical attenuation length and minority carrier diffusion length can improve the dark current to provide greater sensitivity or higher temperature operation. However, if the quantum efficiency (QE) also decreases with absorber thickness, the advantage of reduced dark current is eliminated. We discuss the use of a metallic grating to couple the incident light into laterally propagating surface plasmon polariton (SPP) modes and increase the effective absorption length. We fabricate the gratings using a deposited Ge layer, which provides a uniform profile without increasing the dark current. Using this process in conjunction with a 0.5-μm-thick InAsSb absorber lattice-matched to GaSb, we demonstrate an external QE of 34% for T = 78 to 240 K. An nBn structure with an InAs0.8Sb0. 2 absorber that is grown metamorphically on GaSb using a step-graded InGaSb buffer has a peak external QE of 39% at 100 K, which decreases to 32% by 240 K. Finally, we demonstrate that a grating with SPP resonance near the bandgap extends the absorption band and can potentially reduce the dark current by another factor of 3 to 8 times in addition to the 5 × reduction due to the thinner absorber.
Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring

Abstract. Major calcifications are of great concern when performing percutaneous coronary interventions because they inhibit proper stent deployment. We created a comprehensive software to segment calcifications in intravascular optical coherence tomography (IVOCT) images and to calculate their impact using the stent-deployment calcification score, as reported by Fujino et al. We segmented the vascular lumen and calcifications using the pretrained SegNet, convolutional neural network, which was refined for our task. We cleaned segmentation results using conditional random field processing. We evaluated the method on manually annotated IVOCT volumes of interest (VOIs) without lesions and with calcifications, lipidous, or mixed lesions. The dataset included 48 VOIs taken from 34 clinical pullbacks, giving a total of 2640 in vivo images. Annotations were determined from consensus between two expert analysts. Keeping VOIs intact, we performed 10-fold cross-validation over all data. Following segmentation noise cleaning, we obtained sensitivities of 0.85 ± 0.04, 0.99 ± 0.01, and 0.97 ± 0.01 for calcified, lumen, and other tissue classes, respectively. From segmented regions, we automatically determined calcification depth, angle, and thickness attributes. Bland–Altman analysis suggested strong correlation between manually and automatically obtained lumen and calcification attributes. Agreement between manually and automatically obtained stent-deployment calcification scores was good (four of five lesions gave exact agreement). Results are encouraging and suggest our classification approach could be applied clinically for assessment and treatment planning of coronary calcification lesions.