Miracle-dz/UniFAR-FTUs huggingface dataset:
The usability of the ratio of bi-humerus breadth to maximum pelvic breadth in sex estimation

Abstract Background Although sex can be determined with high accuracy in forensic anthropology, additional parameters are still required. Aim To estimate with known simple statistical methods, the usability of the bi-humerus/maximum pelvic breadth ratio in sex estimation. Subjects and methods Bi-humerus breadth and maximum pelvic breadth were measured using the topogram images (196 males, 171 females), the ratio between them was calculated. We examined the usability of the ratio of the distance between the lateral edges of the right and left humeral heads to the maximum distance between the two most lateral parts of the iliac crests in sex estimation. Results There was significant difference in the bi-humerus breadth and “bi-humerus breadth/maximum pelvic breadth” according to sex. The greatest breadth of the pelvis was higher in females, yet the difference was not statistically significant. The ratio yielded 80.6%−90.3% accuracy for females and 73.6%–74.7% for males, depending on arm position. Conclusions The obtained data may contribute to the development of formulas created with metric measurements used in sex estimation. This can be used as a parameter to help in estimating the sex of skeletal remains found as a whole or excavated without losing their integrity, and also in the reconstruction of body structure.
Noise sensitivity of random walks on groups

A random walk on a group is noise sensitive if resampling every step independantly with a small probability results in an almost independant output. We precisely define two notions: $\ell^1$-noise sensitivity and entropy noise sensitivity. Groups with one of these properties are necessarily Liouville. Homomorphisms to free abelian groups provide an obstruction to $\ell^1$-noise sensitivity. We also provide examples of $\ell^1$ and entropy noise sensitive random walks. Noise sensitivity raises many open questions which are described at the end of the paper.
Ends of immersed minimal and Willmore surfaces in asymptotically flat spaces

We study ends of an oriented, immersed, non-compact, complete Willmore surfaces, which are critical points of the integral of the square of the mean curvature, in asymptotically flat spaces of any dimension; assuming the surface has $L^2$-bounded second fundamental form and satisfies a weak power growth on the area. We give the precise asymptotic behavior of an end of such a surface. This asymptotic information is very much dependent on the way the ambient metric decays to the Euclidean one. Our results apply in particular to minimal surfaces.
The environmental history of group and cluster galaxies in a Λ cold dark matter universe

We use publicly available galaxy merger trees, obtained applying semi-analytic techniques to a large high-resolution cosmological simulation, to study the environmental history of group and cluster galaxies. Our results highlight the existence of an intrinsic history bias which makes the nature versus nurture (as well as the mass versus environment) debate inherently ill posed. In particular, we show that (i) surviving massive satellites were accreted later than their less massive counterparts, from more massive haloes and (ii) the mixing of galaxy populations is incomplete during halo assembly, which creates a correlation between the time a galaxy becomes satellite and its present distance from the parent halo centre. The weakest trends are found for the most massive satellites, as a result of efficient dynamical friction and late formation times of massive haloes. A large fraction of the most massive group/cluster members are accreted on to the main progenitor of the final halo as central galaxies, while about half of the galaxies with low and intermediate stellar masses are accreted as satellites. Large fractions of group and cluster galaxies (in particular those of low stellar mass) have therefore been ‘pre-processed’ as satellites of groups with mass ∼1013 M⊙. To quantify the relevance of hierarchical structure growth on the observed environmental trends, we have considered observational estimates of the passive galaxy fractions and their variation as a function of halo mass and clustercentric distance. Comparisons with our theoretical predictions require relatively long times (∼5–7 Gyr) for the suppression of star formation in group and cluster satellites. It is unclear how such a gentle mode of strangulation can be achieved by simply relaxing the assumption of instantaneous stripping of the hot gas reservoir associated with accreting galaxies, or if the difficulties encountered by recent galaxy formation models in reproducing the observed trends signal a more fundamental problem with the treatment of star formation and feedback in these galaxies.
Analytic Torsion, 3d Mirror Symmetry And Supergroup Chern-Simons Theories

We consider topological field theories that compute the Reidemeister-Milnor-Turaev torsion in three dimensions. These are the psl(1|1) and the U(1|1) Chern-Simons theories, coupled to a background complex flat gauge field. We use the 3d mirror symmetry to derive the Meng-Taubes theorem, which relates the torsion and the Seiberg-Witten invariants, for a three-manifold with arbitrary first Betti number. We also present the Hamiltonian quantization of our theories, find the modular transformations of states, and various properties of loop operators. Our results for the U(1|1) theory are in general consistent with the results, found for the GL(1|1) WZW model. We also make some comments on more general supergroup Chern-Simons theories.
The geometry on smooth toroidal compactifications of Siegel varieties

We study smooth toroidal compactifications of Siegel varieties thoroughly from the view- points of Hodge theory and Kahler-Einstein metric. We observe that any cusp of a Siegel space can be identified as a set of certain weight one polarized mixed Hodge structures. We then study the in- finity boundary divisors of toroidal compactifications, and obtain a global volume form formula of an arbitrary smooth Siegel variety Ag,Γ( g> 1) with a smooth toroidal compactification Ag,Γ such that D∞ := Ag,Γ \ Ag,Γ is normal crossing. We use this volume form formula to show that the unique group-invariant Kahler-Einstein metric on Ag,Γ endows some restraint combinatorial conditions for all smooth toroidal compactifications of Ag,Γ. Again using the volume form formula, we study the asymptotic behavior of logarithmical canonical line bundle on any smooth toroidal compactification of Ag,Γ carefully and we obtain that the logarithmical canonical bundle degenerate sharply even though it is big and numerically effective.
A Spectral Solver for Solar Inertial Waves

Inertial waves, which are predominantly driven by the Coriolis force, likely play an important role in solar dynamics, and, additionally, they provide a window into the solar subsurface. The latter allows us to infer properties that are inaccessible to the traditional technique of acoustic wave helioseismology. Thus, a full characterization of these normal modes holds the promise of enabling investigations into solar subsurface dynamics. In this work, we develop a spectral eigenvalue solver to model the spectrum of inertial waves in the Sun. We model the solar convection zone as an anelastic medium, and solve for the normal modes of the momentum and energy equations. We demonstrate that the solver can well reproduce the observed mode frequencies and line widths, not only of sectoral Rossby modes, but also of recently observed high-frequency inertial modes. In addition, we believe that the spectral solver is a useful contribution to the numerical methods of modeling inertial modes on the Sun.
Reward-Predictive Clustering

Recent advances in reinforcement-learning research have demonstrated impressive results in building algorithms that can out-perform humans in complex tasks. Nevertheless, creating reinforcement-learning systems that can build abstractions of their experience to accelerate learning in new contexts still remains an active area of research. Previous work showed that reward-predictive state abstractions fulﬁll this goal, but have only be applied to tabular settings. Here, we provide a clustering algorithm that enables the application of such state abstractions to deep learning settings, providing compressed representations of an agent’s inputs that preserve the ability to predict sequences of reward. A convergence theorem and simulations show that the resulting reward-predictive deep network maximally compresses the agent’s inputs, signiﬁcantly speeding up learning in high dimensional visual control tasks. Furthermore, we present diﬀerent generalization experiments and analyze under which conditions a pre-trained reward-predictive representation network can be re-used without re-training to accelerate learning—a form of systematic out-of-distribution transfer.
Influence of chiral chemical potential, parallel electric, and magnetic fields on the critical temperature of QCD

We study the influence of external electric, $E$, and magnetic, $B$, fields parallel to each other, and of a chiral chemical potential, $\mu_5$, on the chiral phase transition of Quantum Chromodynamics. Our theoretical framework is a Nambu-Jona-Lasinio model with a contact interaction. Within this model we compute the critical temperature of chiral symmetry restoration, $T_c$, as a function of the chiral chemical potential and field strengths. We find that the fields inhibit and $\mu_5$ enhances chiral symmetry breaking, in agreement with previous studies.
Herschel : the first science highlights Special feature L etter to the E ditor Herschel observations of EXtraOrdinary Sources ( HEXOS ) : Detection of hydrogen fluoride in absorption towards Orion KL

We report a detection of the fundamental rotational transition of hydrogen fluoride in absorption towards Orion KL using Herschel/HIFI. After the removal of contaminating features associated with common molecules (“weeds”), the HF spectrum shows a P-Cygni profile, with weak redshifted emission and strong blue-shifted absorption, associated with the low-velocity molecular outflow. We derive an estimate of 2.9 × 1013 cm−2 for the HF column density responsible for the broad absorption component. Using our best estimate of the H2 column density within the low-velocity molecular outflow, we obtain a lower limit of ∼1.6 × 10−10 for the HF abundance relative to hydrogen nuclei, corresponding to ∼0.6% of the solar abundance of fluorine. This value is close to that inferred from previous ISO observations of HF J = 2−1 absorption towards Sgr B2, but is in sharp contrast to the lower limit of 6 × 10−9 derived by Neufeld et al. for cold, foreground clouds on the line of sight towards G10.6-0.4.
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing tech-nique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires ex-pert knowledge. We provide simpliﬁed, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We ﬁnd that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions. Code is made publicly available at https://github.com/iprapas/landslide-sar-unet .
Universal distribution of Lyapunov exponents for products of Ginibre matrices

Starting from exact analytical results on singular values and complex eigenvalues of products of independent Gaussian complex random N × N matrices, also called the Ginibre ensemble, we rederive the Lyapunov exponents for an infinite product. We show that for a large number t of product matrices, the distribution of each Lyapunov exponent is normal, and we compute its t-dependent variance as well as corrections in a large-t expansion. Originally Lyapunov exponents are defined for the singular values of the product matrix that represents a linear time evolution. Surprisingly a similar construction for the moduli of the complex eigenvalues yields the very same exponents and normal distributions to leading order. We discuss a general mechanism for 2 × 2 matrices showing why the singular values and the radii of complex eigenvalues collapse onto the same value in the large-t limit. We thereby rederive Newmanʼs triangular law which has a simple interpretation as the radial density of complex eigenvalues in the circular law, and study the commutativity of the two limits t → ∞ ? > and N → ∞ ? > on the global scale and on the local scale. We show as a mathematical byproduct that a particular asymptotic expansion of a Meijer G-function with large index leads to a Gaussian.
On learning history based policies for controlling Markov decision processes

Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state abstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP. However, there has been little formal analysis of such history-based algorithms, as most existing frameworks focus exclusively on memory-less features. In this paper, we introduce a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction mappings. Furthermore, we use this framework to design a practical RL algorithm and we numerically evaluate its effectiveness on a set of continuous control tasks.
Random non-Abelian G-circulant matrices. Spectrum of random convolution operators on large finite groups

We analyze the limiting behavior of the eigenvalue and singular value distribution for random convolution operators on large (not necessarily Abelian) groups, extending the results by Meckes for the Abelian case. We show that for regular sequences of groups, the limiting distribution of eigenvalues (respectively singular values) is a mixture of eigenvalue (respectively singular value) distributions of Ginibre matrices with the directing measure being related to the limiting behavior of the Plancherel measure of the sequence of groups. In particular, for the sequence of symmetric groups, the limiting distributions are just the circular and quarter circular laws, whereas e.g. for the dihedral groups, the limiting distributions have unbounded supports but are different than in the Abelian case. We also prove that under additional assumptions on the sequence of groups (in particular, for symmetric groups of increasing order) families of stochastically independent random convolution operators converge in moments to free circular elements. Finally, in the Gaussian case, we provide Central Limit Theorems for linear eigenvalue statistics.
MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation

In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new domain with a different distribution than its training set. Domain Adaptation (DA) is used to mitigate this problem. One approach of existing DA algorithms is to ﬁnd domain invariant features whose distributions in the source domain are the same as their distribution in the target domain. In this paper, we propose to let the classiﬁer that performs the ﬁnal classiﬁ- cation task on the target domain learn implicitly the invariant features to perform classiﬁcation. It is achieved via feeding the classiﬁer during training generated fake samples that are similar to samples from both the source and target domains. We call these generated samples domain agnostic samples. To accomplish this we propose a novel variation of genera- tive adversarial networks (GAN), called the MiddleGAN, that generates fake samples that are similar to samples from both the source and target domains, using two discriminators and one generator. We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empir- ically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and sam- ples from the target domain. We conducted extensive evaluations using 24 benchmarks; on the 24 benchmarks, we com- pare MiddleGAN against various state-of-the-art algorithms and outperform the state-of-the-art by up to 20.1% on certain benchmarks.
Transverse momentum cross section of $e^+e^-$ pairs in the $Z$-boson region from $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV

The transverse momentum cross section of $e^+e^-$ pairs in the $Z$-boson mass region of 66-116 GeV/$c^2$ is precisely measured using Run II data corresponding to 2.1 fb$^{-1}$ of integrated luminosity recorded by the Collider Detector at Fermilab. The cross section is compared with quantum chromodynamic calculations. One is a fixed-order perturbative calculation at ${\cal O}(\alpha_s^2)$, and the other combines perturbative predictions at high transverse momentum with the gluon resummation formalism at low transverse momentum. Comparisons of the measurement with calculations show reasonable agreement. The measurement is of sufficient precision to allow refinements in the understanding of the transverse momentum distribution.
Linear Inviscid Damping for a Class of Monotone Shear Flow in Sobolev Spaces

In this paper, we prove the decay estimates of the velocity and H1 scattering for the two‐dimensional linearized Euler equations around a class of monotone shear flow in a finite channel. Our result is consistent with the decay rate predicted by Case in 1960. © 2016 Wiley Periodicals, Inc.
MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.
RECURSIVE COMBINATORIAL ASPECTS OF COMPACTIFIED MODULI SPACES

A connection between moduli spaces of algebro-geometric objects and moduli spaces of polyhedral objects has been under investigation in recent years. Loosely speaking, the skeleton of an algebro-geometric moduli space is expressed as the modul space of the skeleta of the objects it parametrizes. The connection is based on the combinatorial properties of the boundary of the algebro-geometric moduli spaces. This paper overviews this topic with focus on moduli of curves, admissible covers, Jacobians and their Neron models.
Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions

Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case sum- marization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.
Multiscale analysis in momentum space for quasi-periodic potential in dimension two

We consider a polyharmonic operator H=(−Δ)l+V(x) in dimension two with l ⩾ 2, l being an integer, and a quasi-periodic potential V(x). We prove that the absolutely continuous spectrum of H contains a semiaxis and there is a family of generalized eigenfunctions at every point of this semiaxis with the following properties. First, the eigenfunctions are close to plane waves ei⟨ϰ,x⟩ at the high energy region. Second, the isoenergetic curves in the space of momenta ϰ corresponding to these eigenfunctions have a form of slightly distorted circles with holes (Cantor type structure). A new method of multiscale analysis in the momentum space is developed to prove these results.
Kinetic models for traffic flow resulting in a reduced space of microscopic velocities

The purpose of this paper is to study the properties of kinetic models for traffic flow described by a Boltzmann-type approach and based on a continuous space of microscopic velocities. In our models, the particular structure of the collision kernel allows one to find the analytical expression of a class of steady-state distributions, which are characterized by being supported on a quantized space of microscopic speeds. The number of these velocities is determined by a physical parameter describing the typical acceleration of a vehicle and the uniqueness of this class of solutions is supported by numerical investigations. This shows that it is possible to have the full richness of a kinetic approach with the simplicity of a space of microscopic velocities characterized by a small number of modes. Moreover, the explicit expression of the asymptotic distribution paves the way to deriving new macroscopic equations using the closure provided by the kinetic model.
A numerical approach to Blow-up issues for Davey-Stewartson II type systems

We provide a numerical study of various issues pertaining to the dynamics of the Davey-Stewartson systems of the DS II type. In particular we investigate whether or not the properties (blow-up, radiation,.) displayed by the focusing and defocusing DS II integrable systems persist in the non integrable case.