Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical hydraulic model for the Leonardo's rule.

More than five hundred years ago Leonardo Da Vinci found a pattern in the growth of trees nowadays known as the Leonardo's rule. This rule relates the thickness of the stem with the thickness of the branches at different bifurcation stages in a Pythagorean fashion. He argued that his rule was the result of the conservation of sap flux. In the present work, we explore this idea by assuming that the sap flux through each xylem element behaves as a non-ideal fluid and the size-distribution of the xylem elements obeys a power law distribution. We find that the simultaneous fulfillment of Leonardo's rule and the conservation of the sap flux, lead to a global behavior of the sap like that of an ideal fluid after summing over all xylem elements. These results are supported by field and experimental work. In particular, we corroborated the Leonardo's rule in different tree species by measuring the stem-branches thickness at different bifurcations stages. We also determined the statistical size-distribution of the xylem elements through a maceration process, finding that it corresponds to a power law distribution with an exponent close to three, which is the exponent required for the Leonardo's rule. As far as we know this is the first time that a statistical hydraulic model supported by experimental data is presented for the Leonardo's rule.
A P ] 2 2 Se p 20 20 RECONSTRUCTION IN THE CALDERÓN PROBLEM ON CONFORMALLY TRANSVERSALLY ANISOTROPIC MANIFOLDS

We show that a continuous potential q can be constructively determined from the knowledge of the Dirichlet–to–Neumann map for the Schrödinger operator −∆g+q on a conformally transversally anisotropic manifold of dimension≥ 3, provided that the geodesic ray transform on the transversal manifold is constructively invertible. This is a constructive counterpart of the uniqueness result of [11]. A crucial role in our reconstruction procedure is played by a constructive determination of the boundary traces of suitable complex geometric optics solutions based on Gaussian beams quasimodes concentrated along non-tangential geodesics on the transversal manifold, which enjoy uniqueness properties. This is achieved by applying the simplified version of the approach of [33] to our setting. We also identify the main space introduced in [33] with a standard Sobolev space on the boundary of the manifold. Another ingredient in the proof of our result is a reconstruction formula for the boundary trace of a continuous potential from the knowledge of the Dirichlet–to–Neumann map.
Topological Gravity as the Early Phase of Our Universe

Motivated by string dualities we propose topological gravity as the early phase of our universe. The topological nature of this phase naturally leads to the explanation of many of the puzzles of early universe cosmology. A concrete realization of this scenario using Witten's four dimensional topological gravity is considered. This model leads to the power spectrum of CMB fluctuations which is controlled by the conformal anomaly coefficients $a,c$. In particular the strength of the fluctuation is controlled by $1/a$ and its tilt by $c g^2$ where $g$ is the coupling constant of topological gravity. The positivity of $c$, a consequence of unitarity, leads automatically to an IR tilt for the power spectrum. In contrast with standard inflationary models, this scenario predicts $\mathcal{O}(1)$ non-Gaussianities for four- and higher-point correlators and the absence of tensor modes in the CMB fluctuations.
Public Health Informatics: Proposing Causal Sequence of Death Using Neural Machine Translation

Each year there are nearly 57 million deaths around the world, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, as institutions and government agencies rely on death reports to analyze vital statistics and to formulate responses to communicable diseases. Inaccurate death reporting may result in potential misdirection of public health policies. Determining the causes of death is, nevertheless, challenging even for experienced physicians. To facilitate physicians in accurately reporting causes of death, we present an advanced AI approach to determine a chronically ordered sequence of clinical conditions that lead to death, based on decedent's last hospital admission discharge record. The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); the priority-ordered clinical conditions on the discharge record are coded in ICD-9. We identify three challenges in proposing the causal chain of death: two versions of coding system in clinical codes, medical domain knowledge conflict, and data interoperability. To overcome the first challenge in this sequence-to-sequence problem, we apply neural machine translation models to generate target sequence. We evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.44 out of 100. To address the second challenge, we incorporate expert-verified medical domain knowledge as constraint in generating output sequence to exclude infeasible causal chains. Lastly, we demonstrate the usability of our work in a Fast Healthcare Interoperability Resources (FHIR) interface to address the third challenge.
Spirals, shadows, and precession in HD 100453 – I. The orbit of the binary

In recent years, several protoplanetary discs have been observed to exhibit spirals, both in scattered light and (sub)millimetre continuum data. The HD 100453 binary star system hosts such a disc around its primary. Previous work has argued that the spirals were caused by the gravitational interaction of the secondary, which was assumed to be on a circular orbit, coplanar with the disc (meaning here the large outer disc, as opposed to the very small inner disc). However, recent observations of the CO gas emission were found incompatible with this assumption. In this paper, we run SPH simulations of the gas and dust disc for seven orbital configurations taken from astrometric fits and compute synthetic observations from their results. Comparing to high-resolution ALMA $^{12}$CO data, we find that the best agreement is obtained for an orbit with eccentricity $e=0.32$ and semi-major axis $a=207$ au, inclined by $61^\circ$ relative to the disc plane. The large misalignment between the disc and orbit planes is compatible with the tidal evolution of a circumprimary disc in an eccentric, unequal-mass binary star.
Years-delayed X-Ray Afterglows of TDEs Originated from Wind–Torus Interactions

Tidal disruption events (TDEs) occurring in active galactic nuclei (AGNs) are a special class of sources with outstanding scientific significance. TDEs can generate ultrafast winds, which should almost inevitably collide with the preexisting AGN dusty tori. We perform analytical calculations and simulations on the wind–torus interactions and find such a process can generate considerable X-ray afterglow radiation several years or decades later after the TDE outburst. This provides a new origin for the years delayed X-rays in TDEs. The X-ray luminosity can reach 1041−42 erg s−1, and the light curve characteristics depend on the parameters of winds and tori. We apply the model to two TDE candidates, and provide lower limits on the masses of the disrupted stars, as well as rigorous constraints on the gas densities of tori. Our results suggest that the observations of the time delay, spectral shape, luminosity, and the light curve of the X-ray afterglow can be used to constrain the physical parameters of both TDE winds and tori, including the wind velocity, wind density, cloud density, and cloud size.
Learning Task-Agnostic Action Spaces for Movement Optimization

We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous article, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances; this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.
Texas Spectroscopic Search for Lyα Emission at the End of Reionization. III. The Lyα Equivalent-width Distribution and Ionized Structures at z > 7

Lyα emission from galaxies can be utilized to characterize the ionization state in the intergalactic medium (IGM). We report our search for Lyα emission at z > 7 using a comprehensive Keck/MOSFIRE near-infrared spectroscopic data set, as part of the Texas Spectroscopic Search for Lyα Emission at the End of Reionization Survey. We analyze data from 10 nights of MOSFIRE observations which together target 72 high-z candidate galaxies in the GOODS-N field, all with deep exposure times of 4.5–19 hr. Utilizing an improved automated emission-line search, we report 10 Lyα emission lines detected (>4σ) at z > 7, significantly increasing the spectroscopically confirmed sample. Our sample includes large equivalent-width (EW) Lyα emitters (>50 Å), and additional tentative Lyα emission lines detected at 3σ–4σ from five additional galaxies. We constrain the Lyα EW distribution at z ∼ 7.6, finding a significant drop from z ≲ 6, suggesting an increasing fraction of neutral hydrogen (H i) in the IGM in this epoch. We estimate the Lyα transmission through the IGM (=EWz ∼ 7.6/EW ) and infer an IGM H i fraction (XHI) of at z ∼ 7.6, which is lower in modest tension (>1σ) with recent measurements at z ∼ 7.6. The spatial distribution of the detected Lyα emitters implies the presence of a potential highly ionized region at z ∼ 7.55, which hosts four Lyα emitters within a ∼40 cMpc spatial separation. The prominence of this ionized region in our data set could explain our lower inferred value of XHI, though our analysis is also sensitive to the chosen reference Lyα EW distribution values and reionization models.
Construction of Artificial Point Sources for a Linear Wave Equation in Unknown Medium

We study the wave equation on a bounded domain of $\mathbb R^m$ and on a compact Riemannian manifold $M$ with boundary. We assume that the coefficients of the wave equation are unknown but that we are given the hyperbolic Neumann-to-Dirichlet map $\Lambda$ that corresponds to the physical measurements on the boundary. Using the knowledge of $\Lambda$ we construct a sequence of Neumann boundary values so that at a time $T$ the corresponding waves converge to zero while the time derivative of the waves converge to a delta distribution. Such waves are called an artificial point source. The convergence of the wave takes place in the function spaces naturally related to the energy of the wave. We apply the results for inverse problems and demonstrate the focusing of the waves numerically in the 1-dimensional case.
Modeling Techniques for Logic Locking

Logic locking is a method to prevent intellectual property (IP) piracy. However, under a reasonable attack model, SAT-based methods have proven to be powerful in obtaining the secret key. In response, many locking techniques have been developed to specifically resist this form of attack. In this paper, we demonstrate two SAT modeling techniques that can provide many orders of magnitude speed up in discovering the correct key. Specifically, we consider relaxed encodings and symmetry breaking. To demonstrate their impact, we model and attack a state-of-the-art logic locking technique, Full-Lock. We show that circuits previously unbreakable within 15 days of run time can be solved in seconds. Consequently, in assessing the strength of any given locking, it is imperative that these modeling techniques be considered. To remedy this vulnerability in the considered locking technique, we demonstrate an extended version, logic-enhanced Banyan locking, that is resistant to our proposed modeling techniques.
OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles

This paper presents OpenREALM, a real-time mapping framework for Unmanned Aerial Vehicles (UAVs). A camera attached to the onboard computer of a moving UAV is utilized to acquire high resolution image mosaics of a targeted area of interest. Different modes of operation allow OpenREALM to perform simple stitching assuming an approximate plane ground, or to fully recover complex 3D surface information to extract both elevation maps and geometrically corrected orthophotos. Additionally, the global position of the UAV is used to georeference the data. In all modes incremental progress of the resulting map can be viewed live by an operator on the ground. Obtained, up-to-date surface information will be a push forward to a variety of UAV applications. For the benefit of the community, source code is public at https://github.com/laxnpander/OpenREALM.
A rapidly updating stratified mix-adjusted median property price index model

Homeowners, first-time buyers, banks, governments and construction companies are highly interested in following the state of the property market. Currently, property price indexes are published several months out of date and hence do not offer the up-to-date information which housing market stakeholders need in order to make informed decisions. In this article, we present an updated version of a central-price tendency based property price index which uses geospatial property data and stratification in order to compare similar houses. The expansion of the algorithm to include additional parameters owing to a new data structure implementation and a richer dataset allows for the construction of a far smoother and more robust index than the original algorithm produced.
Effects of climate change on typical forest in the northeast of China

By using the forest gap model-FAREAST,we simulated the effects of climate changing in future on forest composition and forest biomass of typical forest type in northeast of China.We chose three different climate change scenarios developed from GCMs results of ECHAM5-OM and HadCM3:current climate,warmer climate and the state of changing precipitation with higher temperature.The results show as follows,if the climate goes with no change,northeastern forest composition and forest biomass will maintain dynamic balance.Warmer climate is unadvantaged to major forest type in the northeast of China,The percentage of major conifers decrease,along with the percentage increase of some broadleaf trees.The Mixed broad-leaved tree species/Korean pine forest belt in temperate zone will tend to move up.And more warmer,more distinct.Taken account of rainfall further,the forest belt of northeast of China will tend to move northward,rainfall has a little effect on the Mixed broad-leaved tree species/Korean pine forests in temperate zone.
Rank one tensor completion problem

In this paper, we consider the rank-one tensor completion problem. We address the question of existence and uniqueness of the rank-one solution. In particular we show that the global uniqueness over the field of real numbers can be verified in a polynomial time. We give examples showing that there is an essential difference between the question of global uniqueness over the fields of real and complex numbers. Finally we briefly discuss the rank-one approximation problem for noisy observations.
On the origin of the optical and near-infrared extragalactic background light

In optical and near-infrared background light, excess brightness and fluctuation over the known backgrounds have been reported. To delineate their origin, a fluctuation analysis of the deepest optical images was performed, leading to the detection of a flat fluctuation down to 0.2 arcsec, which is much larger than that expected for galaxies. The sky brightness obtained from the detected fluctuation is a few-times brighter than the integrated light of the galaxies. These findings require some new objects. As a candidate, faint compact objects (FCOs) whose surface number density rapidly increases to the faint end were proposed. FCOs are very compact and show peculiar spectra with infrared excess. If FCOs cause the excess brightness and fluctuation, the surface number density reaches 2.6 × 103 arcsec−2. γ-ray observations require the redshift of FCOs to be less than 0.1 with FCOs consisting of missing baryons. A very low M/L indicates that FCOs are powered by gravitational energy associated with black holes.
Graph limits: An alternative approach to s‐graphons

We show that s‐convergence of graph sequences is equivalent to the convergence of certain compact sets, called shapes, of Borel probability measures. This result is analogous to the characterization of graphon convergence (with respect to the cut distance) by the convergence of envelopes, due to Doležal, Grebík, Hladký, Rocha, and Rozhoň.
Continuous Reasoning for Managing Next-Gen Distributed Applications

Continuous reasoning has proven effective in incrementally analysing changes in application codebases within Continuous Integration/Continuous Deployment (CI/CD) software release pipelines. In this article, we present a novel declarative continuous reasoning approach to support the management of multi-service applications over the Cloud-IoT continuum, in particular when infrastructure variations impede meeting application's hardware, software, IoT or network QoS requirements. We show how such an approach brings considerable speed-ups compared to non-incremental reasoning.
Bootstrapped Newtonian Cosmology and the Cosmological Constant Problem

Bootstrapped Newtonian gravity was developed with the purpose of estimating the impact of quantum physics in the nonlinear regime of the gravitational interaction, akin to corpuscular models of black holes and inflation. In this work, we set the ground for extending the bootstrapped Newtonian picture to cosmological spaces. We further discuss how such models of quantum cosmology can lead to a natural solution to the cosmological constant problem.
Analysis of tunnel failure characteristics under multiple explosion loads based on persistent homology-based machine learning

The study of tunnel failure characteristics under the load of external explosion source is an important problem in tunnel design and protection, in particular, it is of great significance to construct an intelligent topological feature description of the tunnel failure process. The failure characteristics of tunnels under explosive loading are described by using discrete element method and persistent homology-based machine learning. Firstly, the discrete element model of shallow buried tunnel was established in the discrete element software, and the explosive load was equivalent to a series of uniformly distributed loads acting on the surface by Saint-Venant principle, and the dynamic response of the tunnel under multiple explosive loads was obtained through iterative calculation. The topological characteristics of surrounding rock is studied by persistent homology-based machine learning. The geometric, physical and interunit characteristics of the tunnel subjected to explosive loading are extracted, and the nonlinear mapping relationship between the topological quantity of persistent homology, and the failure characteristics of the surrounding rock is established, and the results of the intelligent description of the failure characteristics of the tunnel are obtained. The research shows that the length of the longest Betty 1 bar code is closely related to the stability of the tunnel, which can be used for effective early warning of the tunnel failure, and an intelligent description of the tunnel failure process can be established to provide a new idea for tunnel engineering protection.
University of Birmingham The use of artificial compressibility to improve partitioned semi-implicit FSI coupling within the classical Chorin-Témam projection framework

Over the last decade the classical Chorin-Témam projection method has been utilized to address fluid-structure interaction in a semi-implicit manner. In previous studies the fluid projection step is fully coupled with the structural motion due to the divergence-free constraint. A set of simultaneous equations thus have to be iteratively solved. To overcome this difficulty, a simple and accurate partitioned semi-implicit coupling method is proposed based on the artificial compressibility (AC) in this paper. The iterated AC parameter decouples the pressure, end-of-step velocity and structural motion within the characteristic-based split scheme. The present approach is completely matrix-free and has unlimited access to the finite elements. Its performance is demonstrated for an oscillating bluff body subjected to uniform flows.
Synthetic Data for Hindi Character Recognition

: The success of deep learning techniques has provided solution to many pattern recognition problems. It has shown abrupt advancement in technology related to artificial intelligence and machine learning. Deep learning involves self learning using convolved filters and emphasize on automatic feature extraction. This self learning requires huge amount of training data to learn from different examples so that recognition system can handle variations in character at testing time. The major challenge in Hindi Character recognition is scarcity of handwritten database. The training dataset should contain variety of characters to build robust recognition system. The field of Hindi handwritten character recognition has scarcity of grayscale databases, thus techniques for generating synthetic dataset for Hindi isolated characters has been proposed in this paper. This synthetic dataset is generated using original handwritten isolated characters of Hindi language. The synthetic data is trained and tested on convolutional neural network architecture for recognition and experimental results show the efficacy of this dataset, it can be used independently or in combination with original handwritten characters.
Operator-valued formulas for Riemannian Gradient and Hessian and families of tractable metrics in optimization and machine learning

We provide an explicit formula for the Levi-Civita connection and Riemannian Hessian when the {\it tangent space} at each point of a Riemannian manifold is embedded in an inner product space with a non-constant metric. Together with a classical formula for projection, this allows us to evaluate Riemannian gradient and Hessian for several families of metric extending existing ones on classical manifolds: a family of metrics on Stiefel manifolds connecting both the constant and canonical ambient metrics with closed-form geodesics; a family of quotient metrics on a manifold of positive-semidefinite matrices of fixed rank, considered as a quotient of a product of Stiefel and positive-definite matrix manifold with affine-invariant metrics; a large family of new metrics on flag manifolds. We show in many instances, this method allows us to apply symbolic calculus to derive formulas for the Riemannian gradient and Hessian. The method greatly extends the list of potential metrics that could be used in manifold optimization and machine learning.