Miracle-dz/UniFAR-FTUs huggingface dataset:
Self Expanding Neural Networks

The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only its size, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous optimization while doing so. We thereby introduce a natural gradient based approach which intuitively expands both the width and depth of a neural network when this is likely to substantially reduce the hypothetical converged training loss. We prove an upper bound on the ``rate'' at which neurons are added, and a computationally cheap lower bound on the expansion score. We illustrate the benefits of such Self-Expanding Neural Networks with full connectivity and convolutions in both classification and regression problems, including those where the appropriate architecture size is substantially uncertain a priori.
Lagrangian Descriptors with Uncertainty

Lagrangian descriptors provide a global dynamical picture of the geometric structures for arbitrarily time-dependent flows with broad applications. This paper develops a mathematical framework for computing Lagrangian descriptors when uncertainty appears. The uncertainty originates from estimating the underlying flow field as a natural consequence of data assimilation or statistical forecast. It also appears in the resulting Lagrangian trajectories. The uncertainty in the flow field directly affects the path integration of the crucial nonlinear positive scalar function in computing the Lagrangian descriptor, making it fundamentally different from many other diagnostic methods. Despite being highly nonlinear and non-Gaussian, closed analytic formulae are developed to efficiently compute the expectation of such a scalar function due to the uncertain velocity field by exploiting suitable approximations. A rapid and accurate sampling algorithm is then built to assist the forecast of the probability density function (PDF) of the Lagrangian trajectories. Such a PDF provides the weight to combine the Lagrangian descriptors along different paths. Simple but illustrative examples are designed to show the distinguished behavior of using Lagrangian descriptors in revealing the flow field when uncertainty appears. Uncertainty can either completely erode the coherent structure or barely affect the underlying geometry of the flow field. The method is also applied for eddy identification, indicating that uncertainty has distinct impacts on detecting eddies at different time scales. Finally, when uncertainty is incorporated into the Lagrangian descriptor for inferring the source target, the likelihood criterion provides a very different conclusion from the deterministic methods.
A Combinatorial Characterization of Supervised Online Learnability

We study the online learnability of hypothesis classes with respect to arbitrary, but bounded loss functions. No characterization of online learnability is known at this level of generality. We give a new scale-sensitive combinatorial dimension, named the sequential minimax dimension, and show that it gives a tight quantitative characterization of online learnability. In addition, we show that the sequential minimax dimension subsumes most existing combinatorial dimensions in online learning theory.
The method for homography estimation between two planes based on lines and points

The paper considers the problem of estimating a transform connecting two images of one plane object. The method based on RANSAC is proposed for calculating the parameters of projective transform which uses points and lines correspondences simultaneously. A series of experiments was performed on synthesized data. Presented results show that the algorithm convergence rate is significantly higher when actual lines are used instead of points of lines intersection. When using both lines and feature points it is shown that the convergence rate does not depend on the ratio between lines and feature points in the input dataset.
Porous CrO$_2$: a ferromagnetic half-metallic member in sparse hollandite oxide family

A stable polymorph of CrO$_2$ is predicted using PBE+U method. The porous material is isostructural with $\alpha$-MnO$_2$ making it the second transition metal oxide in sparse hollandite group of materials. However, unlike the anti-ferromagnetic semiconducting character of the $\alpha$-MnO$_2$, it is found to be a ferromagnetic half-metal. At Fermi level, the hole pocket has ample contribution from O-2$p$ orbital, though, the electron pocket is mostly contributed by Cr-3$d_{xy}$ and Cr-3d$_{x^2-y^2}$. A combination of negative charge transfer through orbital mixing and extended anti-bonding state near Fermi level is responsible for the half-metallic ferromagnetic character of the structure. A comparative study of rutile and hollandite CrO$_2$ and hollandite MnO$_2$ structures delineate the interplay between structural, electronic and magnetic properties. The material shows a robust magnetic character under hydrothermal pressure, as well as, the band topology is conserved under uniaxial strain. Moderate magneto-crystalline anisotropy is observed and it shows a correspondence with the anisotropy of elastic constants.
Ultra-high temperature Soret effect in a silicate melt: SiO2 migration to cold side.

The Soret effect, temperature gradient driven diffusion, in silicate melts has been investigated intensively in the earth sciences from the 1980s. The SiO2 component is generally concentrated in the hotter region of silicate melts under a temperature gradient. Here, we report that at ultra-high temperatures above ∼3000 K, SiO2 becomes concentrated in the colder region of the silicate melts under a temperature gradient. The interior of an aluminosilicate glass [63.3SiO2-16.3Al2O3-20.4CaO (mol. %)] was irradiated with a 250 kHz femtosecond laser pulse for local heating. SiO2 migrated to the colder region during irradiation with an 800 pulse (3.2 ms irradiation). The temperature analysis indicated that migration to the colder region occurred above 3060 K. In the non-equilibrium molecular dynamics (NEMD) simulation, SiO2 migrated to the colder region under a temperature gradient, which had an average temperature of 4000 K; this result supports the experimental result. On the other hand, SiO2 exhibited a tendency to migrate to the hotter region at 2400 K in both the NEMD and experimental study. The molar volume calculated by molecular dynamics simulation without a temperature gradient indicates two bends at 1650 and 3250 K under 500 MPa. Therefore, the discontinuous (first order) transition with coexistence of two phases of different composition could be related to the migration of SiO2 to colder region. However, the detailed mechanism has not been elucidated.
Efficient Correlation Clustering Methods for Large Consensus Clustering Instances

Consensus clustering (or clustering aggregation) inputs $k$ partitions of a given ground set $V$, and seeks to create a single partition that minimizes disagreement with all input partitions. State-of-the-art algorithms for consensus clustering are based on correlation clustering methods like the popular Pivot algorithm. Unfortunately these methods have not proved to be practical for consensus clustering instances where either $k$ or $V$ gets large. In this paper we provide practical run time improvements for correlation clustering solvers when $V$ is large. We reduce the time complexity of Pivot from $O(|V|^2 k)$ to $O(|V| k)$, and its space complexity from $O(|V|^2)$ to $O(|V| k)$ -- a significant savings since in practice $k$ is much less than $|V|$. We also analyze a sampling method for these algorithms when $k$ is large, bridging the gap between running Pivot on the full set of input partitions (an expected 1.57-approximation) and choosing a single input partition at random (an expected 2-approximation). We show experimentally that algorithms like Pivot do obtain quality clustering results in practice even on small samples of input partitions.
The global surface and atmosphere radiation budget: an assessment of accuracy with 5 years of calculations and observations

We compute the surface and atmosphere radiation budget (SARB) and validate with simultaneous, collocated broadband observations at top of atmosphere (TOA) and surface. The differences of TOA and surface fluxes determine the net radiative cooling of the atmosphere. If known accurately over the globe at all times, this net radiation would balance the sum of the global mean latent and sensible heating other benchmarks sought by the GCM and climate diagnostics community. Our radiative transfer calculations are a bit more sophisticated than those in GCMs. By comparing computations and observations at TOA and surface, we find specific examples of the strengths and weaknesses of the radiative transfer code, the inputs for the code, and the satellite and groundbased broadband fluxes. While mainly intended to provide observations of TOA fluxes, the Clouds and the Earth's Radiant Energy System (CERES, Wielicki, et al, 1996) includes a program to compute the fluxes at TOA, within the atmosphere and at the surface, and also to validate with independent ground based measurements (Charlock and Alberta, 1996). The multi-year computed SARB described here is archived as Terra CRS Edition 2B (ungridded snapshots at satellite overpass) and Terra FSW Edition 2C (gridded and placed in the hour-box closest to overpass); it does not yet span the entire diurnal cycle. Related papers in this volume by Rose et al (2006) describe a preliminary version of a CERES SARB that uses geostationary data to cover the diurnal cycle; a finished, gridded surface albedo product that is available on the web (Rutan et al, 2006a); an ocean surface surface albedo tool that is suitalble for GCMs, as well as remote sensing (Jin et al, 2006); and a more advanced radiative transfer code for the next generation of CERES products (Rose et al. 2006b).
Accelerating Secure and Verifiable Data Deletion in Cloud Storage via SGX and Blockchain

Secure data deletion enables data owners to fully control the erasure of their data stored on local or cloud data centers and is essential for preventing data leakage, especially for cloud storage. However, traditional data deletion based on unlinking, overwriting, and cryptographic key management either ineffectiveness in cloud storage or rely on unpractical assumption. In this paper, we present SevDel, a secure and verifiable data deletion scheme, which leverages the zero-knowledge proof to achieve the verification of the encryption of the outsourced data without retrieving the ciphertexts, while the deletion of the encryption keys are guaranteed based on Intel SGX. SevDel implements secure interfaces to perform data encryption and decryption for secure cloud storage. It also utilizes smart contract to enforce the operations of the cloud service provider to follow service level agreements with data owners and the penalty over the service provider, who discloses the cloud data on its servers. Evaluation on real-world workload demonstrates that SevDel achieves efficient data deletion verification and maintain high bandwidth savings.
Scaling analysis and SE simulation of the tilted cylinder-interface capillary interaction

The capillary interaction induced by a tilted cylinder and interface is the basic configuration of many complex systems, such as micro-pillar arrays clustering, super-hydrophobicity of hairy surface, water-walking insects, and fiber aggregation. We systematically analyzed the scaling laws of tilt angle, contact angle, and cylinder radius on the contact line shape by SE simulation and experiment. The following in-depth analysis of the characteristic parameters (shift, stretch and distortion) of the deformed contact lines reveals the self-similar shape of contact line. Then a general capillary force scaling law is proposed to incredibly grasp all the simulated and experimental data by a quite straightforward ellipse approximation approach.
Mediation Analysis with Graph Mediator

This study introduces a mediation analysis framework when the mediator is a graph. A Gaussian covariance graph model is assumed for graph representation. Causal estimands and assumptions are discussed under this representation. With a covariance matrix as the mediator, parametric mediation models are imposed based on matrix decomposition. Assuming Gaussian random errors, likelihood-based estimators are introduced to simultaneously identify the decomposition and causal parameters. An efficient computational algorithm is proposed and asymptotic properties of the estimators are investigated. Via simulation studies, the performance of the proposed approach is evaluated. Applying to a resting-state fMRI study, a brain network is identified within which functional connectivity mediates the sex difference in the performance of a motor task.
Distinguishing between Dirac and Majorana neutrinos using temporal correlations

In the context of two flavour neutrino oscillations, it is understood that the $2\times 2$ mixing matrix is parameterized by one angle and a Majorana phase. However, this phase does not impact the oscillation probabilities in vacuum or in matter with constant density. Interestingly, the Majorana phase becomes relevant when we describe neutrino oscillations along with neutrino decay. This is due to the fact that effective Hamiltonian has Hermitian and anti-Hermitian components which cannot be simultaneously diagonalized (resulting in decay eigenstates being different from the mass eigenstates). We consider the $\cal PT$ symmetric non-Hermitian Hamiltonian describing two flavour neutrino case and study the violation of Leggett-Garg Inequalities (LGI) in this context for the first time. We demonstrate that temporal correlations in the form of LGI allow us to probe whether neutrinos are Dirac or Majorana. We elucidate the role played by the mixing and decay parameters on the extent of violation of LGI. We emphasize that for optimized choice of parameters, the difference in $K_4$ ($K_3$) for Dirac and Majorana case is $\sim 15\%$ ($\sim 10\%$).
Why machines do not understand: A response to Søgaard

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, S{\o}gaard has argued in this journal for a thesis of this sort, on the basis of the idea (1) that where there is semantics there is also understanding and (2) that machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics \parencite{sogaard:2022}. We show that he goes wrong because he pays insufficient attention to the difference between language as used by humans and the sequences of inert of symbols which arise when language is stored on hard drives or in books in libraries.
Density asymmetry and wind velocities in the orbital plane of the symbiotic binary EG Andromedae

Context. Non-dusty late-type giants without a corona and large-scale pulsations represent objects that do not fulfil the conditions under which standard mass-loss mechanisms can be applied efficiently. The driving mechanism of their winds is still unknown. Aims. The main goal of this work is to match the radial velocities of absorbing matter with a depth in the red giant (RG) atmosphere in the S-type symbiotic star EG And. Methods. We measured fluxes and radial velocities of ten FeI absorption lines from spectroscopic observations with a resolution of ~30 000. At selected orbital phases, we modelled their broadened profiles, including all significant broadening mechanisms. Results. The selected FeI absorption lines at 5151 - 6469A, originate at a radial distance ~1.03 RG radii from its centre. The corresponding radial velocity is typically ~1 km/s , which represents a few percent of the terminal velocity of the RG wind. The high scatter of the radial velocities of several km/s in the narrow layer of the stellar atmosphere points to the complex nature of the near-surface wind mass flow. The average rotational velocity of 11 km/s implies that the rotation of the donor star can contribute to observed focusing the wind towards the orbital plane. The orbital variability of the absorbed flux indicates the highest column densities of the wind in the area between the binary components, even though the absorbing neutral material is geometrically more extended from the opposite side of the giant. This wind density asymmetry in the orbital plane region can be ascribed to gravitational focusing by the white dwarf companion. Conclusions. Our results suggest that both gravitational and rotational focusing contribute to the observed enhancement of the RG wind towards the orbital plane, which makes mass transfer by the stellar wind highly efficient.
A Counterexample and a Corrective to the Vector Extension of the Bellman Equations of a Markov Decision Process

Under the expected total reward criterion, the optimal value of a finite-horizon Markov decision process can be determined by solving the Bellman equations. The equations were extended by D. J. White to processes with vector rewards in 1982. Using a counterexample, we show that the assumptions underlying this extension fail to guarantee its validity. Analysis of the counterexample leads us to articulate a sufficient condition for White's functional equations to be valid. The condition is shown to be true when the policy space has been refined to include a special class of non-Markovian policies, or when the dynamics of the model are deterministic, or when the decision making horizon does not exceed three time steps. The paper demonstrates that, in general, the solutions to White's equations are sets of Pareto efficient policy returns over the refined policy space. Our results are illustrated with an example.
Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints

Semidefinite programs are optimization methods with a wide array of applications, such as approximating difficult combinatorial problems. One such semidefinite program is the Goemans-Williamson algorithm, a popular integer relaxation technique. We introduce a variational quantum algorithm for the Goemans-Williamson algorithm that uses only n+1 qubits, a constant number of circuit preparations, and poly(n) expectation values in order to approximately solve semidefinite programs with up to N=2n variables and M∼O(N) constraints. Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit, a technique known as the Hadamard Test. The Hadamard Test enables us to optimize the objective function by estimating only a single expectation value of the ancilla qubit, rather than separately estimating exponentially many expectation values. Similarly, we illustrate that the semidefinite programming constraints can be effectively enforced by implementing a second Hadamard Test, as well as imposing a polynomial number of Pauli string amplitude constraints. We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems, including MaxCut. Our method exceeds the performance of analogous classical methods on a diverse subset of well-studied MaxCut problems from the GSet library.
Optimizing an LTS-Simulation Algorithm (Technical Report)

When comparing the fastest algorithm for computing the largest simulation preorder over Kripke structures with the one for labeled transition systems (LTS), there is a noticeable time and space complexity blow-up proportional to the size of the alphabet of an LTS. In this paper, we present optimizations that suppress this increase of complexity and may turn a large alphabet of an LTS to an advantage. Our experimental results show significant speed-ups and memory savings. Moreover, the optimized algorithm allows one to improve asymptotic complexity of procedures for computing simulations over tree automata using recently proposed algorithms based on computing simulation over certain special LTS derived from a tree automaton.
Study of the $B^-\to K^-\eta\eta_c$ decay due to the $D\bar{D}$ bound state

We study the $B^-\to K^-\eta \eta_c$ decay by taking into account the $S$-wave contributions from the pseudoscalar meson--pseudoscalar meson interactions within the unitary coupled-channel approach, where the $D\bar{D}$ bound state is dynamically generated. In addition, the contribution from the intermediate resonance $K_0^*(1430)$, with $K_0^*(1430)\to K^-\eta$, is also considered. Our results show that there is a clear peak around $3730$~MeV in the $\eta \eta_c$ invariant mass distribution, which could be associated with the $D \bar{D}$ bound state. The future precise measurements of the $B^-\to K^-\eta \eta_c$ process at the Belle II and LHCb experiments could be, therefore, used to check the existence of the $D \bar{D}$ bound state, and to deepen our understanding of the hadron-hadron interactions.
SOFIA pointing and chopping: performance and prospect

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is a 2.5m infrared telescope built into a Boeing 747 SP. In 2014 SOFIA reached its Full Operational Capability milestone and nowadays takes off about three times a week to observe the infrared sky from altitudes above most of the atmosphere’s water vapor content. Despite reaching this major milestone the work to improve the observatory’s performance is continuing in many areas. This paper focuses on the telescope’s current pointing and chopping performance and gives an overview over the ongoing and foreseen work to further improve in those two areas. Pointing performance as measured with the fast focal plane camera in flight is presented and based on that data it is elaborated how and in which frequency bands a further reduction of image jitter might be achieved. One contributor to the remaining jitter as well as the major actuator to reduce jitter with frequencies greater than 5 Hz is SOFIA’s Secondary Mirror Assembly (SMA) or Chopper. As-is SMA jitter and chopping performance data as measured in flight is presented as well as recent improvements to the position sensor cabling and calibration and their effect on the SMA’s pointing accuracy. Furthermore a brief description of a laboratory mockup of the SMA is given and the intended use of this mockup to test major hardware changes for further performance improvement is explained.