Miracle-dz/UniFAR-FTUs huggingface dataset:
The probability of selecting $k$ edge-disjoint Hamilton cycles in the complete graph.

Let $H_1,\dots,H_k$ be Hamilton cycles in $K_n$, chosen independently and uniformly at random. We show, for $k = o(n^{1/100})$, that the probability of $H_1,\dots,H_k$ being edge-disjoint is $(1+o(1))e^{-2\binom{k}{2}}$. This extends a corresponding estimate obtained by Robbins in the case $k=2$.
Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation

Neural machine translation (NMT) has achieved impressive performance on machine translation task in recent years. However, in consideration of efficiency, a limited-size vocabulary that only contains the top-N highest frequency words are employed for model training, which leads to many rare and unknown words. It is rather difficult when translating from the low-resource and morphologically-rich agglutinative languages, which have complex morphology and large vocabulary. In this paper, we propose a morphological word segmentation method on the source-side for NMT that incorporates morphology knowledge to preserve the linguistic and semantic information in the word structure while reducing the vocabulary size at training time. It can be utilized as a preprocessing tool to segment the words in agglutinative languages for other natural language processing (NLP) tasks. Experimental results show that our morphologically motivated word segmentation method is better suitable for the NMT model, which achieves significant improvements on Turkish-English and Uyghur-Chinese machine translation tasks on account of reducing data sparseness and language complexity.
Power-Law Tails in a Fitness-Driven Model for Biological Evolution

We study a discrete-time stochastic process that can also be interpreted as a model for a viral evolution. A distinguishing feature of our process is power-law tails due to dynamics that resembles preferential attachment models. In the model we study, a population is partitioned into sites, with each site labeled by a uniquely-assigned real number in the interval $[0,1]$ known as fitness. The population size is a discrete-time transient birth-and-death process with probability $p$ of birth and $1-p$ of death. The fitness is assigned at birth according to the following rule: the new member of the population either "mutates" with probability $r$, creating a new site uniformly distributed on $[0,1]$ or "inherits" with probability $1-r$, joining an existing site with probability proportional to the site's size. At each death event, a member from the site with the lowest fitness is killed. The number of sites eventually tends to infinity if and only if $pr>1-p$. Under this assumption, we show that as time tends to infinity, the joint empirical measure of site size and fitness (proportion of population in sites of size and fitness in given ranges) converges a.s. to the product of a modified Yule distribution and the uniform distribution on $[(1-p)/(pr),1]$. Our approach is based on the method developed in \cite{similar-but-different}. The model and the results were independently obtained by Roy and Tanemura in [RT].
Passively mode-locked thulium-doped all-fiber laser based on low V number fiber bending

This paper describes a mode locking technique based on enhanced polarization dependent loss (PDL). The method utilizes a bent single mode fiber (SMF28) coil to induce sufficient PDL at 2 \(\mu\)m wavelength region. Significant PDL in SMF28 coils is enabled since the light is much more weakly guided. A passively mode-locked thulium doped all-fiber laser is demonstrated using this simple device with polarization controllers as mode locker. The results indicate a moderate amount of 1 dB is sufficient to initiate and sustain stable mode-locking operation. We believe, to the best of our knowledge, this is the first demonstration of mode-locked fiber laser fully based on polarization dependent property of bent fiber section.
Modeling the Upper kHz QPOs of 4U 1728-34 with X-Ray Reverberation

While kilohertz quasi-periodic oscillations (kHz QPOs) have been well studied for decades since their initial discovery, the cause of these signals remains unknown, as no model has been able to accurately predict all of their spectral and timing properties. Separately, X-ray reverberation lags have been detected in active galactic nuclei and stellar-mass black hole binaries, and reverberation may be expected to occur in neutron star systems as well, producing lags of the same amplitude as the lags measured of the kHz QPOs. Furthermore, the detection of a relativistically reflected Fe K line in the time-averaged spectra of many neutron star systems provides an additional motivation for testing reverberation. While it has been shown that the lag-energy properties of the lower kHz QPOs are unlikely to be produced by X-ray reverberation, the upper kHz QPOs have not yet been explored. We therefore model the upper kHz QPO lag-energy spectra using relativistic ray-tracing functions and apply them to archival Rossi X-Ray Timing Explorer data on 4U 1728–34 where upper kHz QPOs have been detected. By modeling the time-averaged spectra in which upper kHz QPOs had been significantly detected, we determine the reflected flux fraction across all energies and produce a model for the lag-energy spectra from X-ray reverberation. We explore the dependence of the modeled lag properties on several different types of reflection models but are unable to successfully reproduce the measured lags of 4U 1728–34. We conclude that reverberation alone does not explain the measured time lags detected in upper kHz QPOs.
Improving Deep Neuroevolution via Deep Innovation Protection

Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels. This paper presents a method called Deep Innovation Protection (DIP) that allows training complex world models end-to-end for such 3D environments. The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in a world model, allowing other components to adapt. We investigate the emergent representations of these evolved networks, which learn a model of the world without the need for a specific forward-prediction loss.
Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep Learning

The purpose of this study is to develop an automated algorithm for thoracic vertebral segmentation on chest radiography using deep learning. 124 de-identified lateral chest radiographs on unique patients were obtained. Segmentations of visible vertebrae were manually performed by a medical student and verified by a board-certified radiologist. 74 images were used for training, 10 for validation, and 40 were held out for testing. A U-Net deep convolutional neural network was employed for segmentation, using the sum of dice coefficient and binary cross-entropy as the loss function. On the test set, the algorithm demonstrated an average dice coefficient value of 90.5 and an average intersection-over-union (IoU) of 81.75. Deep learning demonstrates promise in the segmentation of vertebrae on lateral chest radiography.
Hedging problems for Asian options with transactions costs

In this paper, we consider the problem of hedging Asian options in financial markets with transaction costs. For this, we use the asymptotic hedging approach. The main task of asymptotic hedging in financial markets with transaction costs is to prove the probability convergence of the terminal value of the investment portfolio to the payment function when the number of portfolio revisions tends to be $n$ to infinity. In practice, this means that the investor, using such a strategy, is able to compensation payments for all financial transactions, even if their number increases unlimitedly.
A Locational Price for Power Injection Fluctuations of Variable Generation and Load

In this paper we calculate the incremental system production cost associated with a measure of locational power injection uncertainty that can be interpreted as a locational price for tracking power fluctuations. This Locational Price of Variability (LPV) can be used to allocate charges for regulation reserves by location, and hence, can also be used to value distributed energy storage employed to mitigate such fluctuations. We consider policy changes that could enable the implementation of the LPV.
Stochastic Maximum Principle with Default

In this paper, we derive sufficient and necessary maximum principles for a stochastic optimal control problem where the system state is given by a controlled stochastic differential equation with default. We prove existence of a unique solution to the controlled default stochastic differential equation. Furthermore, we prove existence and uniqueness of solution to the adjoint backward stochastic differential equation which appears in connection to the maximum principles. Finally, we apply the maximum principles to solve a utility maximisation problem with logarithmic utility functions.
Precision unification and Higgsino dark matter in GUT scale supersymmetry

Even if the concerns related to the naturalness of the electroweak scale are repressed, the Higgs mass and stability of the electroweak vacuum do not allow arbitrarily large supersymmetry breaking scale, $M_S$, in the minimal models with split or high-scale supersymmetry. We show that $M_S$ can be raised to the GUT scale if the theory below $M_S$ contains a Higgs doublet, a pair of TeV scale Higgsino and widely separated gauginos in addition to the Standard Model particles. The presence of wino and gluino below ${\cal O}(100)$ TeV leads to precision unification of the gauge couplings consistent with the current limits on the proton lifetime. Wino, at this scale, renders the Higgsino as pseudo-Dirac dark matter which in turn evades the existing constraints from the direct detection experiments. Bino mass scale is required to be $\gtrsim 10^{10}$ GeV to get the observed Higgs mass respecting the current limit on the charged Higgs mass. The framework predicts, $1 \lesssim \tan\beta \lesssim 2.2$ and $\tau[p\to e^+\, \pi^0] < 7 \times 10^{35}$ years, almost independent of values of the other parameters. The electroweak vacuum is found to be stable or metastable. The underlying framework provides an example of a viable sub-GUT scale theory of supersymmetric grand unified theory in which supersymmetry and unified gauge symmetry are broken at a common scale.
Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption

Abstract. We converted the radiation part of the atmospheric model ECHAM to a single-precision arithmetic. We analyzed different conversion strategies and finally used a step-by-step change in all modules, subroutines and functions. We found out that a small code portion still requires higher-precision arithmetic. We generated code that can be easily changed from double to single precision and vice versa, basically using a simple switch in one module. We compared the output of the single-precision version in the coarse resolution with observational data and with the original double-precision code. The results of both versions are comparable. We extensively tested different parallelization options with respect to the possible runtime reduction, at both coarse and low resolution. The single-precision radiation itself was accelerated by about 40 %, whereas the runtime reduction for the whole ECHAM model using the converted radiation achieved 18 % in the best configuration. We further measured the energy consumption, which could also be reduced.
Security of Medical Cyber-physical Systems: An Empirical Study on Imaging Devices

Recent years have witnessed a boom of connected medical devices, which brings security issues in the meantime. Medical imaging devices, an essential part of medical cyber-physical systems, play a vital role in modern hospitals and are often life-critical. However, security and privacy issues in these medical cyber-physical systems are sometimes ignored. We perform an empirical study on imaging devices to analyse the security of medical cyber-physical systems. To be precise, we design a threat model and propose prospective attack techniques for medical imaging devices. To tackle potential cyber threats, we introduce protection mechanisms, evaluate the effectiveness and efficiency of protection mechanisms as well as its interplay with attack techniques. To scoring security, we design a hierarchical system that provides actionable suggestions for imaging devices in different scenarios. We investigate 15 devices from 9 manufacturers to demonstrate empirical comprehension and real-world security issues.
A Boolean Task Algebra for Reinforcement Learning

We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the negation, disjunction and conjunction of a set of base tasks. We then show that by learning goal-oriented value functions and restricting the transition dynamics of the tasks, an agent can solve these new tasks with no further learning. We prove that by composing these value functions in specific ways, we immediately recover the optimal policies for all tasks expressible under the Boolean algebra. We verify our approach in two domains, including a high-dimensional video game environment requiring function approximation, where an agent first learns a set of base skills, and then composes them to solve a super-exponential number of new tasks.
Automated Discovery of Data Transformations for Robotic Process Automation

Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RPA, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.
Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computational complexity. The proposed system utilizes signal phasing and timing (SPaT) data that are communicated to connected vehicles (CVs) together with real-time vehicle dynamics to compute fuel-optimum trajectories. The proposed algorithm is incorporated in the INTEGRATION microscopic traffic assignment and simulation software to conduct a comprehensive sensitivity analysis of various variables, including: system market penetration rates (MPRs), demand levels, phase splits, offsets and traffic signal spacings on the system performance. The analysis shows that at 100% MPR, fuel consumption can be reduced by as high as 13.8%. Moreover, higher MPRs and shorter phase lengths result in larger fuel savings. Optimum demand levels and traffic signal spacings exist that maximize the effectiveness of the algorithm. Furthermore, the study demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value. Finally, the study highlights the need for further work to enhance the algorithm to deal with over-saturated traffic conditions.
Complete population inversion of maximally entangled states in 
22N
-level systems via Pythagorean-triple coupling

Maximally entangled states play a central role in quantum information processing. Despite much progress throughout the years, robust protocols for manipulations of such states in many-level systems are still scarce. Here we present a control scheme that allow efficient manipulation of complete population transfer between two maximally entangled states. Exploiting the self-duality of $\mathrm{SU}\left(2\right)$, we present in this work a family of ${\mathrm{2}}^{\mathrm{2}N}$-level systems with couplings related to Pythagorean triples that make a complete population transfer from one state to another (orthogonal) state, using very few couplings and generators. We relate our method to the recently-developed retrograde-canon scheme and derive a more general complete transfer recipe. We also discuss the cases of $\left(2n\right)^2$-level systems, $\left(2n+1\right)^2$-level systems and other unitary groups.
The Strikingly Metal-rich Halo of the Sombrero Galaxy

The nature of the Sombrero galaxy (M104 = NGC 4594) has remained elusive despite many observational studies at a variety of wavelengths. Here we present Hubble Space Telescope imaging of two fields at ∼16 and 33 kpc along the minor axis to examine stellar metallicity gradients in the extended spheroid. We use this imaging, extending more than 2 mag below the tip of the red giant branch, in combination with artificial star tests to forward model observed color–magnitude diagrams, measuring metallicity distribution functions (MDFs) at different radii along the minor axis. An important and unexpected result is that the halo of the Sombrero is strikingly metal-rich: even the outer field, located at ∼17 effective radii of the bulge, has a median metallicity [Z/H] ∼ −0.15, and the fraction of stars with [Z/H] < −1.0 is negligible. This is unprecedented among massive galaxy halos studied to date, even among giant ellipticals. We find significant radial metallicity gradients, characterized by an increase in the fraction of metal-poor stars with radius and a gradient in median metallicity of ∼0.01 dex kpc−1. The density profile is well fit by power laws with slopes that exhibit a dependence on metallicity, with flatter slopes for more metal-poor stars. We discuss our results in the context of recent stellar MDF studies of other nearby galaxies and potential formation scenarios for the Sombrero galaxy.
Real-Time Estimation of Vehicle Counts on Signalized Intersection Approaches Using Probe Vehicle Data

This paper presents a novel method for estimating the number of vehicles traveling along signalized approaches using probe vehicle data only. The proposed method uses the Kalman Filtering technique to produce reliable vehicle count estimates using real-time probe vehicle estimates of the expected travel times. The proposed method introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of probe vehicles. The proposed method is evaluated using empirical and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that vehicle count estimates produced by the proposed method are accurate. The paper also examines the model’s accuracy when installing a single stationary sensor (e.g., loop detector), producing slight improvements especially when the probe vehicle market penetration rate is low. Finally, the paper investigates the sensitivity of the estimation model to traffic demand levels, showing that the model works better at higher demand levels given that more probe vehicles exist for the same market penetration rate.
The energy of muscle contraction. I. Tissue force and deformation during isometric contractions

During contraction the energy of muscle tissue increases due to energy from the hydrolysis of ATP. This energy is distributed across the tissue as strain-energy potentials in the contractile elements, strain-energy potential from the 3D deformation of the base-material tissue (containing cellular and ECM effects), energy related to changes in the muscle's nearly incompressible volume and external work done at the muscle surface. Thus, energy is redistributed through the muscle's tissue as it contracts, with only a component of this energy being used to do mechanical work and develop forces in the muscle's line-of-action. Understanding how the strain-energy potentials are redistributed through the muscle tissue will help enlighten why the mechanical performance of whole muscle in its line-of-action does not match the performance that would be expected from the contractile elements alone. Here we demonstrate these physical effects using a 3D muscle model based on the finite element method. The tissue deformations within contracting muscle are large, and so the mechanics of contraction were explained using the principles of continuum mechanics for large deformations. We present simulations of a contracting medial gastrocnemius muscle, showing tissue deformations that mirror observations from MRI-based images. This paper tracks the redistribution of strain-energy potentials through the muscle tissue during isometric contractions, and shows how fibre shortening, pennation angle, transverse bulging and anisotropy in the stress and strain of the muscle tissue are all related to the interaction between the material properties of the muscle and the action of the contractile elements.
BioSNet: A Fast-Learning and High-Robustness Unsupervised Biomimetic Spiking Neural Network

Spiking Neural Network (SNN), as a brain-inspired machine learning algorithm, is closer to the computing mechanism of human brain and more suitable to reveal the essence of intelligence compared with Artificial Neural Networks (ANN), attracting more and more attention in recent years. In addition, the information processed by SNN is in the form of discrete spikes, which makes SNN have low power consumption characteristics. In this paper, we propose an efficient and strong unsupervised SNN named BioSNet with high biological plausibility to handle image classification tasks. In BioSNet, we propose a new biomimetic spiking neuron model named MRON inspired by 'recognition memory' in the human brain, design an efficient and robust network architecture corresponding to biological characteristics of the human brain as well, and extend the traditional voting mechanism to the Vote-for-All (VFA) decoding layer so as to reduce information loss during decoding. Simulation results show that BioSNet not only achieves state-of-the-art unsupervised classification accuracy on MNIST/EMNIST data sets, but also exhibits superior learning efficiency and high robustness. Specifically, the BioSNet trained with only dozens of samples per class can achieve a favorable classification accuracy over 80% and randomly deleting even 95% of synapses or neurons in the BioSNet only leads to slight performance degradation.