Miracle-dz/UniFAR-FTUs huggingface dataset:
Decomposition of Large Scale Linear Programming Problems Containing both Linking Variables and Constraints via Accuracy Certificates

Several well known large scale linear programming decomposition methodologies exist. Benders Decomposition, which covers the case where some small subset of variables link the otherwise separable subproblems. Dantzig-Wolfe decomposition and Lagrangian decompositions, which cover the case where some few constraints link the otherwise separable subproblems, and finally the "Cross-Decomposition" originating from TJ Van Roy which enables one to deal with both linking constraints and linking variables by essentially alternating iteratively between the Benders and the Lagrangian Decomposition. In this paper we present a novel alternative to Cross-decomposition that deals with both linking constraints and linking variables through the application of accuracy certificates for black-box, sub-gradient based algorithms such as NERML.
MEGA: Merger graphs of structure formation

When following the growth of structure in the Universe, we propose replacing merger trees with merger graphs, in which haloes can both merge and split into separate pieces. We show that this leads to smoother mass growth and eliminates catastrophic failures in which massive haloes have no progenitors or descendants. For those who prefer to stick with merger trees, we find that trees derived from our merger graphs have similar mass growth properties to previous methods, but again without catastrophic failures. For future galaxy formation modelling, two different density thresholds can be used to distinguish host haloes (extended galactic haloes, groups, and clusters) from higher density subhaloes: sites of galaxy formation.
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images

This paper presents a visualization method of intestine (the small and large intestine) regions and their stenosed parts caused by ileus from CT volumes. Since it is difficult for non-expert clinicians to find stenosed parts, the intestine and its stenosed parts should be visualized intuitively. Furthermore, the intestine regions of ileus cases are quite hard to be segmented. The proposed method segments intestine regions by 3D FCN (3D U-Net). Intestine regions are quite difficult to be segmented in ileus cases since the inside the intestine is filled with liquids. These liquids have similar intensities with intestinal wall on 3D CT volumes. We segment the intestine regions by using 3D U-Net trained by a weak annotation approach. Weak-annotation makes possible to train the 3D U-Net with small manually-traced label images of the intestine. This avoids us to prepare many annotation labels of the intestine that has long and winding shape. Each intestine segment is volume-rendered and colored based on the distance from its endpoint in volume rendering. Stenosed parts (disjoint points of an intestine segment) can be easily identified on such visualization. In the experiments, we showed that stenosed parts were intuitively visualized as endpoints of segmented regions, which are colored by red or blue.
ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search

How to discover and evaluate the true strength of models quickly and accurately is one of the key challenges in Neural Architecture Search (NAS). To cope with this problem, we propose an Architecture-Driven Weight Prediction (ADWP) approach for neural architecture search (NAS). In our approach, we first design an architecture-intensive search space and then train a HyperNetwork by inputting stochastic encoding architecture parameters. In the trained HyperNetwork, weights of convolution kernels can be well predicted for neural architectures in the search space. Consequently, the target architectures can be evaluated efficiently without any finetuning, thus enabling us to search fortheoptimalarchitectureinthespaceofgeneralnetworks (macro-search). Through real experiments, we evaluate the performance of the models discovered by the proposed AD-WPNAS and results show that one search procedure can be completed in 4.0 GPU hours on CIFAR-10. Moreover, the discovered model obtains a test error of 2.41% with only 1.52M parameters which is superior to the best existing models.
Compact Bright Radio-loud AGNs. III. A Large VLBA Survey at 43 GHz

We present the results from the 43 GHz Very Long Baseline Array (VLBA) observations of 124 compact radio-loud active galactic nuclei (AGNs) that were conducted between 2014 November and 2016 May. The typical dimensions of the restoring beam in each image are about 0.5 mas × 0.2 mas. The highest resolution of 0.2 mas corresponds to a physical size of 0.02 pc for the lowest redshift source in the sample. The 43 GHz very long baseline interferometry (VLBI) images of 97 AGNs are presented for the first time. We study the source compactness on milliarcsecond and submilliarcsecond scales, and suggest that 95 sources in our sample are suitable for future space VLBI observations. By analyzing our data supplemented with other VLBA AGN surveys from the literature, we find that the core brightness temperature increases with increasing frequency below a break frequency ∼7 GHz, and decreases between ∼7 and 240 GHz but increases again above 240 GHz in the rest frame of the sources. This indicates that the synchrotron opacity changes from optically thick to thin. We also find a strong statistical correlation between radio and γ-ray flux densities. Our correlation is tighter than those in the literature derived from lower-frequency VLBI data, suggesting that the γ-ray emission is produced more cospatially with the 43 GHz VLBA core emission. This correlation can also be extrapolated to the unbeamed AGN population, implying that a universal γ-ray production mechanism might be at work for all types of AGNs.
A Ringed Pole-on Outflow from DO Tauri Revealed by ALMA

We present new Atacama Large Millimeter/submillimeter Array (ALMA) Band 6 observations including the CO(2−1) line and 1.3 mm continuum emission from the surroundings of the young stellar object DO Tauri. The ALMA CO molecular data show three different series of rings at different radial velocities. These rings have radii around 220 and 800 au. We make individual fits to the rings and note that their centers are aligned with DO Tauri and its optical high-velocity jet. In addition, we notice that the velocity of these structures increases with the separation from the young star. We discuss the data under the hypothesis that the rings represent velocity cuts through three outflowing shells that are possibly driven by a wide-angle wind, dragging the environment material along a direction close to the line of sight (i = 19°). We estimate the dynamical ages, the mass, the momentum, and the energy of each individual outflow shell and those of the whole outflow. The results are in agreement with those found in outflows from Class II sources. We make a rough estimate for the size of the jet/wind launching region, which needs to be of ≲15 au. We report the physical characteristics of DO Tauri’s disk continuum emission (almost face-on and with a projected major axis in the north–south direction) and its velocity gradient orientation (north–south), indicative of disk rotation for a 1–2 M⊙ central star. Finally, we show a Hubble Space Telescope [S ii] image of the optical jet and report a measurement of its orientation in the plane of the sky.
Inferring the location of reflecting surfaces exploiting loudspeaker directivity

Accurate sound field reproduction in rooms is often limited by the lack of knowledge of the room characteristics. Information about the room shape or nearby reflecting boundaries can, in principle, be used to improve the accuracy of the reproduction. In this paper, we propose a method to infer the location of nearby reflecting boundaries from measurements on a microphone array. As opposed to traditional methods, we explicitly exploit the loudspeaker directivity model—beyond omnidirectional radiation—and the microphone array geometry. This approach does not require noiseless timing information of the echoes as input, nor a tailored loudspeaker-wall-microphone measurement step. Simulations show the proposed model outperforms current methods that disregard directivity in reverberant environments.
Thermal phase structure of a supersymmetric matrix model

We present initial results from ongoing lattice investigations into the thermal phase structure of the Berenstein--Maldacena--Nastase deformation of maximally supersymmetric Yang--Mills quantum mechanics. The phase diagram of the theory depends on both the temperature $T$ and the deformation parameter $\mu$, through the dimensionless ratios $T / \mu$ and $g \equiv \lambda / \mu^3$ with $\lambda$ the 't Hooft coupling. Considering couplings $g$ that span three orders of magnitude, we reproduce the weak-coupling perturbative prediction for the deconfinement $T / \mu$ and approach recent large-$N$ dual supergravity analyses in the strong-coupling limit. We are carrying out calculations with lattice sizes up to $N_{\tau} = 24$ and numbers of colors up to $N = 16$, to allow initial checks of the large-$N$ continuum limit.
Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors

This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.
Explicitly Trained Spiking Sparsity in Spiking Neural Networks with Backpropagation

Spiking Neural Networks (SNNs) are being explored for their potential energy efficiency resulting from sparse, event-driven computations. Many recent works have demonstrated effective backpropagation for deep Spiking Neural Networks (SNNs) by approximating gradients over discontinuous neuron spikes or firing events. A beneficial side-effect of these surrogate gradient spiking backpropagation algorithms is that the spikes, which trigger additional computations, may now themselves be directly considered in the gradient calculations. We propose an explicit inclusion of spike counts in the loss function, along with a traditional error loss, causing the backpropagation learning algorithms to optimize weight parameters for both accuracy and spiking sparsity. As supported by existing theory of over-parameterized neural networks, there are many solution states with effectively equivalent accuracy. As such, appropriate weighting of the two loss goals during training in this multi-objective optimization process can yield an improvement in spiking sparsity without a significant loss of accuracy. We additionally explore a simulated annealing-inspired loss weighting technique to increase the weighting for sparsity as training time increases. Our preliminary results on the Cifar-10 dataset show up to 70.1% reduction in spiking activity with iso-accuracy compared to an equivalent SNN trained only for accuracy and up to 73.3% reduction in spiking activity if allowed a trade-off of 1% reduction in classification accuracy.
Disentangling Physical Dynamics From Unknown Factors for Unsupervised Video Prediction

Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video forecasting models. Since physics is too restrictive for describing the full visual content of generic video sequences, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. A second contribution is to propose a new recurrent physical cell (PhyCell), inspired from data assimilation techniques, for performing PDE-constrained prediction in latent space. Extensive experiments conducted on four various datasets show the ability of PhyDNet to outperform state-of-the-art methods. Ablation studies also highlight the important gain brought out by both disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet presents interesting features for dealing with missing data and long-term forecasting.
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is faced with numerous tactical and strategical choices. Most state-of-the-art AV platforms are implementing tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable, but have no clear priority among each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation.
A New Method for Deriving Composition of S-type Asteroids from Noisy and Incomplete Near-infrared Spectra

The surface composition of S-type asteroids can be determined using band parameters extracted from their near-infrared (NIR) spectra (0.7–2.50 μm) along with spectral calibrations derived from laboratory samples. In the past, these empirical equations have been obtained by combining NIR spectra of meteorite samples with information about their composition and mineral abundance. For these equations to give accurate results, the characteristics of the laboratory spectra they are derived from should be similar to those of asteroid spectral data (i.e., similar signal-to-noise ratio (S/N) and wavelength range). Here we present new spectral calibrations that can be used to determine the mineral composition of ordinary chondrite-like S-type asteroids. Contrary to previous work, the S/N of the ordinary chondrite spectra used in this study has been decreased to recreate the S/N typically observed among asteroid spectra, allowing us to obtain more realistic results. In addition, the new equations have been derived for five wavelength ranges encompassed between 0.7 and 2.50 μm, making it possible to determine the composition of asteroids with incomplete data. The new spectral calibrations were tested using band parameters measured from the NIR spectrum of asteroid (25143) Itokawa, and comparing the results with laboratory measurements of the returned samples. We found that the spectrally derived olivine and pyroxene chemistry, which are given by the molar contents of fayalite (Fa) and ferrosilite (Fs), are in excellent agreement with the mean values measured from the samples (Fa28.6±1.1 and Fs23.1±2.2), with a maximum difference of 0.6 mol% for Fa and 1.4 mol% for Fs.
Resilience of the superradiant phase against 
A2
 effects in the quantum Rabi dimer

We explore the quantum criticality of a two-site model combining quantum Rabi models with hopping interaction. Through a combination of analytical and numerical approaches, we find that the model allows the appearance of a superradiant quantum phase transition (QPT) even in the presence of strong ${\mathbf{A}}^{2}$ terms, preventing single-site superradiance. In the two-site model the effect of ${\mathbf{A}}^{2}$ terms can be surmounted by the photon delocalization from hopping, and a reversed superradiant QPT occurs as a consequence of the competition between ${\mathbf{A}}^{2}$ terms and the hopping interaction. We characterize the phase diagram and scaling functions and extract the critical exponents in the vicinity of the critical point, thus establishing the universal behavior of the second-order phase transition. Remarkably the effective hopping strength will be enhanced if more cavities are cascaded. We also prove that the multiqubit counterpart of the quantum Rabi dimer, i.e., the Dicke dimer, has the same properties in beating the ${\mathbf{A}}^{2}$ effect. Our work provides a path to the study of phase transitions in the presence of the ${\mathbf{A}}^{2}$ terms and offers the prospect of investigating quantum-criticality physics and quantum devices in many-body systems.
Intelligent UAV Base Station Selection in Urban Environments: A Supervised Learning Approach

When Unmanned Aerial Vehicles (UAVs) connect into the cellular network, their wireless channel is negatively impacted by strong Line-of-Sight (LoS) interference from terrestrial Base Stations (BSs), as well as from antenna misalignment due to downtilted BS antennas. Moreover, due to their aerial positions, these UAVs are exposed to a large number of BSs with which they can associate for wireless service. Therefore, to maximise the performance of the UAV-BS wireless link, the UAVs need to be able to choose which BSs to connect to, based on the observed environmental conditions. In this regard, this paper proposes a supervised learning-based association scheme, using which a UAV can intelligently associate to the most appropriate BS. We train a Neural Network (NN) to identify the most suitable BS from several candidate BSs, based on the received signal powers from the BSs, known distances to the BSs, as well as the known locations of potential interferers. We then compare the performance of the NN-based association scheme against strongest-signal and closest-neighbour association schemes.
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization

Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on publicly available datasets often do not work well on real-world clinical data due to the differences in patient population and image device configurations. Also, manually annotating clinical images is expensive. In this work, we propose an unsupervised domain adaptation (UDA) method using Cycle-GAN to improve the generalization ability of the model without using any additional manual annotations.
Distilled Hierarchical Neural Ensembles with Adaptive Inference Cost

Deep neural networks form the basis of state-of-the-art models across a variety of application domains. Moreover, networks that are able to dynamically adapt the computational cost of inference are important in scenarios where the amount of compute or input data varies over time. In this paper, we propose Hierarchical Neural Ensembles (HNE), a novel framework to embed an ensemble of multiple networks by sharing intermediate layers using a hierarchical structure. In HNE we control the inference cost by evaluating only a subset of models, which are organized in a nested manner. Our second contribution is a novel co-distillation method to boost the performance of ensemble predictions with low inference cost. This approach leverages the nested structure of our ensembles, to optimally allocate accuracy and diversity across the ensemble members. Comprehensive experiments over the CIFAR and Ima-geNet datasets confirm the effectiveness of HNE in building deep networks with adaptive inference cost for image classification.
The funnel annihilations of light dark matter and the Higgs invisible decay

The semi-constrained NMSSM (scNMSSM), or NMSSM with non-universal Higgs masses, can naturally predict a light dark matter under current constraints including Higgs data, sparticles mass bounds, dark matter search, and muon g-2, etc. In this work, we take this scenario of scNMSSM as an example to study the funnel-annihilation mechanism of light dark matter ($1\thicksim62$ GeV) and Higgs invisible decay. In this scenario we found that: (i) There can be four funnel-annihilation mechanisms for the LSP $\tilde{\chi}^0_1$, which are the $h_2$, $Z$, $h_1$ and $a_1$ funnel. (ii) For the $h_1$ and $a_1$ funnel with right relic density, the $\tilde{\chi}^0_1$ mass is lighter than 12 GeV, and the Higgs invisible decay can be $2\%$ at most. (iii) For the $h_2$ and $Z$ funnel with right relic density, the Higgs invisible decay can be about $0.4\%$ and $1\%$ respectively at most. (iv) If Higgs invisible decay was discovered at the HL-LHC, the four funnel-annihilation mechanisms of light dark matter may be all excluded with $\tilde{\chi}^0_1$ as the only dark matter source.
Fast and efficient deterministic quantum state transfer between two remote mechanical resonators

The main challenge in deterministic quantum state transfer in long-distance quantum communications is the transmission losses in the communication channel. To overcome this limitation, here we use the adiabatic theorem and find a lossless evolution path between two remote mechanical modes. By adiabatic variation of the effective coupling strengths between the two nodes and the intermediate optical channel modes, we engineer a transmission path for the quantum state transfer that is decoupled from the decaying fiber modes. Using our proposed method we show that one obtains a quantum state transfer with high efficiency. Furthermore, to bypass the slow nature of the adiabatic process and its sensitivity to the mechanical damping and noise as well as the strength of the driving pulses, we develop the shortcut to adiabatic passage protocol for our proposed quantum state transfer. Our results show that the shortcut to adiabaticity provides an efficient and fast quantum state transfer even for small values of the coupling strength. We show that the performance of our protocol for long-distance quantum communications remains efficient for transferring the quantum states between two remote mechanical resonators being hundred meters apart.
Peer Effect of Middle School Students: A Natural Experiment Based on Puberty Developmental Maturity

Peer is an important factor affecting the academic performance of middle school students. Domestic research has discussed this issue, but there are still some shortcomings in solving endogeneity aspects. Therefore, this study used the random effect of natural changes in pubertal development and maturity of middle school students in two years adjacent to the same class to identify peer effects due to changes in pubertal development and maturity; at the same time, first-order difference and quantile regression are used to solve the endogeneity problems in general regression analysis. We found that girls’ developmental maturity is significantly negatively related to their performance, and boys’ developmental maturity is significantly positively related to their performance, which indicates that there is a gender difference in the impact of adolescent developmental maturity on academic performance of middle school students. In addition, the peer effect is also heterogeneous, that is, in overall, the lower the development maturity of girls in a class, the more conducive to the improvement of student performance; the higher the maturity of students in class, the more conducive to the improvement of student performance. The findings of this study have certain policy implications for adjusting the school age of school-age children. Best Evid Chin Edu 2019; 4(1):425-442. Doi: 10.15354/bece.20. ar019.