Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control

Today's fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement.
Probing the Properties of the Pulsar Wind in the Gamma-Ray Binary HESS J0632+057 with NuSTAR and VERITAS Observations

HESS J0632+057 is a gamma-ray binary composed of a compact object orbiting a Be star with a period of about 315 days. Extensive X-ray and TeV gamma-ray observations have revealed a peculiar light curve containing two peaks, separated by a dip. We present the results of simultaneous observations in hard X-rays with NuSTAR and in TeV gamma-rays with VERITAS, performed in 2017 November and December. These observations correspond to the orbital phases ϕ ≈ 0.22 and 0.3, where the fluxes are rising toward the first light-curve peak. A significant variation of the spectral index from 1.77 ± 0.05 to 1.56 ± 0.05 is observed in the X-ray data. The multiwavelength spectral energy distributions (SED) derived from the observations are interpreted in terms of a leptonic model, in which the compact object is assumed to be a pulsar and nonthermal radiation is emitted by high-energy electrons accelerated at the shock formed by the collision between the stellar and pulsar wind. The results of the SED fitting show that our data can be consistently described within this scenario, and allow us to estimate the magnetization of the pulsar wind at the location of the shock formation. The constraints on the pulsar wind magnetization provided by our results are shown to be consistent with those obtained from other systems.
Discovery of a soft X-ray lag in the ultraluminous X-ray source NGC 1313 X-1

Ultraluminous X-ray sources (ULXs) provide a unique opportunity to probe the geometry and energetics of super-Eddington accretion. The radiative processes involved in super-Eddington accretion are not well understood, and so studying correlated variability between different energy bands can provide insights into the causal connection between different emitting regions. We present a spectral-timing analysis of NGC 1313 X-1 from a recent XMM–Newton campaign. The spectra can be decomposed into two thermal-like components, the hotter of which may originate from the inner accretion disc, and the cooler from an optically thick outflow. We find correlated variability between hard (2–10 keV) and soft (0.3–2 keV) bands on kilosecond time-scales, and find a soft lag of ∼150 s. The covariance spectrum suggests that emission contributing to the lags is largely associated with the hotter of the two thermal-like components, likely originating from the inner accretion flow. This is only the third ULX to exhibit soft lags. The lags range over three orders of magnitude in amplitude, but all three are ∼5–20 per cent of the corresponding characteristic variability time-scales. If these soft lags can be understood in the context of a unified picture of ULXs, then lag time-scales may provide constraints on the density and extent of radiatively driven outflows.
Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection

Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects. Instead of directly regressing the four vertices, we glide the vertex of the horizontal bounding box on each corresponding side to accurately describe a multi-oriented object. Specifically, We regress four length ratios characterizing the relative gliding offset on each corresponding side. This may facilitate the offset learning and avoid the confusion issue of sequential label points for oriented objects. To further remedy the confusion issue for nearly horizontal objects, we also introduce an obliquity factor based on area ratio between the object and its horizontal bounding box, guiding the selection of horizontal or oriented detection for each object. We add these five extra target variables to the regression head of faster R-CNN, which requires ignorable extra computation time. Extensive experimental results demonstrate that without bells and whistles, the proposed method achieves superior performances on multiple multi-oriented object detection benchmarks including object detection in aerial images, scene text detection, pedestrian detection in fisheye images.
Privacy-Preserving Payment Splitting

Abstract Widely used payment splitting apps allow members of a group to keep track of debts between members by sending charges for expenses paid by one member on behalf of others. While offering a great deal of convenience, these apps gain access to sensitive data on users’ financial transactions. In this paper, we present a payment splitting app that hides all transaction data within a group from the service provider, provides privacy protections between users in a group, and provides integrity against malicious users or even a malicious server. The core protocol proceeds in a series of rounds in which users either submit real data or cover traffic, and the server blindly updates balances, informs users of charges, and computes integrity checks on user-submitted data. Our protocol requires no cryptographic operations on the server, and after a group’s initial setup, the only cryptographic tool users need is AES. We implement the payment splitting protocol as an Android app and the accompanying server. We find that, for realistic group sizes, it requires fewer than 50 milliseconds per round of computation on a user’s phone and the server requires fewer than 300 microseconds per round for each group, meaning that our protocol enjoys excellent performance and scalability properties.
Active gas features in three HSC-SSP CAMIRA clusters revealed by high angular resolution analysis of MUSTANG-2 SZE and XXL X-ray observations

We present results from simultaneous modelling of high angular resolution GBT/MUSTANG-2 90 GHz Sunyaev–Zel’dovich effect (SZE) measurements and XMM-XXL X-ray images of three rich galaxy clusters selected from the HSC-SSP Survey. The combination of high angular resolution SZE and X-ray imaging enables a spatially resolved multicomponent analysis, which is crucial to understand complex distributions of cluster gas properties. The targeted clusters have similar optical richnesses and redshifts, but exhibit different dynamical states in their member galaxy distributions: a single-peaked cluster, a double-peaked cluster, and a cluster belonging to a supercluster. A large-scale residual pattern in both regular Compton-parameter y and X-ray surface brightness distributions is found in the single-peaked cluster, indicating a sloshing mode. The double-peaked cluster shows an X-ray remnant cool core between two SZE peaks associated with galaxy concentrations. The temperatures of the two peaks reach ∼20–30 keV in contrast to the cool core component of ∼2 keV, indicating a violent merger. The main SZE signal for the supercluster is elongated along a direction perpendicular to the major axis of the X-ray core, suggesting a minor merger before core passage. The SX and y distributions are thus perturbed at some level, regardless of the optical properties. We find that the integrated Compton y parameter and the temperature for the major merger are boosted from those expected by the weak-lensing mass and those for the other two clusters show no significant deviations, which is consistent with predictions of numerical simulations.
Smoothed nested testing on directed acyclic graphs

We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesis must be false if the inner hypothesis is false. We model the nested structure as a directed acyclic graph, including chain and tree graphs as special cases. Each node in the graph is a hypothesis and rejecting a node requires also rejecting all of its ancestors. We propose a general framework for adjusting node-level test statistics using the known logical constraints. Within this framework, we study a smoothing procedure that combines each node with all of its descendants to form a more powerful statistic. We prove that a broad class of smoothing strategies can be used with existing selection procedures to control the familywise error rate, false discovery exceedance rate, or false discovery rate, so long as the original test statistics are independent under the null. When the null statistics are not independent, but are derived from positively correlated normal observations, we prove control for all three error rates when the smoothing method is an arithmetic averaging of the observations. Simulations and an application to a real biology dataset demonstrate that smoothing leads to substantial power gains.
Empirical Autopsy of Deep Video Captioning Frameworks

Contemporary deep learning based video captioning follows encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to its composite nature, the encoder-decoder pipeline provides the freedom of multiple choices for each of its components, e.g the choices of CNNs models, feature transformations, word embeddings, and language models etc. Component selection can have drastic effects on the overall video captioning performance. However, current literature is void of any systematic investigation in this regard. This article fills this gap by providing the first thorough empirical analysis of the role that each major component plays in a contemporary video captioning pipeline. We perform extensive experiments by varying the constituent components of the video captioning framework, and quantify the performance gains that are possible by mere component selection. We use the popular MSVD dataset as the test-bed, and demonstrate that substantial performance gains are possible by careful selection of the constituent components without major changes to the pipeline itself. These results are expected to provide guiding principles for future research in the fast growing direction of video captioning.
Continuous coexistency preservers on effect algebras

Let H be a finite-dimensional Hilbert space, dim H ⩾ 2. We prove that every continuous coexistency preserving map on the effect algebra E(H) is either a standard automorphism of E(H), or a standard automorphism of E(H) composed with the orthocomplementation. We present examples showing the optimality of the result.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means

Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-parametric models to estimate the value function, they learn rapidly, retrieving cached values from similar past states. In realistic scenarios, with limited resources and noisy data, maintaining meaningful representations in memory is essential to speed up the learning and avoid catastrophic forgetting. Unfortunately, EC methods have a large space and time complexity. We investigate different solutions to these problems based on prioritising and ranking stored states, as well as online clustering techniques. We also propose a new dynamic online k-means algorithm that is both computationally-efficient and yields significantly better performance at smaller memory sizes; we validate this approach on classic reinforcement learning environments and Atari games.
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization

Neural Architecture Search (NAS) has shown great potentials in finding a better neural network design than human design. Sample-based NAS is the most fundamental method aiming at exploring the search space and evaluating the most promising architecture. However, few works have focused on improving the sampling efficiency for a multi-objective NAS. Inspired by the nature of the graph structure of a neural network, we propose BOGCN-NAS, a NAS algorithm using Bayesian Optimization with Graph Convolutional Network (GCN) predictor. Specifically, we apply GCN as a surrogate model to adaptively discover and incorporate nodes structure to approximate the performance of the architecture. For NAS-oriented tasks, we also design a weighted loss focusing on architectures with high performance. Our method further considers an efficient multi-objective search which can be flexibly injected into any sample-based NAS pipelines to efficiently find the best speed/accuracy trade-off. Extensive experiments are conducted to verify the effectiveness of our method over many competing methods, e.g. 128.4x more efficient than Random Search and 7.8x more efficient than previous SOTA LaNAS for finding the best architecture on the largest NAS dataset NasBench-101.
Minimum time learning model predictive
 control

In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed‐loop system. We show how to construct a time varying safe set and terminal cost function using closed‐loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non‐decreasing performance. Computational efficiency is obtained by convexifing the time‐varying safe set and time‐varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.
A study of Higgs $\boldsymbol{CP}$ properties using the Higgs Characterization Model and top associated production

This study utilises the Higgs Characterization model to investigate the $CP$ properties of the Higgs coupling to the top quark using the $tH$ and $t\bar{t}H$ generation processes. This is done via simulations of proton-proton collisions with ATLAS detector conditions, which are calculated for seven different $CP$ eigenstates using MadGraph5_aMC@NLO. Three orthogonal categories of cuts are subsequently implemented to find the cut efficiencies, which are used in conjunction with the cross-section to find the signal strength. Comparing the signal strength with experimental results shows that one cannot yet conclusively rule out any Higgs $CP$ eigenstates. Analysing histograms showed that the most $CP$ sensitive variables were the transverse momenta of the Higgs, leptons and photons, which showed a hardening as the Higgs coupling became $CP$ odd.
Planck Far-infrared Detection of Hyper Suprime-Cam Protoclusters at z ∼ 4: Hidden AGN and Star Formation Activity

We perform a stacking analysis of Planck, AKARI, Infrared Astronomical Satellite, Wide-field Infrared Survey Explorer, and Herschel images of the largest number of (candidate) protoclusters at z ∼ 3.8 selected from the Hyper Suprime-Cam Subaru Strategic Program. Stacking the images of the 179 candidate protoclusters, the combined infrared (IR) emission of the protocluster galaxies in the observed 12–850 μm wavelength range is successfully detected with >5σ significance (at Planck). This is the first time that the average IR spectral energy distribution (SED) of a protocluster has been constrained at z ∼ 4. The observed IR SEDs of the protoclusters exhibit significant excess emission in the mid-IR compared to that expected from typical star-forming galaxies (SFGs). They are reproduced well using SED models of intense starburst galaxies with warm/hot dust heated by young stars, or by a population of active galactic nucleus (AGN)/SFG composites. For the pure star-forming model, a total IR (from 8–1000 μm) luminosity of 19.3 − 4.2 + 0.6 × 10 13 L ⊙ and a star formation rate of 16.3 − 7.8 + 1.0 × 10 3 M ⊙ yr−1 are found, whereas for the AGN/SFG composite model, 5.1 − 2.5 + 2.5 × 10 13 L ⊙ and 2.1 − 1.7 + 6.3 × 10 3 M ⊙ yr−1 are found. Uncertainty remains in the total SFRs; however, the IR luminosities of the most massive protoclusters are likely to continue increasing up to z ∼ 4. Meanwhile, no significant IR flux excess is observed around optically selected QSOs at similar redshifts, which confirms previous results. Our results suggest that the z ∼ 4 protoclusters trace dense, intensely star-forming environments that may also host obscured AGNs missed by the selection in the optical.
M51 ULX-7: superorbital periodicity and constraints on the neutron star magnetic field

In this work, we explore the applicability of standard theoretical models of accretion to the observed properties of M51 ULX-7. The spin-up rate and observed X-ray luminosity are evidence of a neutron star with a surface magnetic field of 2–7 × 1013 G, rotating near equilibrium. Analysis of the X-ray light curve of the system (Swift/XRT data) reveals the presence of a ∼39 d superorbital period. We argue that the superorbital periodicity is due to disc precession, and that material is accreted on to the neutron star at a constant rate throughout it. Moreover, by attributing this modulation to the free precession of the neutron star we estimate a surface magnetic field strength of 3–4 × 1013 G. The agreement of these two independent estimates provide strong constraints on the surface polar magnetic field strength of the NS.
Customized Graph Embedding: Tailoring the Embedding Vector to a Specific Application

The graph is a natural representation of data in a variety of real-world applications, for example as a knowledge graph, a social network, or a biological network. To better leverage the information behind the data, the method of graph embedding is recently proposed and extensively studied. The traditional graph embedding method, while it provides an effective way to understand what is behind the graph data, is unfortunately sub-optimal in many cases. This is because its learning procedure is disconnected from the target application. In this paper, we propose a novel approach, Customized Graph Embedding (CGE), to tackle this problem. The CGE algo-rithm learns a customized vector representation of the graph by differentiating the varying importance of distinct graph paths. Experiments are carried out on a diverse set of node classiﬁcation datasets and strong performance is demonstrated.
Remarkable interaction effects in quadratic and cubic nodal line fermion systems

Influence of short-range four-fermion interactions on quadratic and cubic nodal line fermion systems is studied by renormalization group theory. It is found that arbitrarily weak four-fermion interaction could drive quadratic or cubic nodal line fermion system to a new phase. According to the initial conditions and value of fermion flavor, the system may appear three kinds of instabilities. First, quadratic or cubic nodal line is split into conventional nodal lines, thus the system becomes nodal line semimetal; Second, finite excitonic gap is generated, and the system becomes an excitonic insulator; Third, the systems is driven into superconducting phase. Thus, quadratic and cubic nodal line fermion systems are rare strong correlated fermion systems in three dimension under the influence of four-fermion interactions. These theoretical results may be verified in the candidates for quadratic and cubic nodal line fermion systems.
Large-scale Multi-view Subspace Clustering in Linear Time

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.
Air, bone and soft-tissue Segmentation on 3D brain MRI Using Semantic Classification Random Forest with Auto-Context Model

As bone and air produce weak signals with conventional MR sequences, segmentation of these tissues particularly difficult in MRI. We propose to integrate patch-based anatomical signatures and an auto-context model into a machine learning framework to iteratively segment MRI into air, bone and soft tissue. The proposed semantic classification random forest (SCRF) method consists of a training stage and a segmentation stage. During training stage, patch-based anatomical features were extracted from registered MRI-CT training images, and the most informative features were identified to train a series of classification forests with auto-context model. During segmentation stage, we extracted selected features from MRI and fed them into the well-trained forests for MRI segmentation. The DSC for air, bone and soft tissue obtained with proposed SCRF were 0.976, 0.819 and 0.932, compared to 0.916, 0.673 and 0.830 with RF, 0.942, 0.791 and 0.917 with U-Net. SCRF also demonstrated superior segmentation performances for sensitivity and specificity over RF and U-Net for all three structure types. The proposed segmentation technique could be a useful tool to segment bone, air and soft tissue, and have the potential to be applied to attenuation correction of PET/MRI system, MRI-only radiation treatment planning and MR-guided focused ultrasound surgery.