Miracle-dz/UniFAR-FTUs huggingface dataset:
On the asymptotic distribution of model averaging based on information criterion

Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are very widely used in model averaging and are very easily to implement. Especially, the optimal model averaging method MMA and JMA have only been well developed in linear models. Only by modifying, they can be applied to other models. But S-AIC and S-BIC can be used in all situations where AIC and BIC can be calculated. In this paper, we study the asymptotic behavior of two commonly used model averaging estimators, the S-AIC and S-BIC estimators, under the standard asymptotic with general fixed parameter setup. In addition, the resulting coverage probability in Buckland et al. (1997) is not studied accurately, but it is claimed that it will be close to the intended. Our derivation make it possible to study accurately. Besides, we also prove that the confidence interval construction method in Hjort and Claeskens (2003) still works in linear regression with normal distribution error. Both the simulation and applied example support our theory conclusion.
AbsPoseLifter: Absolute 3D Human Pose Lifting Network from a Single Noisy 2D Human Pose

This study presents a new network (i.e., PoseLifter) that can lift a 2D human pose to an absolute 3D pose in a camera coordinate system. The proposed network estimates the absolute 3D location of a target subject and generates an improved 3D relative pose estimation compared with existing pose-lifting methods. Using the PoseLifter with a 2D pose estimator in a cascade fashion can estimate a 3D human pose from a single RGB image. In this case, we empirically prove that using realistic 2D poses synthesized with the real error distribution of 2D body joints considerably improves the performance of our PoseLifter. The proposed method is applied to public datasets to achieve state-of-the-art 2D-to-3D pose lifting and 3D human pose estimation.
CONNA: Addressing Name Disambiguation on the Fly

Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly — to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework — CONNA — to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21-19.84 percent improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner — a large online academic search system.
The Spin of the Black Hole GRS 1716-249 Determined from the Hard Intermediate State

We present three simultaneous/quasi-simultaneous NuSTAR and Swift data sets of the black hole (BH) GRS 1716-249 in its hard intermediate state. The accretion disk in this state may have reached the innermost stable circular orbit, and the NuSTAR spectra show a broad relativistic iron line and a strong Compton hump. To measure the BH spin, we construct a joint model consisting of a relativistic disk model kerrbb and a reflection model relxill, to fit the continuum and the reflection components, respectively. By applying this model to each data set independently, a consistent result is obtained on the BH spin and the disk inclination. The BH spin is a* ≳ 0.92, and the inclination angle (i) is around 40°–50°, based on the measurements of all data sets. In the third data set, a high BH mass (MBH) is strongly disfavored by the spectral fits. By unfreezing the BH mass, we find a* > 0.92, , and MBH < 8.0 M⊙, at a 90% confidence level. Considering the lower limit derived from a previous optical constraint, MBH is in a range of 4.9–8.0 M⊙.
Modeling Inter-Speaker Relationship in XLNet for Contextual Spoken Language Understanding

We propose two methods to capture relevant history information in a multi-turn dialogue by modeling inter-speaker relationship for spoken language understanding (SLU). Our methods are tailored for and therefore compatible with XLNet, which is a state-of-the-art pretrained model, so we verified our models built on the top of XLNet. In our experiments, all models achieved higher accuracy than state-of-the-art contextual SLU models on two benchmark datasets. Analysis on the results demonstrated that the proposed methods are effective to improve SLU accuracy of XLNet. These methods to identify important dialogue history will be useful to alleviate ambiguity in SLU of the current utterance.
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems

We present a non-trivial integration of dimension-independent likelihood-informed (DILI) MCMC (Cui, Law, Marzouk, 2016) and the multilevel MCMC (Dodwell et al., 2015) to explore the hierarchy of posterior distributions. This integration offers several advantages: First, DILI-MCMC employs an intrinsic likelihood-informed subspace (LIS) (Cui et al., 2014)—which involves a number of forward and adjoint model simulations—to design accelerated operator-weighted proposals. By exploiting the multilevel structure of the discretised parameters and discretised forward models, we design a Rayleigh–Ritz procedure to significantly reduce the computational effort in building the LIS and operating with DILI proposals. Second, the resulting DILI-MCMC can drastically improve the sampling efficiency of MCMC at each level, and hence reduce the integration error of the multilevel algorithm for fixed CPU time. Numerical results confirm the improved computational efficiency of the multilevel DILI approach.
Building mental health promotion capacity in health care: results from Phase I of a workforce development project

The development of mental-health-promoting health care systems is dependent on having a skilled and informed workforce to effectively integrate mental health promotion (MHP) into programme and service delivery. This paper describes Phase I (September 2009-July 2010) of Health Compass, an innovative, multi-phased project that aims to transform health care practice and shift organizational culture by enhancing the capacity of health care providers to further promote patient, client and family mental well-being. Phase I of Health Compass examined the current state of MHP within British Columbia's Provincial Health Service Authority health care services. The findings, based on group discussions and key informant interviews, examined health care providers' current understanding and knowledge of MHP; identified existing strategies, facilitators and barriers that help or hinder the incorporation of MHP into health care practice and services; and identified preferred learning modalities for development and piloting of future MHP resources in Phases II and III.
Deep Learning Models for Digital Pathology

Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. However, diagnostics from histopathology images generally rely on a visual cognitive assessment of tissue slides which implies an inherent element of interpretation and hence subjectivity. Access to digitized histopathology images enabled the development of computational systems aiming at reducing manual intervention and automating parts of pathologists' workflow. Specifically, applications of deep learning to histopathology image analysis now offer opportunities for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However digitized histopathology tissue slides are unique in a variety of ways and come with their own set of computational challenges. In this survey, we summarize the different challenges facing computational systems for digital pathology and provide a review of state-of-the-art works that developed deep learning-based solutions for the predictive modeling of histopathology images from a detection, stain normalization, segmentation, and tissue classification perspective. We then discuss the challenges facing the validation and integration of such deep learning-based computational systems in clinical workflow and reflect on future opportunities for histopathology derived image measurements and better predictive modeling.
Power Allocation and User Assignment Scheme for Beyond 5G Heterogeneous Networks

The issue of spectrum scarcity in wireless networks is becoming prominent and critical with each passing year. Although several promising solutions have been proposed to provide a solution to spectrum scarcity, most of them have many associated tradeoffs. In this context, one of the emerging ideas relates to the utilization of cognitive radios (CR) for future heterogeneous networks (HetNets). This paper provides a marriage of two promising candidates (i.e., CR and HetNets) for beyond fifth generation (5G) wireless networks. More specifically, a joint power allocation and user assignment solution for the multiuser underlay CR-based HetNets has been proposed and evaluated. To counter the limiting factors in these networks, the individual power of transmitting nodes and interference temperature protection constraints of the primary networks have been considered. An efficient solution is designed from the dual decomposition approach, where the optimal user assignment is obtained for the optimized power allocation at each node. The simulation results validate the superiority of the proposed optimization scheme against conventional baseline techniques.
An Efficient Framework for Implementing Persist Data Structures on Remote NVM

The byte-addressable Non-Volatile Memory (NVM) is a promising technology since it simultaneously provides DRAM-like performance, disk-like capacity, and persistency. In this paper, we rethink the current symmetric NVM deployment which directly attaches NVM devices with servers. Following the trend of disaggregation, we propose \emph{rNVM}, a new asymmetric deployment of NVM devices, that decouples servers from persistent data storage. In this architecture, the NVM devices can be shared by multiple servers and provide recoverable persistent data structures. We develop a prototype of \emph{rNVM} leveraging the advantages of the powerful RDMA-enabled network that naturally fits the asymmetric deployment model. With several optimizations, we demonstrate that \emph{rNVM} can achieve comparable performance as symmetric deployment while enjoying the benefits of the large data size not limited by local memory, high availability and shared data structures. Specifically, thanks to operation batching, local memory caching and efficient concurrency control, the throughput of operations on eight widely used data structures is improved by 6$\sim$22 $\times$ without lowering the consistency promising.
Ozone Therapy- A Paradigm Shift in Dentistry.

Ozone is a colorless gas form of oxygen and is present in atmosphere. It is a powerful oxidizer. It effectively kills bacteria, fungi, viruses and parasites at a dramatically lower concentration. So far, arresting and reversing the process of caries without invasive treatment are unpredictable and rely highly on patient compliance. Most people suffer anxieties about being treated for tooth decay or more precisely; they fear the injections and drills, but, now, with ozone treatment, this is all the thing of the past. Ozone possibly could arrest the progress of the lesion and may, in the presence of fluoride, perhaps allow re-mineralization to occur which in turn delay or prevent the need for traditional methods of dental conservation. The versatility of ozone therapy, its unique properties, noninvasive nature, absence of side effects or adverse reactions are responsible for its wide spread use like treatment of caries, bleaching of discoloured teeth, desensitization and treatment of some soft tissue infections. Treating patients with ozone cuts off the treatment time with a great deal of difference, it eliminates the bacterial count more precisely and moreover, completely painlessly, hence, increasing the patients’ acceptability and compliance.
Modelling the stochasticity of high-redshift halo bias

A very large dynamic range with simultaneous capture of both large and small scales in the simulations of cosmic structures is required for correct modelling of many cosmological phenomena, particularly at high redshift. This is not always available, or when it is, it makes such simulations very expensive. We present a novel subgrid method for modelling low-mass ($10^5\, {\rm M}_\odot \le M_{\rm halo}\le 10^9\, {\rm M}_\odot$) haloes, which are otherwise unresolved in large-volume cosmological simulations limited in numerical resolution. In addition to the deterministic halo bias that captures the average property, we model its stochasticity that is correlated in time. We find that the instantaneous binned distribution of the number of haloes is well approximated by a lognormal distribution, with overall amplitude modulated by this ‘temporal correlation bias’. The robustness of our new scheme is tested against various statistical measures, and we find that temporally correlated stochasticity generates mock halo data that is significantly more reliable than that from temporally uncorrelated stochasticity. Our method can be applied for simulating processes that depend on both the small- and large-scale structures, especially for those that are sensitive to the evolution history of structure formation such as the process of cosmic reionization. As a sample application, we generate a mock distribution of medium-mass (108 ≤ M/M⊙ ≤ 109) haloes inside a 500 Mpc $\, h^{-1}$, 3003 grid simulation box. This mock halo catalogue bears a reasonable statistical agreement with a halo catalogue from numerically resolved haloes in a smaller box, and therefore will allow a very self-consistent sets of cosmic reionization simulations in a box large enough to generate statistically reliable data.
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning

The design of recommendation strategies in the adaptive learning systems focuses on utilizing currently available information to provide learners with individual-specific learning instructions. As a critical motivate for human behaviours, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we propose a curiosity-driven recommendation policy within the reinforcement learning framework, allowing for an efficient and enjoyable personalized learning path. Specifically, a curiosity reward from a well-designed predictive model is generated to model one's familiarity with the knowledge space. Given such curiosity rewards, we apply the actor-critic method to approximate the policy directly through neural networks. Numerical analyses with a large continuous knowledge state space and concrete learning scenarios are provided to further demonstrate the efficiency of the proposed method.
Greedy Sparse Signal Recovery Algorithm Based on Bit-wise MAP detection

We propose a novel greedy algorithm for the support recovery of a sparse signal from a small number of noisy measurements. In the proposed method, a new support index is identified for each iteration based on bit-wise maximum a posteriori (B-MAP) detection. This is optimal in the sense of detecting one of the remaining support indices, provided that all the detected indices in the previous iterations are correct. Despite its optimality, it requires an expensive complexity for computing the maximization metric (i.e., a posteriori probability of each remaining support) due to the marginalization of high-dimensional sparse vector. We address this problem by presenting a good proxy (named B-MAP proxy) on the maximization metric which is accurate enough to find the maximum index, rather than an exact probability, Moreover, it is easily evaluated only using vector correlations as in orthogonal matching pursuit (OMP), but the use completely different proxy matrices for maximization. We demonstrate that the proposed B-MAP detection provides a significant gain compared with the existing methods as OMP and MAP-OMP, having the same complexity. Subsequently, we construct the advanced greedy algorithms, based on B-MAP proxy, by leveraging the idea of compressive sampling matching pursuit (CoSaMP) and subspace pursuit (SP). Via simulations, we show that the proposed method outperforms also OMP and MAP-OMP under the frameworks of the advanced greedy algorithms.
A Comparison of Neural Network Training Methods for Text Classification

We study the impact of neural networks in text classification. Our focus is on training deep neural networks with proper weight initialization and greedy layer-wise pretraining. Results are compared with 1-layer neural networks and Support Vector Machines. We work with a dataset of labeled messages from the Twitter microblogging service and aim to predict weather conditions. A feature extraction procedure specific for the task is proposed, which applies dimensionality reduction using Latent Semantic Analysis. Our results show that neural networks outperform Support Vector Machines with Gaussian kernels, noticing performance gains from introducing additional hidden layers with nonlinearities. The impact of using Nesterov's Accelerated Gradient in backpropagation is also studied. We conclude that deep neural networks are a reasonable approach for text classification and propose further ideas to improve performance.
A* with Perfect Potentials

Quickly determining shortest paths in networks is an important ingredient for many routing applications. While Dijkstra's algorithm can be used to solve these problems, it is too slow for many practical problems. A* is an extension to Dijkstra's algorithm. It uses a potential function to estimate the distance of each node to the target. By adding these estimates to the queue keys, the search is directed towards the target. The quality of this potential determines the performance of A*. We introduce a novel way to efficiently calculate perfect potentials for extended problem settings where a lower bound graph is available. For example, in the case of routing with live traffic, this could be the free flow graph.
Low-Cost GPS-Aided LiDAR State Estimation and Map Building

Using different sensors in an autonomous vehicle (AV) can provide multiple constraints to optimize AV location estimation. In this paper, we present a low-cost GPS-assisted LiDAR state estimation system for AVs. Firstly, we utilize LiDAR to obtain highly precise 3D geometry data. Next, we use an inertial measurement unit (IMU) to correct point cloud misalignment caused by incorrect place recognition. The estimated LiDAR odometry and IMU measurement are then jointly optimized. We use a low-cost GPS instead of a realtime kinematic (RTK) module to refine the estimated LiDAR-inertial odometry. Our low-cost GPS and LiDAR complement each other, and can provide highly accurate vehicle location information. Moreover, a low-cost GPS is much cheaper than an RTK module, which reduces the overall AV sensor cost. Our experimental results demonstrate that our proposed GPS-aided LiDAR-inertial odometry system performs very accurately. The accuracy achieved when processing a dataset collected in an industrial zone is approximately 0.14 m.
Using reinforcement learning to minimize taxi idle times

Abstract Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis should follow in order to minimize their idle times are hard to calculate; they depend on complex effects like passenger demand, traffic conditions, and inter-taxi competition. Here we explore if reinforcement learning (RL) can be used for this purpose. Using real-world data from three major cities, we show RL-taxis can indeed learn to minimize their idle times in different environments. In particular, a single RL-taxi competing with a population of regular taxis learns to out-perform its rivals.
S Y ] 20 O ct 2 01 5 Quadrotor Controller

In this paper we construct a trajectory tracking controller for a quadrotor system by finding a coordinate change which transforms the quadrotor’s vector field into that of a thust propelled system. In a thrust propelled system, th e goal is to stabilize its position around the origin, while the system is actuated by a one dimensional acceleration/thrus t along a direction vector, by a time-varying gravity, and by the angular acceleration of the direction vector. For this syst em, a solution has been proposed in [1] based on the implicit knowledge of a bounded controller for a double integrator system, and on the implicit knowledge of a Lyapunov function that guarantees the origin is asymptotically stable for thedouble integrator controlled by the bounded controller. In this paper, we present two alternative bounded controllers for a double integrator system, and corresponding Lyapunov functions. I. I NTRODUCTION Many controllers for trajectory tracking of aerial vehicle s have been proposed. Controllers based on a linearized model around the hover condition have been successfully deployed , but their applicability is limited to trajectories where th e flight envelop remains in small angles [2], [3]. Controllers based on an inner and outer loop strategy have also been successfully applied, where the inner loop is responsible f or attitude control loop, while the outer loop is responsible f or the position control [4]. The quadrotor dynamics depend on the vehicle’s rotation matrix, most control strategies als o provide a control law for the space corresponding to the yaw motion. Different parameterizations for the vehicle’s rot ation matrix have also been used, such as euler angles [4], and unit quaternions [5], [6]. Controllers that guarantee traj ectory tracking for all initial conditions, can also be found [6]. II. M ODELING A sketch of a simplified model of a quadrotor is presented in Fig. 1. We denotep ∈ R as the quadrotor’s positions, v(t) := ṗ(t) as the quadrotor’s velocity, and m ∈ R >0 as the quadrotor’s mass. Also we denote R ∈ SO(3) as the quadrotor’s rotation matrix,r3 := Re3 ∈ S as the quadrotor’s direction where thrust is provided, andJ = J ∈ R as the quadrotor matrix of inertia. Finally, we denote T ∈ R as the quadrotor’s thrust, τ ∈ R as the quadrotor’s torque, whereT , τ andτm are all assumed to be a control inputs. The authors are with the School of Electrical Engineering, K TH Royal Institute of Technology, SE-100 44, Stockholm, Sweden. {ppereira, boskos, dimos}@kth.se. This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation P r gramme under the Grant Agreement No.644128 (AEROWORKS). p p/m: Position/mass Tr T : Thrust r: Thust direction