Miracle-dz/UniFAR-FTUs huggingface dataset:
Calculating Question Similarity is Enough: A New Method for KBQA Tasks

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base. The core idea is to find the link between the internal knowledge behind questions and known triples of the knowledge base. Traditional KBQA task pipelines contain several steps, including entity recognition, entity linking, answering selection, etc. In this kind of pipeline methods, errors in any procedure will inevitably propagate to the final prediction. To address this challenge, this paper proposes a Corpus Generation - Retrieve Method (CGRM) with Pre-training Language Model (PLM) for the KBQA task. The major novelty lies in the design of the new method, wherein our approach, the knowledge enhanced T5 (kT5) model aims to generate natural language QA pairs based on Knowledge Graph triples and directly solve the QA by retrieving the synthetic dataset. The new method can extract more information about the entities from PLM to improve accuracy and simplify the processes. We test our method on NLPCC-ICCPOL 2016 KBQA dataset, and the results show that our method improves the performance of KBQA and the out straight-forward method is competitive with the state-of-the-art.
D^2LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D$^2$LV, unsupervised pre-training substitutes the commonly-used supervised one. When training, we design a set of basic and six advanced transformations, and a simple but effective baseline learns robust representation. During testing, a global-local and local-global matching strategy is proposed. The strategy performs local-verification between reference and query images. Experiments demonstrate that the proposed method is effective. The proposed approach ranks first out of 1,103 participants on the Facebook AI Image Similarity Challenge: Matching Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track1-Submission.
Control Barrier Function Augmentation in Sampling-based Control Algorithm for Sample Efficiency

For a nonlinear stochastic path planning problem, sampling-based algorithms generate thousands of random sample trajectories to find the optimal path while guaranteeing safety by Lagrangian penalty methods. However, the sampling-based algorithm can perform poorly in obstacle-rich environments because most samples might violate safety constraints, invalidating the corresponding samples. To improve the sample efficiency of sampling-based algorithms in cluttered environments, we propose an algorithm based on model predictive path integral control and control barrier function (CBF). The proposed algorithm needs fewer samples and time-steps and has a better performance in cluttered environments compared to the original model predictive path integral control algorithm.
Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

In low-latency or mobile applications, lower computation complexity, lower memory footprint and better energy efficiency are desired. Many prior works address this need by removing redundant parameters. Parameter quantization replaces floating-point arithmetic with lower precision fixed-point arithmetic, further reducing complexity. Typical training of quantized weight neural networks starts from fully quantized weights. Quantization creates random noise. As a way to compensate for this noise, during training, we propose to quantize some weights while keeping others in floating-point precision. A deep neural network has many layers. To arrive at a fully quantized weight network, we start from one quantized layer and then quantize more and more layers. We show that the order of layer quantization affects accuracies. Order count is large for deep neural networks. A sensitivity pre-training is proposed to guide the layer quantization order. Recent work in weight binarization replaces weight-input matrix multiplication with additions. We apply the proposed iterative training to weight binarization. Our experiments cover fully connected and convolutional networks on MNIST, CIFAR-10 and ImageNet datasets. We show empirically that, starting from partial binary weights instead of from fully binary ones, training reaches fully binary weight networks with better accuracies for larger and deeper networks. Layer binarization in the forward order results in better accuracies. Guided layer binarization can further improve that. The improvements come at a cost of longer training time.
Effects of flares on the habitable zones of M dwarfs accessible to
 TESS
 planet detections

This is the pre-peer reviewed version of the following article: M. Bogner, B. Stelzer, and St. Raetz (2021), Effects of flares on the habitable zones of M dwarfs accessible to TESS planet detections, which has been published in final form at doi.org/10.1002/asna.20210079. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Photometric space missions like Kepler and TESS continuously discover new exoplanets and advance the search for a second habitable world. The light curves recorded by these telescopes also reveal signs of magnetic activity, such as star spot modulation and flares, which can influence habitability. Searching for these characteristics, we analyzed TESS light curves of 112 M dwarfs selected according to the criterion that TESS can spot planet transits over their entire habitable zone (HZ). We detected 2532 flare events, occurring on 35 stars, thus the flare incidence rate is ≈ 32%. For only ≈ 11% of our stars, we found rotation periods. We calculated bolometric flare energies and luminosities, flare energy frequency distributions (FFDs) and the bolometric flux reaching the HZ at the peak of the flare. We estimated the effects of flaring on the atmosphere of an Earth-like planet in the HZ in the view of both ozone depletion and the enabling of chemical reactions necessary to build ribonucleic acid (RNA). None of our targets exhibits highly energetic flares at a frequency large enough to trigger ozone depletion or RNA formation. The flux reaching the inner HZ edge during flare events goes up to ≈ 4.5 times the quiescent solar flux at 1 au.
AI in Games: Techniques, Challenges and Opportunities

—With breakthrough of AlphaGo, AI in human-computer game has become a very hot topic attracting researchers all around the world, which usually serves as an effective standard for testing artiﬁcial intelligence. Various game AI systems (AIs) have been developed such as Libratus, OpenAI Five and AlphaStar, beating professional human players. In this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, ﬁrst-person shooting game AIs and real time strategy game AIs. Through this survey, we 1) compare the main difﬁculties among different kinds of games for the intelligent decision making ﬁeld ; 2) illustrate the mainstream frameworks and techniques for developing professional level AIs; 3) raise the challenges or drawbacks in the current AIs for intelligent decision making; and 4) try to propose future trends in the games and intelligent decision making techniques. Finally, we hope this brief review can provide an introduction for beginners, inspire insights for researchers in the ﬁled of AI in games.
Critical spin dynamics of Heisenberg ferromagnets revisited

We calculate the dynamic structure factor $S (\boldsymbol{k},\omega)$ in the paramagnetic regime of quantum Heisenberg ferromagnets for temperatures $T$ close to the critical temperature $T_c$ using our recently developed functional renormalization group approach to quantum spin systems. In $d=3$ dimensions we find that for small momenta $\boldsymbol{k}$ and frequencies $\omega$ the dynamic structure factor assumes the scaling form $S(\boldsymbol{k},\omega) = (\tau T G (\boldsymbol{k})/\pi)\Phi (k\xi, \omega\tau)$, where $ G (\boldsymbol{k})$ is the static spin-spin correlation function, $\xi$ is the correlation length, and the characteristic time-scale $\tau$ is proportional to $\xi^{5/2}$. We explicitly calculate the dynamic scaling function $\Phi (x,y)$ and find satisfactory agreement with neutron scattering experiments probing the critical spin dynamics in EuO and EuS. Precisely at the critical point where $\xi = \infty$ our result for the dynamic structure factor can be written as $S (\boldsymbol{k},\omega) = (\pi\omega_k)^{-1} T_c G (\boldsymbol{k}) \Psi_c (\omega/\omega_k)$, where $\omega_k \propto k^{5/2}$. We find that $\Psi_c(\nu)$ vanishes as $\nu^{-13/5}$ for large $\nu$, and as $\nu^{3/5}$ for small $\nu$. While the large-frequency behavior of $\Psi_c (\nu)$ is consistent with calculations based on mode-coupling theory and with perturbative renormalization group calculations to second order in $\epsilon = 6-d$, our result for small frequencies disagrees with previous calculations. We argue that up until now neither experiments nor numerical simulations are sufficiently accurate to determine the low-frequency behavior of $\Psi_c (\nu)$. We also calculate the low-temperature behavior of $S ( \boldsymbol{k},\omega)$ in one- and two dimensional ferromagnets and find that it satisfies dynamic scaling with exponent $z=2$ and exhibits a pseudogap for small frequencies.
On equivalence between linear-chain conditional random fields and hidden Markov chains

Practitioners successfully use hidden Markov chains (HMCs) in different problems for about sixty years. HMCs belong to the family of generative models and they are often compared to discriminative models, like conditional random fields (CRFs). Authors usually consider CRFs as quite different from HMCs, and CRFs are often presented as interesting alternative to HMCs. In some areas, like natural language processing (NLP), discriminative models have completely supplanted generative models. However, some recent results show that both families of models are not so different, and both of them can lead to identical processing power. In this paper we compare the simple linear-chain CRFs to the basic HMCs. We show that HMCs are identical to CRFs in that for each CRF we explicitly construct an HMC having the same posterior distribution. Therefore, HMCs and linear-chain CRFs are not different but just differently parametrized models.
Fault Diagnosis of Nonlinear Systems using a Hybrid-Degree Dual Cubature-based Estimation Scheme

In this paper a novel hybrid-degree dual cubature-based nonlinear filtering methodology is proposed for fault diagnosis of nonlinear systems subject to multiplicative component faults. Distinct from conventional dual estimation schemes, the nonlinear functions are approximated with cubature rules to achieve a designated and case-dependent degree of accuracy. Our methodology is motivated from two primary observations: (i) dynamic characteristics of system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the system dynamics and vary when approximate high-dimensional integrations are utilized. The boundedness of the estimation error covariance and stability analysis are formally investigated in presence of approximation errors due to cubature rules, uncertainties, and noise. The effectiveness of our proposed methodology is evaluated by application to a gas turbine engine for addressing the multi-mode component fault diagnosis problem within an integrated fault detection, isolation and identification framework. Case studies are provided to substantiate the superiority of the proposed methodology when compared with those of other representative filters including the Unscented Kalman Filters (UKF) and Particle Filters (PF).
HgTe quantum wells for QHE metrology under soft cryomagnetic conditions: permanent magnets and liquid ${^4He}$ temperatures

HgTe quantum wells with a thickness of ~7 nm may have a graphene-like band structure and have been recently proposed to be potential candidates for quantum Hall effect (QHE) resistance standards under condition of operation in the fields above certain critical field B c , above which the topological phase (with parasitic edge conduction) disappears . We present experimental studies of the magnetoresistance of different of HgTe quantum wells as a function temperature and magnetic field, determining the critical magnetic field B c . We demonstrate that for QWs of specific width B c becomes low enough to grant observation of remarkably wide QHE plateaus at the filling factor  = – 1 (holes) in relaxed cryomagnetic conditions: while using commercial 0.8T Neodymium permanent magnets and temperature of a few Kelvin provided by 4 He liquid system only. Realistic band structure calculations evidence that the observed phenomena can be qualitatively understood by considering two types of the charge carries – the light and heavy holes. The formers are attributed to the Γ point of the Brillouin zone, while the latters are caused by the side maxima of the valence band. Our work clearly shows that the peculiar band structure of HgTe QWs make them a favorable platform for developing metrological devices under soft cryomagnetic conditions.
A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design

Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.
An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2

We present an analysis of the eye tracking signal quality of the HoloLens 2’s integrated eye tracker. Signal quality was measured from eye movement data captured during a random saccades task from a new eye movement dataset collected on 30 healthy adults. We characterize the eye tracking signal quality of the device in terms of spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. Most notably, our evaluation of spatial accuracy reveals that the eye movement data in our dataset appears to be uncalibrated. Recalibrating the data using a subset of our dataset task produces notably better eye tracking signal quality.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks

In wireless multi-hop networks, delay is an important metric for many applications. However, the max-weight scheduling algorithms in the literature typically focus on instantaneous optimality, in which the schedule is selected by solving a maximum weighted independent set (MWIS) problem on the interference graph at each time slot. These myopic policies perform poorly in delay-oriented scheduling, in which the dependency between the current backlogs of the network and the schedule of the previous time slot needs to be considered. To address this issue, we propose a delay-oriented distributed scheduler based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module generates node embeddings that capture the network topology as well as multi-step lookahead backlogs, before calling a distributed greedy MWIS solver. In small- to medium-sized wireless networks with heterogeneous transmit power, where a few central links have many interfering neighbors, our proposed distributed scheduler can outperform the myopic schedulers based on greedy and instantaneously optimal MWIS solvers, with good generalizability across graph models and minimal increase in communication complexity.
Speech Emotion Recognition System by Quaternion Nonlinear Echo State Network

Department of Computer Engineering, Razi University, Kermanshah, IRAN F.daneshfar@uok.ac. ir, Kabudian@razi.ac. ir Echo state network (ESN) is a powerful and efficient tool for displaying dynamic data. However, many existing ESNs have limitations for properly modeling high-dimensional data. The most important limitation of these networks is the high memory consumption due to their reservoir structure, which has prevented the increase of reservoir units and the maximum use of special capabilities of this type of networks. One way to solve this problem is to use quaternion algebra. Because quaternions have four different dimensions, high-dimensional data are easily represented and, using Hamilton’s multiplication, with fewer parameters than real numbers, make external relations between the multidimensional features easier. In addition to the memory problem in the ESN network, the linear output of the ESN network poses an indescribable limit to its processing capacity, as it cannot effectively utilize higher-order statistics of features provided by the nonlinear dynamics of reservoir neurons. In this research, a new structure based on ESN is presented, in which quaternion algebra is used to compress the network data with the simple split function, and the output linear combiner is replaced by a multidimensional bilinear filter. This filter will be used for nonlinear calculations of the output layer of the ESN. In addition, the two-dimensional principal component analysis (2dPCA) technique is used to reduce the number of data transferred to the bilinear filter. In this study, the coefficients and the weights of the quaternion nonlinear ESN (QNESN) are optimized using genetic algorithm (GA). In order to prove the effectiveness of the proposed model compared to the previous methods, experiments for speech emotion recognition (SER) have been performed on EMODB, SAVEE and IEMOCAP speech emotional datasets. Comparisons show that the proposed QNESN network performs better than the ESN and most currently SER systems.
Real-time Emotion and Gender Classification using Ensemble CNN

Analysing expressions on the person's face plays a very vital role in identifying emotions and behavior of a person. Recognizing these expressions automatically results in a crucial component of natural human-machine interfaces. Therefore research in this field has a wide range of applications in bio-metric authentication, surveillance systems , emotion to emoticons in various social media platforms. Another application includes conducting customer satisfaction surveys. As we know that the large corporations made huge investments to get feedback and do surveys but fail to get equitable responses. Emotion&Gender recognition through facial gestures is a technology that aims to improve product and services performance by monitoring customer behavior to specific products or service staff by their evaluation. In the past few years there have been a wide variety of advances performed in terms of feature extraction mechanisms , detection of face and also expression classification techniques. This paper is the implementation of an Ensemble CNN for building a real-time system that can detect emotion and gender of the person. The experimental results shows accuracy of 68% for Emotion classification into 7 classes (angry, fear , sad , happy , surprise , neutral , disgust) on FER-2013 dataset and 95% for Gender classification (Male or Female) on IMDB dataset. Our work can predict emotion and gender on single face images as well as multiple face images. Also when input is given through webcam our complete pipeline of this real-time system can take less than 0.5 seconds to generate results.
Positive periodic solutions for systems of impulsive delay differential equations

<p style='text-indent:20px;'>A class of periodic differential <inline-formula><tex-math id="M1">\begin{document}$ n $\end{document}</tex-math></inline-formula>-dimensional systems with patch structure with (possibly infinite) delay and nonlinear impulses is considered. These systems incorporate very general nonlinearities and impulses whose signs may vary. Criteria for the existence of at least one positive periodic solution are presented, extending and improving previous ones established for the scalar case. Applications to systems inspired in mathematical biology models, such as impulsive hematopoiesis and Nicholson-type systems, are also included. </p>
Geometric PID Controller for Stabilization of Nonholonomic Mechanical Systems on Lie Groups

The PID controller is an elegant and versatile controller for set point tracking in double integrator systems of which mechanical systems evolving on Euclidean space constitute a large class. But since mechanical systems are typically constrained interconnections of rigid bodies whose configuration space is $SE(3)$, which is not even topologically Euclidean, a geometric PID controller has been developed for mechanical systems evolving on Lie groups. In this work, we extend the framework to such systems which have nonholonomic constraints. It encompasses many practically applicable mechanical systems encountered in robotics as robots are constrained interconnections of rigid bodies where the constraints could either be holonomic or nonholonomic.
Hierarchical clustering by aggregating representatives in sub-minimum-spanning-trees

One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation. However, conventional hierarchical clustering approaches have adopted some simple tricks to select the"representative"points which might not be as representative as enough. Thus, the constructed cluster tree is less attractive in terms of its poor robustness and weak reliability. Aiming at this issue, we propose a novel hierarchical clustering algorithm, in which, while building the clustering dendrogram, we can effectively detect the representative point based on scoring the reciprocal nearest data points in each sub-minimum-spanning-tree. Extensive experiments on UCI datasets show that the proposed algorithm is more accurate than other benchmarks. Meanwhile, under our analysis, the proposed algorithm has O(nlogn) time-complexity and O(logn) space-complexity, indicating that it has the scalability in handling massive data with less time and storage consumptions.