Miracle-dz/UniFAR-FTUs huggingface dataset:
CLUE: A Chinese Language Understanding Evaluation Benchmark

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks. com
Multi-Objective Channel Allocation in Cognitive Radio Networks

This paper addresses the problem of channel allocation in Cognitive Radio (CR) networks. CR has been considered as a technology which improves spectrum utilization significantly by carrying out Dynamic Spectrum Management (DSM). One issue of the DSM is the using of frequency channels by secondary/CR users that are under-utilized and/or not used by primary users. CR users eager to use them when they are not used by primary users. The number of these spectrum bands is limited. This scarcity leads to conflict among CR users; As many as bands used by any CR user leads to decaying objective functions of other users. Thus, they have a destructive effect on each other. The paper models this conflict as a new multi-objective problem. Pareto set is attained via a Multi-Objective Optimization (MOO) technique, namely {\epsilon} - constraint method. Results show the efficiency of the method.
The Kinematics of Tracked Vehicles via the Power Dissipation Method

This paper develops a new quasi-static modeling framework for tracked robots based on the power dissipation method. Given a set of track speeds, this method predicts the vehicle's instantaneous rigid body motion. We introduce three specific models: a model for tracked operation on flat ground, a model for vehicle motion when the track's grouser tips touch the ground, and a model for operation on stairs. Experiments show that these models predict tracked vehicle motion more accurately than existing kinematic models, and predict phenomena which are not captured by other models. These novel models provide a basis for new feedback control approaches.
Polariton-polariton interaction beyond the Born approximation: A toy model study

We theoretically investigate the polariton-polariton interaction in microcavities beyond the commonly used Born approximation (i.e., mean field), by adopting a toy model with a contact interaction to approximately describe the attraction between electrons and holes in quantum well and by using a Gaussian pair fluctuation theory beyond mean field. We obtain a density or chemical potential independent polariton-polariton interaction strength even in two dimensions, which result from coupling to the photon field. We show that quantum fluctuations lead to about a factor of 2 reduction in the polariton-polariton interaction strength within our toy model. Together with corrections to the $1s$ exciton approximation at very strong light-matter coupling, we find the polariton-polariton interaction strength under typical experimental conditions is overestimated by a factor of 3 in the widely used theories, if our toy model can qualitatively simulate the polariton interaction in GaAs quantum wells. We compare our prediction with the most recent measurement and argue that the beyond-Born-approximation effect to the polariton-polariton interaction strength is crucial for a quantitative understanding of the experimental data by Estrecho et al. [Phys. Rev. B 100, 035306 (2019)].
A Review on Deep Learning Techniques for Video Prediction

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this article devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions.
Data Age Aware Scheduling for Wireless Powered Mobile-Edge Computing in Industrial Internet of Things

Wireless powered mobile-edge computing has been envisioned as a promising paradigm to enhance the computation capability of low-power wireless devices in industrial Internet of things. An efficient resource scheduling method is critical yet challenging to design in such a scenario due to stochastic traffic arrival, time-coupling uplink/downlink decision, and incomplete system state knowledge. To tackle these challenges, an online optimization algorithm is proposed in this article to maximize long-term system utility balancing throughput and fairness, subject to data age and stability constraints. A set of virtual queues is designed to transform the scheduling task, which is hard to solve due to time-dependent data age constraints, into a stochastic optimization problem. Leveraging Lyapunov and convex optimization techniques, the proposed approach can achieve asymptotically near-optimal online decisions without any prior statistical knowledge, and maintain the asymptotic optimality in the presence of partial and outdated network state information. Numerical simulations corroborate the theoretical analysis and demonstrate the effectiveness of the proposed approach.
Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging

While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. By focusing on sequence tagging NLP tasks, we propose a method, referred to as Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), that actively adapts to the deep learning model being trained to further eliminate such redundant examples chosen by an AL strategy. We show that A$\mathbf{^2}$L is widely applicable by using it in conjunction with several different AL strategies and sequence tagging tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by $\approx\mathbf{2-16\%}$ on multiple sequence tagging tasks while achieving the same performance.
Pseudo‐real‐time retinal layer segmentation for high‐resolution adaptive optics optical coherence tomography

We present a pseudo‐real‐time retinal layer segmentation for high‐resolution Sensorless Adaptive Optics‐Optical Coherence Tomography (SAO‐OCT). Our pseudo‐real‐time segmentation method is based on Dijkstra's algorithm that uses the intensity of pixels and the vertical gradient of the image to find the minimum cost in a geometric graph formulation within a limited search region. It segments six retinal layer boundaries in an iterative process according to their order of prominence. The segmentation time is strongly correlated to the number of retinal layers to be segmented. Our program permits en face images to be extracted during data acquisition to guide the depth specific focus control and depth dependent aberration correction for high‐resolution SAO‐OCT systems. The average processing times for our entire pipeline for segmenting six layers in a retinal B‐scan of 496 × 400 and 240 × 400 pixels are around 25.60 and 13.76 ms, respectively. When reducing the number of layers segmented to only two layers, the time required for a 240 × 400 pixel image is 8.26 ms.
A Universal Formulation of Uncertainty Relation for Error and Disturbance

We present a universal formulation of uncertainty relation valid for any conceivable quantum measurement and their observer effects of statistical nature. Owing to its simplicity and operational tangibility, our general relation is also experimentally verifiable. Our relation violates the na{i}ve bound $\hbar/2$ for the position-momentum measurement while respecting Heisenberg's original philosophy of the uncertainty principle. Our error-disturbance relation is found to be a corollary to our relation for errors, the latter of which also entails the standard Kennard-Robertson (Schr{o}dinger) relation as a special case; this attains a unified picture of the three orthodox realms of uncertainty regarding quantum indeterminacy, measurement, and observer effect within a single framework.
On Sylvester solution for degenerate eigenvalues.

In this paper we introduce the use of Sylvester's formula for systems with degenerate eigenvalues in relation to obtaining their analytical solutions. To appreciate the use we include two other forms of analytical solutions namely adiabatic and Magnus approximations. In quantum mechanics, the Schrodinger equation is a mathematical equation that describes the evolution over time of a physical system in which quantum effects, such as wave--particle duality, are significant. The equation is a mathematical formulation for studying quantum mechanical systems. Just like Newtons's laws govern the motion of objects, Schrodinger equations of motion also govern the motion of quantum objects. Unlike the classical motion of objects the equation of motions of quantum phenomenon deals with the likelihood of the trajectories.
Submodular Clustering in Low Dimensions

We study a clustering problem where the goal is to maximize the coverage of the input points by $k$ chosen centers. Specifically, given a set of $n$ points $P \subseteq \mathbb{R}^d$, the goal is to pick $k$ centers $C \subseteq \mathbb{R}^d$ that maximize the service $ \sum_{p \in P}\mathsf{\varphi}\bigl( \mathsf{d}(p,C) \bigr) $ to the points $P$, where $\mathsf{d}(p,C)$ is the distance of $p$ to its nearest center in $C$, and $\mathsf{\varphi}$ is a non-increasing service function $\mathsf{\varphi} : \mathbb{R}^+ \to \mathbb{R}^+$. This includes problems of placing $k$ base stations as to maximize the total bandwidth to the clients -- indeed, the closer the client is to its nearest base station, the more data it can send/receive, and the target is to place $k$ base stations so that the total bandwidth is maximized. We provide an $n^{\varepsilon^{-O(d)}}$ time algorithm for this problem that achieves a $(1-\varepsilon)$-approximation. Notably, the runtime does not depend on the parameter $k$ and it works for an arbitrary non-increasing service function $\mathsf{\varphi} : \mathbb{R}^+ \to \mathbb{R}^+$.
NDE 4.0: Digital Twin, Semantics, Interfaces, Networking, Feedback, New Markets and Integration into the Industrial Internet of Things

The industrial revolution is divided into three phases by historians: The invention of the steam engine (mechanization), electricity (mass production) and the microelectric revolution (automation). There was a similar development in non-destructive evaluation: tools such as lenses or stethoscopes allowed the human senses to be sharpened, the conversion of waves makes the invisible visible and thus offers a"look"into the components and finally automation, digitization and reconstruction. During the entire industrial development NDE was decisively responsible for the quality and thus for the success of the manufactured goods. Industry is now talking about a fourth revolution: The informatization, digitization and networking of industrial production. As always, NDE will be critical to the success of this fourth revolution by providing the database needed for feedback in a networked production environment. For NDE, this will lead to change. The test results must be made available to a networked production environment in such a way that they can be evaluated for feedback loops, the testability must be considered in the design and the reliability of the test statements will become increasingly important. This publication presents first an orientation to NDE 4.0, including the development of Industry and NDE, a definition of its revolutions, a collection of several current-day challenges of NDE, and a discussion whether and how those can be solved with NDE 4.0. Second this publication presents concepts on how NDE can be integrated into Industry 4.0 landscapes: The Reference Architecture Model Industry 4.0 (RAMI 4.0) shows the complete Industry 4.0 space and allows every Industry 4.0 standard and interface to be located. The Industry 4.0 Asset Administration Shell (AAS) implements the digital twin and is the interface between Industry 4.0 communication and the physical device. The .
Covariance Estimation for Matrix-valued Data

Abstract Covariance estimation for matrix-valued data has received an increasing interest in applications. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, we propose a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure. Under these conditions, the original covariance matrix is decomposed into a Kronecker product of two bandable small covariance matrices representing the variability over row and column directions. We formulate a unified framework for estimating bandable covariance, and introduce an efficient algorithm based on rank one unconstrained Kronecker product approximation. The convergence rates of the proposed estimators are established, and the derived minimax lower bound shows our proposed estimator is rate-optimal under certain divergence regimes of matrix size. We further introduce a class of robust covariance estimators and provide theoretical guarantees to deal with heavy-tailed data. We demonstrate the superior finite-sample performance of our methods using simulations and real applications from a gridded temperature anomalies dataset and an S&P 500 stock data analysis. Supplementary materials for this article are available online.
Reduction of divisors for classical superintegrable GL(3) magnetic chain

Separated variables for a classical GL(3) magnetic chain are coordinates of a generic positive divisor D of degree n on a genus g non-hyperelliptic algebraic curve. Because n > g, this divisor D has unique representative ρ(D) in the Jacobian, which can be constructed by using dim|D| = n − g steps of Abel’s algorithm. We study the properties of the corresponding chain of divisors and prove that the classical GL(3) magnetic chain is a superintegrable system with dim|D| = 2 superintegrable Hamiltonians.
The information capacity of entanglement-assisted continuous variable quantum measurement

The present paper is devoted to the investigation of the entropy reduction and entanglement-assisted classical capacity (information gain) of continuous variable quantum measurements. These quantities are computed explicitly for multimode Gaussian measurement channels. For this we establish a fundamental property of the entropy reduction of a measurement: under a restriction on the second moments of the input state it is maximized by a Gaussian state (providing an analytical expression for the maximum). In the case of one mode, the gain of entanglement assistance is investigated in detail.
Spectral Variability of VHS J1256–1257b from 1 to 5 μm

Multiwavelength time-resolved observations of rotationally modulated variability from brown dwarfs and giant exoplanets are the most effective method for constraining their heterogeneous atmospheric structures. In a companion paper, we reported the discovery of strong near-infrared variability in HST/WFC3/G141 light curves of the very red L-dwarf companion VHS J1256–1257b. In this paper, we present a follow-up 36 hr Spitzer/IRAC Channel 2 light curve together with an in-depth analysis of the Hubble space telescope (HST) and the Spitzer data. The combined data set provides time-resolved light curves of VHS1256b sampling 1.1 to 4.5 μm. The Spitzer light curve is best fit with a single sine wave with a period of 22.04 ± 0.05 hr and a peak-to-peak amplitude of 5.76 ± 0.04%. Combining the period with a previously measured projected rotational velocity ( ), we find that VHS1256b is most consistent with equatorial viewing geometry. The HST/G141+Spitzer spectral energy distribution favors a model with a of 1000 K and low surface gravity with disequilibrium chemistry. The spectral variability of VHS1256b is consistent with predictions from partly cloudy models, suggesting that heterogeneous clouds are the dominant source of the observed modulations. We find evidence at the 3. 3σ level for amplitude variations within the 1.67 μm CH4 band, which is the first such detection for a variable L-dwarf. We compare the HST/G141 time-resolved spectra of three red L-dwarfs with high-amplitude near-infrared rotational modulations and find that although their time-averaged spectra are similar, their spectroscopic variabilities exhibit notable differences. This diversity reinforces the advantage of time-resolved spectroscopic observations for understanding the atmospheres of brown dwarfs and directly imaged exoplanets.
Loneliness and sleep: A systematic review and meta-analysis

Despite the mounting evidence linking loneliness with health, the mechanisms underlying this relationship remain obscure. This systematic review and meta-analysis on the association between loneliness and one potential mechanism—sleep—identified 27 relevant articles. Loneliness correlated with self-reported sleep disturbance (r = .28, 95% confidence interval (.24, .33)) but not duration, across a diverse set of samples and measures. There was no evidence supporting age or gender as moderators or suggesting publication bias. The longitudinal relationship between loneliness and sleep remains unclear. Loneliness is related to sleep disturbance, but research is necessary to determine directionality, examine the influence of other factors, and speak to causality.
Neoadjuvant chemoradiotherapy or radiotherapy in patients aged 75 years or older with locally advanced rectal cancer

Background: To evaluate the efficacy and treatment related morbidity of neoadjuvant chemoradiotherapy or radiotherapy in elderly patients (aged 75 years or older) with locally advanced rectal cancer (LARC). Methods: We reviewed clinical records of elderly patients with LARC treated with neoadjuvant chemoradiotherapy or radiotherapy between January 2008 and June 2017 at our institute. A dose of 45-50Gy in 25 fractions was delivered to pelvis. The primary tumor received a dose of 55Gy concomitantly for patients receiving intensity modulated radiotherapy (IMRT). The concurrent chemotherapy included capecitabine alone and capecitabine plus oxaliplatin (Xelox). Surgery was performed for suitable patients at least 6 weeks after neoadjuvant treatment. Overall survival (OS), disease specific survival (DSS), disease free survival (DFS) and local control (LC) were calculated with Kaplan-Meier method. Results: A total of 85 patients were enrolled in this study, the median age was 80 years old (range: 75-90 years). After neoadjuvant treatment, surgery was performed in 56 patients (65.9%). Downstaging rate was 85.7% (48/56) with T downstaging in 35 patients (62.5%) and N downstaging in 36 patients (64.3%). Twelve patients (21.4%) obtained pathological complete response (pCR). The incidence of grade 3 or greater acute hematological, gastrointestinal and genitourinary toxicities were 10.7%, 5.2% and 1.8%, respectively. Seven patients (12.5%) experienced postoperative complications. The median follow-up duration was 35.7 months (range: 4.3-100.3 months), The 3-year and 5-year OS, DSS, DFS, LC were 68.9% and 47.2%, 75.8% and 60.4%, 68.2% and 56.1%, 83.9% and 78.3%, respectively. Conclusion: In patients aged 75 years or older with LARC, neoadjuvant chemoradiotherapy followed by surgery was well tolerated with promising survival outcomes, which should be strongly suggested if medically suitable.
Exploring feature selection and classification methods for predicting heart disease

Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen by various feature-selection methods. Four commonly used heart disease datasets have been evaluated using principal component analysis, Chi squared testing, ReliefF and symmetrical uncertainty to create distinctive feature sets. Then, a variety of classification algorithms have been used to create models that are then compared to seek the optimal features combinations, to improve the correct prediction of heart conditions. We found the benefits of using feature selection vary depending on the machine learning technique used for the heart datasets we consider. However, the best model we created used a combination of Chi-squared feature selection with the BayesNet algorithm and achieved an accuracy of 85.00% on the considered datasets.