Miracle-dz/UniFAR-FTUs huggingface dataset:
DQC-ADMM: Decentralized Dynamic ADMM With Quantized and Censored Communications

In distributed learning and optimization, a network of multiple computing units coordinates to solve a large-scale problem. This article focuses on dynamic optimization over a decentralized network. We develop a communication-efficient algorithm based on the alternating direction method of multipliers (ADMM) with quantized and censored communications, termed DQC-ADMM. At each time of the algorithm, the nodes collaborate to minimize the summation of their time-varying, local objective functions. Through local iterative computation and communication, DQC-ADMM is able to track the time-varying optimal solution. Different from traditional approaches requiring transmissions of the exact local iterates among the neighbors at every time, we propose to quantize the transmitted information, as well as adopt a communication-censoring strategy for the sake of reducing the communication cost in the optimization process. To be specific, a node transmits the quantized version of the local information to its neighbors, if and only if the value sufficiently deviates from the one previously transmitted. We theoretically justify that the proposed DQC-ADMM is capable of tracking the time-varying optimal solution, subject to a bounded error caused by the quantized and censored communications, as well as the system dynamics. Through numerical experiments, we evaluate the tracking performance and communication savings of the proposed DQC-ADMM.
Steric and aromatic impact on intramolecular hydrogen bonds in o-hydroxyaryl ketones and ketimines

X-ray measurements at 100 K and quantum-mechanical calculations showed a domination of the enol (OH) form in o-hydroxyaryl ketones [6-methoxy-2-hydroxyacetophenone (6OMeK), 4-methoxy-2-hydroxyacetophenone (4OMeK), 5-chloro-4-methyl-2-hydroxybenzophenone (5Cl4MeK) and 2-hydroxyacetonaphthone (o-HAN)], whereas a prevailing proton-transfer (NH) form was found in o-hydroxyacetonaphthylimine [2-(N-methyl-α-iminoethyl)naphthol (o-HIN)]. The effective mechanism of the reduction in hydrogen bridge length due to steric repulsion in the o-hydroxyaryl ketones is presented. The following phenomena were found: a decrease in phenol ring aromaticity caused by the proton-transfer process, dependence of the HOMA index of aromaticity on the HOSE index defining the destabilization of the chelate ring and a dependence of the HOMA index on the lengths of the carbonyl bonds. Moreover, a correlation between phenol ring aromaticity [HOMA(phenol)] and the pseudo-aromatic ring [HOSE(chelate)] is shown. Correlations between structural parameters of the chelate ring [d(C—O), d(Caryl—Calkyl)] and aromatic rings [HOMA(phenol)] of the o-hydroxyaryl ketimines and ketones are presented. Copyright © 2005 John Wiley & Sons, Ltd.
A NEW APPROACH TO VIBRATIONAL ANALYSIS OF LARGE MOLECULES BY DENSITY FUNCTIONAL THEORY: WAVENUMBER-LINEAR SCALING METHOD

Normal vibration calculations were performed on 164 basic organic and inorganic compounds by the density functional method of B3LYP/6-311+G** using the wavenumber-linear scaling (WLS) method, in which essentially only one parameter is involved for the whole wavenumber region. The average deviation of the WLS-scaled calculated wavenumbers from the observed wavenumbers for a total of 1223 normal modes was 3.4%. The present results and the previous results on 205 compounds have shown the high performance of the WLS method in vibrational analysis of a variety of compounds. The effect of anharmonicity on the observed wavenumbers was examined on the basis of the calculated wavenumbers for 224 diatomic molecules and ions in comparison with their experimental harmonic wavenumbers. The results indicate that the larger positive deviations of the unscaled calculated wavenumbers from the observed values at higher wavenumbers are attributed almost exclusively to the neglect of anharmonicity in the calculated wavenumbe.
Analysing various methods for object extraction and the classification of kitchens

In the last few years there have been great successes in the application of deep and machine learning for the use of both object detection and classification. However, when there is a limited amount of data available for many different classes, accuracy is low and decent results can often not be obtained. This research aims to show various case-specific methods to analyse the data and to extract important features to improve classification, such as the Hough transform and mean shift segmentation. A convolution neural network, Alexnet has been trained using both the raw data and the extracted features. When training and validating the network using the raw data an accuracy of 28% has been obtained. When applying extracted features, the handles of the kitchen, to the same network accuracy improved from a 28% to an accuracy of 41%. This increase of thirteen percentage points shows that significant improvement is possible when extracting features before training a network.
Federated Multiagent Actor–Critic Learning for Age Sensitive Mobile-Edge Computing

As an emerging technique, mobile-edge computing (MEC) introduces a new scheme for various distributed communication-computing systems, such as industrial Internet of Things (IoT), vehicular communication, smart city, etc. In this work, we mainly focus on the timeliness of the MEC systems where the freshness of the data and computation tasks is significant. First, we formulate a kind of age-sensitive MEC models and define the average Age-of-Information (AoI) minimization problems of interests. Then, a novel mixed-policy-based multimodal deep reinforcement learning (RL) framework, called heterogeneous multiagent actor–critic (H-MAAC), is proposed as a paradigm for joint collaboration in the investigated MEC systems, where edge devices and center controller learn the interactive strategies through their own observations. To improve the system performance, we develop the corresponding online algorithm by introducing the edge federated learning mode into the multiagent cooperation whose advantages on learning convergence can be guaranteed theoretically. To the best of our knowledge, it is the first joint MEC collaboration algorithm that combines the edge federated mode with the multiagent actor–critic RL. Furthermore, we evaluate the proposed approach and compare it with popular RL-based methods. As a result, the proposed algorithm not only outperforms the baselines on average system age, but also promotes the stability of training process. Besides, the simulation outcomes provide several insights for collaboration designs over MEC systems.
Capacity-achieving Polar-based LDGM Codes

In this paper, we study codes with sparse generator matrices. More specifically, low-density generator matrix (LDGM) codes with a certain constraint on the weight of all the columns in the generator matrix are considered. Previous works have shown the existence of capacity-achieving LDGM codes with column weight bounded above by any linear function of the block length N over the BSC, and by the logarithm of N over the BEC. In this paper, it is first shown that when a binary-input memoryless symmetric (BMS) channel W and a constant s > 0 are given, there exists a polarization kernel such that the corresponding polar code is capacity-achieving and the column weights of the generator matrix (GM) are bounded from above by N. Then, a general construction based on a concatenation of polar codes and a rate-1 code, and a new columnsplitting algorithm that guarantees a much sparser GM, is given. More specifically, for any BMS channel and any > 2 ∗, where ∗ ≈ 0.085, an existence of a sequence of capacity-achieving codes with all the GM column weights upper bounded by (logN) is shown. Furthermore, two coding schemes for BEC and BMS channels, based on a second column-splitting algorithm, are devised with low-complexity decoding that uses successivecancellation. The second splitting algorithm allows for the use of a low-complexity decoder by preserving the reliability of the bit-channels observed by the source bits, and by increasing the code block length. In particular, for any BEC and any λ > λ∗ = 0.5 + ∗, the existence of a sequence of capacity-achieving codes where all the GM column weights are bounded from above by (logN) and with decoding complexity O(N log logN) is shown. The existence of similar capacity-achieving LDGM codes with low-complexity decoding is shown for any BMS channel, and for any λ > λ† ≈ 0.631. The concatenation-based construction can also be applied to the random linear code ensemble to yield capacity-achieving codes with all the GM column weights being O(logN) and with (large-degree) polynomial decoding complexity.
Graph signal denoising using $t$-shrinkage priors

We study the graph signal denoising problem by estimating a piecewise constant signal over an undirected graph. We propose a new Bayesian approach that first converts a general graph to a chain graph via the depth-first search algorithm, and then imposes a heavy-tailed $t$-shrinkage prior on the differences between consecutive signals over the induced chain graph. We show that the posterior computation can be conveniently conducted by fully exploring the conjugacy structure in the model. We also derive the posterior contraction rate for the proposed estimator, and show that this rate is optimal up to a logarithmic factor, besides automatically adapting to the unknown edge sparsity level of the graph. We demonstrate the excellent empirical performance of the proposed method via extensive simulation studies and applications to stock market data.
Molecular Dynamics Simulation of Self-Aggregation of Asphaltenes at an Oil/Water Interface: Formation and Destruction of the Asphaltene Protective Film

It is well known that asphaltene molecules play a significant role in stabilizing emulsions of water in crude oil or diluted bitumen solutions: Molecular dynamics simulations were employed to investigate the aggregation anti orientation behaviors of asphaltene molecules in a vacuum and at various water surfaces. Two different continental model asphaltene molecules were employed in this work. It was found that the initially disordered asphaltenes quickly self-assembled into ordered hanoaggregates consisting of several molecules, in which the aromatic rings in asphaltenes were reoriented to form a face-to-face stacked structure. More importantly, statistical analysis indicates that most of the stacked polycyclic aromatic planes of asphaltene nanoaggregates tend to be perpendicular to the water surface. Tithe asphaltene molecules are considered as "stakes", then the asphaltene nanoaggregate can be regarded as a "fence". All the fence,like nanoaggregates were twined and knitted together, which pinned them perpendicularly on the water surface to form a steady protective ail wrapping the water droplets. The mechanism of stabilization Of the water/oil emulsions is thereby well understood. Demulsification processes using a chemical demulsifier were also studied. It was observed that the asphaltene protective film was destroyed by a demulsifier of ethyl cellulose molecules, leading to exposure of the water droplet. The results obtained in this work will be of significance in guiding the development of demulsification technology.
On Generating Extended Summaries of Long Documents

Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in some cases to know more detailed information about its salient points that can't fit in a short summary. This is typically the case for longer documents such as a research paper, legal document, or a book. In this paper, we present a new method for generating extended summaries of long papers. Our method exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach. We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method outperforms or matches the performance of strong baselines. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for long-form summary generation task. Our analysis shows that our multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections. Our datasets, and codes are publicly available at this https URL
Adaptive Threshold for Better Performance of the Recognition and Re-identification Models

Choosing a decision threshold is one of the challenging job in any classiﬁcation tasks. How much the model is accurate, if the deciding boundary is not picked up care-fully, its entire performance would go in vain. On the other hand, for imbalance classiﬁcation where one of the classes is dominant over another, relying on the conventional method of choosing threshold would result in poor performance. Even if the threshold or decision boundary is properly chosen based on machine learning strategies like SVM and decision tree, it will fail at some point for dynamically varying databases and in case of identity-features that are more or less similar, like in face recognition and person re-identiﬁcation models. Hence, with the need for adaptability of the decision threshold selection for imbalanced classiﬁcation and incremental database size, an online optimization-based statistical feature learning adaptive technique is developed and tested on the LFW dataset and self-prepared athletes dataset. This method of adopting adaptive threshold resulted in 12 − 45% improvement in the model accuracy compared to the ﬁxed threshold { 0 . 3 , 0 . 5 , 0 . 7 } that are usually taken via the hit-and-trial method in any classiﬁcation and identiﬁcation tasks. Source code for the complete
Physical insights from the spectrum of the radio halo in MACS J0717.5+3745

We present new LOw-Frequency ARray observations of the massive merging galaxy cluster MACS J0717.5+3745, located at a redshift of 0.5458. The cluster hosts the most powerful radio halo known to date. These new observations, in combination with published uGMRT (300−850 MHz) and VLA (1−6.5 GHz) data, reveal that the halo is more extended than previously thought, with a largest linear size of ∼2.2 Mpc, making it one of the largest known halos. The halo shows a steep spectrum (α144 MHz1.5 GHz ∼ −1.4) and a steepening (α1.5 GHz5.5 GHz ∼ −1.9) above 1.5 GHz. We find a strong scattering in spectral index maps on scales of 50−100 kpc. We suggest that such a strong scattering may be a consequence of the regime where inverse Compton dominates the energy losses of electrons. The spectral index becomes steeper and shows an increased curvature in the outermost regions of the halo. We combined the radio data with Chandra observations to investigate the connection between the thermal and nonthermal components of the intracluster medium (ICM). Despite a significant substructure in the halo emission, the radio brightness correlates strongly with the X-ray brightness at all observed frequencies. The radio-versus-X-ray brightness correlation slope steepens at a higher radio frequency (from b144 MHz = 0.67 ± 0.05 to b3.0 GHz = 0.98 ± 0.09) and the spectral index shows a significant anticorrelation with the X-ray brightness. Both pieces of evidence further support a spectral steepening in the external regions. The compelling evidence for a steep spectral index, the existence of a spectral break above 1.5 GHz, and the dependence of radio and X-ray surface brightness correlation on frequency are interpreted in the context of turbulent reacceleration models. Under this scenario, our results allowed us to constrain that the turbulent kinetic pressure of the ICM is up to 10%.
Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation

It is well known that semantic segmentation neural networks (SSNNs) produce dense segmentation maps to resolve the objects' boundaries while restrict the prediction on down-sampled grids to alleviate the computational cost. A striking balance between the accuracy and the training cost of the SSNNs such as U-Net exists. We propose a spectral analysis to investigate the correlations among the resolution of the down sampled grid, the loss function and the accuracy of the SSNNs. By analyzing the network back-propagation process in frequency domain, we discover that the traditional loss function, cross-entropy, and the key features of CNN are mainly affected by the low-frequency components of segmentation labels. Our discoveries can be applied to SSNNs in several ways including (i) determining an efficient low resolution grid for resolving the segmentation maps (ii) pruning the networks by truncating the high frequency decoder features for saving computation costs, and (iii) using block-wise weak annotation for saving the labeling time. Experimental results shown in this paper agree with our spectral analysis for the networks such as DeepLab V3+ and Deep Aggregation Net (DAN).
Detecting Road Obstacles by Erasing Them

Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from those patches. We then use a network trained to recognize discrepancies between the original patch and the inpainted one, which signals an erased obstacle.
An analytical anisotropic compact stellar model of embedding class I

A class of solutions of Einstein field equations satisfying Karmarkar embedding condition is presented which could describe static, spherical fluid configurations, and could serve as models for compact stars. The fluid under consideration has unequal principal stresses i.e. fluid is locally anisotropic. A certain physically motivated geometry of metric potential has been chosen and codependency of the metric potentials outlines the formation of the model. The exterior spacetime is assumed as described by the exterior Schwarzschild solution. The smooth matching of the interior to the exterior Schwarzschild spacetime metric across the boundary and the condition that radial pressure is zero across the boundary lead us to determine the model parameters. Physical requirements and stability analysis of the model demanded for a physically realistic star are satisfied. The developed model has been investigated graphically by exploring data from some of the known compact objects. The mass-radius (M-R) relationship that shows the maximum mass admissible for observed pulsars for a given surface density has also been investigated. Moreover, the physical profile of the moment of inertia (I) thus obtained from the solutions is confirmed by the Bejger-Haensel concept.
Dynamical localization of interacting bosons in the few-body limit

The quantum kicked rotor is well-known to display dynamical localization in the non-interacting limit. In the interacting case, while the mean-field (Gross-Pitaevskii) approximation displays a destruction of dynamical localization, its fate remains debated beyond mean-field. Here we study the kicked Lieb-Liniger model in the few-body limit. We show that for any interaction strength, two kicked interacting bosons always dynamically localize, in the sense that the energy of the system saturates at long time. However, contrary to the non-interacting limit, the momentum distribution $\Pi(k)$ of the bosons is not exponentially localized, but decays as $\mathcal C/k^4$, as expected for interacting quantum particles, with Tan's contact $\mathcal C$ which remains finite at long time. We discuss how our results will impact the experimental study of kicked interacting bosons.
A Tutorial on Sparse Gaussian Processes and Variational Inference

Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regression problems with Gaussian likelihoods, a GP model enjoys a posterior in closed form. However, identifying the posterior GP scales cubically with the number of training examples and requires to store all examples in memory. In order to overcome these obstacles, sparse GPs have been proposed that approximate the true posterior GP with pseudo-training examples. Importantly, the number of pseudo-training examples is user-defined and enables control over computational and memory complexity. In the general case, sparse GPs do not enjoy closed-form solutions and one has to resort to approximate inference. In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood. This paves the way for a powerful and versatile framework, where pseudo-training examples are treated as optimization arguments of the approximate posterior that are jointly identified together with hyperparameters of the generative model (i.e. prior and likelihood). The framework can naturally handle a wide scope of supervised learning problems, ranging from regression with heteroscedastic and non-Gaussian likelihoods to classification problems with discrete labels, but also multilabel problems. The purpose of this tutorial is to provide access to the basic matter for readers without prior knowledge in both GPs and VI. A proper exposition to the subject enables also access to more recent advances (like importance-weighted VI as well as interdomain, multioutput and deep GPs) that can serve as an inspiration for new research ideas.
Power Iteration for Tensor PCA

In this paper, we study the power iteration algorithm for the spiked tensor model, as introduced in [44]. We give necessary and sufficient conditions for the convergence of the power iteration algorithm. When the power iteration algorithm converges, for the rank one spiked tensor model, we show the estimators for the spike strength and linear functionals of the signal are asymptotically Gaussian; for the multi-rank spiked tensor model, we show the estimators are asymptotically mixtures of Gaussian. This new phenomenon is different from the spiked matrix model. Using these asymptotic results of our estimators, we construct valid and efficient confidence intervals for spike strengths and linear functionals of the signals.
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment

Although pre-trained contextualized language models such as BERT achieve significant performance on various downstream tasks, current language representation still only focuses on linguistic objective at a specific granularity, which may not applicable when multiple levels of linguistic units are involved at the same time. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space. We present a universal representation model, BURT (BERT-inspired Universal Representation from learning meaningful segmenT), to encode different levels of linguistic unit into the same vector space. Specifically, we extract and mask meaningful segments based on point-wise mutual information (PMI) to incorporate different granular objectives into the pre-training stage. We conduct experiments on datasets for English and Chinese including the GLUE and CLUE benchmarks, where our model surpasses its baselines and alternatives on a wide range of downstream tasks. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space through a task-independent evaluation. Finally, we verify the effectiveness of our unified pre-training strategy in two real-world text matching scenarios. As a result, our model significantly outperforms existing information retrieval (IR) methods and yields universal representations that can be directly applied to retrieval-based question-answering and natural language generation tasks.