Miracle-dz/UniFAR-FTUs huggingface dataset:
Timing Synchronization Scheme Using Superimposed Perfect Training in Guard Band

Orthognal Frequency Divison Multiplexing(OFDM) has been adopted by many high speed transmission standards.Pilot tone assisted modulation(PTAM) is well known technique to achieve timing synchronization information in OFDM but is not bandwidth efficient.To achieve high bandwidth efficiency,we proposed to carry out sperimposed a transparent genralized chirp-like(GCL) training with lower power in the guard band onto the time domain to improve the timing synchronization quality.The power ratio of the GCL sequence is determined by minimizing the system bit error rate(BER) using simulation experiments.Simulation results demonstrate that under medium to low SNR conditions,combing superimposed training with synchronization yields BER performance that is comparable with PTAM;the benefit of the former is that bandwidth efficiency is preserved.
An Empirical Comparison of Data Mining Classification Methods

In recent years, huge amount of data have become available due to the rapid development of technologies. Data mining is a field developed as a means of extracting information and knowledge from databases to discover patterns. Classification is a data mining task used in grouping similar data objects together. It can be defined as supervised learning as it assigns class labels to data objects based on the relationship between the data items with a pre-defined class label. In this paper we perform a comparative study of the performance of C4.5, Naive Bayes, SVM and KNN Classification Algorithms. The algorithms are evaluated based on Accuracy, Sensitivity and Specificity and Error rate.
Discrimination algorithms for the remediation of unexploded ordnance

This thesis considers analysis of magnetic and electromagnetic data for the purpose of discriminating between buried unexploded ordnance (UXO) and non-hazardous metallic clutter. Magnetic data acquired over a ferrous object are modelled as a magnetostatic dipole. For time-domain electromagnetic data, a linear combination of decaying, orthogonal dipoles represents the secondary magnetic field radiated by a conductor. Model parameters estimated with inversion can be input into a discrimination algorithm whose output is a ranked diglist of targets. Algorithms that have been applied to UXO discrimination can be broadly categorized as library or statistical methods. Library methods assume that for each ordnance type there is a single set of parameters which is representative of intrinsic target properties. Statistical methods try to formulate a decision rule with a training set of models for targets with known ground truth. Observed data can sometimes have non-normally distributed noise and consequently parameter estimates obtained via least squares inversion may be biased. Robust misfit functions provide improved estimates of model parameters when there are outliers in the data. I also investigate propagation of uncertainties from data to model parameters. For inversion of electromagnetic data I find that parameters derived from the dipole model are approximately normally distributed. However, when data coverage is poor or SNR is low, the posterior distribution of these parameters may be multimodal. I develop a statistical classification algorithm that incorporates parameter uncertainty by integrating over the posterior probability distribution. Simulations and applications to real data indicate that this technique can detect outliers to the distribution of ordnance sooner than conventional classifiers. I quantify the performance of a discrimination algorithm using metrics derived from the receiver operating characteristic (ROC) curve. I use a bootstrapping algorithm to estimate mean values of performance metrics from limited training data and to identify the discrimination algorithm that is best suited to a particular problem. Finally, I consider the problem of determining the stop digging (or operating) point on the ROC. I derive an approximate probability distribution for the point on the ROC at which all ordnance are found and develop methods for estimating this point.
Comment ...

Laurel Smith-Doerr and Timothy Sacco’s review of my book, Trouble in the University, contains a number of factual errors that I wish to correct. The reviewers dismiss my work because they say it is not based on original research, by which they mean interviews. The Acknowledgements (p. vii) state my thanks to those I interviewed. Information on the day-to-day life of the university came from those interviews (e.g., pp. 50, 52, 53, 57, 58, 59, 60). The reviewers may have misinterpreted my discussion of why I had difficulty finding cooperative subjects (p. 133) to mean that I had done no interviews. Even if that had been the case, it does not mean that documentary sources cannot be the source of original research. The reviewers criticize me for not engaging with the literature on higher education. On the contrary, my reliance on that literature is evident from the extensive references. If there is something crucial I missed, the reviewers should identify it. The reviewers conclude that the university I studied was ‘‘bad’’ and deserved to be shut down. I made no such judgment, and it is unwarranted in light of all the competent professionals the university trained, the research done in its labs, and the health services provided. Nor was the university shut down. It lost its autonomous identity when its parts were redistributed to two other public universities. Charges of corruption provided a political opening for the state’s politicians to finally accomplish what they had tried to do for many years. As my conclusion makes clear, there was no evidence that the restructuring addressed the attributes that put the university at risk for corruption.
Lung CT image segmentation combined multi-scale watershed method and region growing method

To solve the problem of lung segmentation and vessels removal,a watershed algorithm is presented.Chest CT image is segmented by choosing different merging parameters,original image is divided into different scales of image,high-scale image and low-scale image.Seed point located inside the branch vessels is manually selected and to the growth threshold is determined with the aid of low-scale image region growing,and the branching vessels region is splited subsequently.The region in the highscale image is removed to get the final segmentation results.A large number of CT lung data segmentation have been experimented and good segmentation results are achieved.
Experimental study on locality-aware application layer multicast

This paper proposed Canicula,a locality-aware overlay multicast system.Its overlay network was constructed by node clustering based on network coordinate.According to the results of the simulations on real-world network dataset and experiments on PlanetLab,it shows that the performance of locality-aware overlay multicast is better than the other prevailing overlay multicast.Considering the nodes' location in Internet during the construction of the overlay can improve the perfor-mance of the application layer multicast.
xR-EgoPose: Egocentric 3D Human Pose From an HMD Camera

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just $2$ cm. ~away from the user's face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset -- xR-EgoPose -- offering 383K frames of high quality renderings of people with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.
Experimental proof of quantum Zeno-assisted noise sensing

In the ideal quantum Zeno (QZ) effect, repeated quantum projective measurements can freeze the coherent dynamics of a quantum system. However, in the weak QZ regime, measurement back-actions can allow the sensing of semi-classical field fluctuations. In this regard, we theoretically show how to combine the controlled manipulation of a quantum two-level system, used as a probe, with a sequence of projective measurements to have direct access to the noise correlation function. We experimentally test the effectiveness of the proposed noise sensing method on a properly engineered Bose–Einstein condensate of 87 Rb atoms realized on an atom chip. We believe that our QZ-based approach can open a new path towards novel quantum sensing devices.
Simulating quantum circuits by classical circuits

In a recent breakthrough, Bravyi, Gosset and Konig (BGK) [Science, 2018] proved that "simulating" constant depth quantum circuits takes classical circuits $\Omega(\log n)$ depth. In our paper, we first formalise their notion of simulation, which we call "possibilistic simulation". Then, from well-known results, we deduce that their circuits can be simulated in depth $O(\log^{2} n)$. Separately, we construct explicit classical circuits that can simulate any depth-$d$ quantum circuit with Clifford and $t$ $T$-gates in depth $O(d+t)$. Our classical circuits use $\{\text{NOT, AND, OR}\}$ gates of fan-in $\leq 2$.
Universality for lozenge tiling local statistics

In this paper we consider uniformly random lozenge tilings of arbitrary domains approximating (after suitable normalization) a closed, simply-connected subset of $\mathbb{R}^2$ with piecewise smooth, simple boundary. We show that the local statistics of this model around any point in the liquid region of its limit shape are given by the infinite-volume, translation-invariant, extremal Gibbs measure of the appropriate slope, thereby confirming a prediction of Cohn-Kenyon-Propp from 2001 in the case of lozenge tilings. Our proofs proceed by locally coupling a uniformly random lozenge tiling with a model of Bernoulli random walks conditioned to never intersect, whose convergence of local statistics has been recently understood by the work of Gorin-Petrov. Central to implementing this procedure is to establish a local law for the random tiling, which states that the associated height function is approximately linear on any mesoscopic scale.
Yield uncertainty and strategic formation of supply chain networks

To understand how supply uncertainty affects the structure of supply chain networks we consider a setting where retailers and suppliers must establish a costly relationship with each other prior too engaging in trade. Suppliers, with uncertain yield, announce wholesale prices, while retailers must decide which suppliers to link to based on their wholesale prices. Subsequently, retailers compete with each other in Cournot fashion to sell the acquired supply to consumers. We find that in equilibrium retailers concentrate their links among too few suppliers, that is, there is insufficient diversification of the supply base. We find that either reduction in supply variance or increase in mean supply, increases a supplier's profit. However, these two ways of improving service have qualitatively different effects on welfare: improvement of the expected yield by a supplier makes everyone better off, whereas change in the variance of the yield impacts system members differently.
Resonant inelastic X-ray scattering of magnetic excitations under pressure.

Resonant inelastic X-ray scattering (RIXS) is an extremely valuable tool for the study of elementary, including magnetic, excitations in matter. The latest developments of this technique have mostly been aimed at improving the energy resolution and performing polarization analysis of the scattered radiation, with a great impact on the interpretation and applicability of RIXS. Instead, this article focuses on the sample environment and presents a setup for high-pressure low-temperature RIXS measurements of low-energy excitations. The feasibility of these experiments is proved by probing the magnetic excitations of the bilayer iridate Sr3Ir2O7 at pressures up to 12 GPa.
Shared Generative Latent Representation Learning for Multi-view Clustering

Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the multi-view data share a common latent embedding despite the diversity among the various views. Specifically, benefitting from the success of the deep generative learning, the proposed model can not only extract the nonlinear features from the views, but render a powerful ability in capturing the correlations among all the views. The extensive experimental results on several datasets with different scales demonstrate that the proposed method outperforms the state-of-the-art methods under a range of performance criteria.
Non-Orthogonal Multiple Access (NOMA): How It Meets 5G and Beyond

Due to massive connectivity and increasing demands of various services and data-hungry applications, a full-scale implementation of the fifth generation (5G) wireless systems requires more effective radio access techniques. In this regard, non-orthogonal multiple access (NOMA) has recently gained ever-growing attention from both academia and industry. Compared to orthogonal multiple access (OMA) techniques, NOMA is superior in terms of spectral efficiency and is thus appropriate for 5G and Beyond. In this article, we provide an overview of NOMA principles and applications. Specifically, the article discusses the fundamentals of power-domain NOMA with single and multiple antennas in both uplink and downlink settings. In addition, the basic principles of code-domain NOMA are elaborated. Further, the article explains various resource allocation techniques such as user pairing and power allocation for NOMA systems; discusses the basic form of cooperative NOMA and its variants; and addresses several opportunities and challenges associated with the compatibility of NOMA with other advanced communication paradigms such as heterogeneous networks and millimeter wave communications.
Interplay of pulse duration, peak intensity, and particle size in laser-driven electron emission from silica nanospheres.

We present the results of a systematic study of photoelectron emission from isolated dielectric nanoparticles (SiO2) irradiated by intense 25 fs, 780 nm linearly polarized laser pulses as a function of particle size (20 nm to 750 nm in diameter) and laser intensity. We also introduce an experimental technique to reduce the effects of focal volume averaging. The highest photoelectron energies show a strong size dependence, increasing by a factor of six over the range of particles sizes studied at a fixed intensity. For smaller particle sizes (up to 200 nm), our findings agree well with earlier results obtained with few-cycle, ∼4 fs pulses. For large nanoparticles, which exhibit stronger near-field localization due to field-propagation effects, we observe the emission of much more energetic electrons, reaching energies up to ∼200 times the ponderomotive energy. This strong deviation in maximum photoelectron energy is attributed to the increase in ionization and charge interaction for many-cycle pulses at similar intensities.
Key techniques of multi-core processor and its development trends

Multi-core processor has replaced single processor as the mainstream in the market for its high performance and low power. Three typical structures of multi-core processor: Hydra, Cell, RAW is introduced, and then seven key techniques is presented, such as core architecture, memory architecture design, communication on chip, low power, operation system design, software application development, and finally the development trends of multi-core processor is discussed, which are manycore, low power and heterogeneous.
Discriminative Learning for Monaural Speech Separation Using Deep Embedding Features

Deep clustering (DC) and utterance-level permutation invariant training (uPIT) have been demonstrated promising for speaker-independent speech separation. DC is usually formulated as two-step processes: embedding learning and embedding clustering, which results in complex separation pipelines and a huge obstacle in directly optimizing the actual separation objectives. As for uPIT, it only minimizes the chosen permutation with the lowest mean square error, doesn't discriminate it with other permutations. In this paper, we propose a discriminative learning method for speaker-independent speech separation using deep embedding features. Firstly, a DC network is trained to extract deep embedding features, which contain each source's information and have an advantage in discriminating each target speakers. Then these features are used as the input for uPIT to directly separate the different sources. Finally, uPIT and DC are jointly trained, which directly optimizes the actual separation objectives. Moreover, in order to maximize the distance of each permutation, the discriminative learning is applied to fine tuning the whole model. Our experiments are conducted on WSJ0-2mix dataset. Experimental results show that the proposed models achieve better performances than DC and uPIT for speaker-independent speech separation.
Graph Reasoning Networks for Visual Question Answering

The interaction between language and visual information has been emphasized in visual question answering (VQA) with the help of attention mechanism. However, the relationship between words in question has been underestimated, which makes it hard to answer questions that involve the relationship between multiple entities, such as comparison and counting. In this paper, we develop the graph reasoning networks to tackle this problem. Two kinds of graphs are investigated, namely inter-graph and intra-graph. The inter-graph transfers features of the detected objects to their related query words, enabling the output nodes to have both semantic and factual information. The intra-graph exchanges information between these output nodes from inter-graph to amplify implicit yet important relationship between objects. These two kinds of graphs cooperate with each other, and thus our resulting model can reason the relationship and dependence between objects, which leads to realization of multi-step reasoning. Experimental results on the GQA v1.1 dataset demonstrate the reasoning ability of our method to handle compositional questions about real-world images. We achieve state-of-the-art performance, boosting accuracy to 57.04%. On the VQA 2.0 dataset, we also receive a promising improvement on overall accuracy, especially on counting problem.
The X-Ray-resolved Supernova Remnant S8 in the Dwarf Irregular Galaxy IC 1613

We conducted an observation of the nearby irregular galaxy IC 1613 with the Chandra X-ray Observatory using the S3 chip of the Advanced CCD Imaging Spectrometer (ACIS) with an effective exposure time of 49.9 ks. The observation primarily targeted the extensive bubble and star formation region in the northeast quadrant of the galaxy. The only known supernova remnant (SNR) in IC 1613, S8, is also the galaxy’s most luminous X-ray source (LX(0.3–8 keV) ∼5–6 × 1036 erg s−1). We resolve the SNR with Chandra and compare its nearly circular X-ray morphology with Hα and radio images. We assign an upper limit on the luminosity of any possible associated compact central object of ≈4 × 1035 erg s−1 (0.3–8 keV band) and conclude that we would detect a Crab-like pulsar but not a Cas A-like object. We infer an age for S8 of ∼3400–5600 yr and compare it to other SNRs in the Local Group. We suggest that S8 is a young composite SNR.
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set of changes a person can undertake in order to improve their outcome. We propose a recourse algorithm that models the underlying data distribution or manifold. We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome. This mechanism can be easily used to provide recourse for any differentiable machine learning based decision making system. Further, the resulting algorithm is shown to be applicable to both supervised classification and causal decision making systems. Our work attempts to fill gaps in existing fairness literature that have primarily focused on discovering and/or algorithmically enforcing fairness constraints on decision making systems. This work also provides an alternative approach to generating counterfactual explanations.
Asymmetry for tensor $t_{2j}$ and vector $t_{1i}$ polarizations with taking into account the deuteron wave function in coordinate space

The analytic forms of deuteron wave function in coordinate space were applied for theoretical calculations of full set asymmetries of tensor $t_{2j}$ and vector $t_{1i}$ polarizations. Nucleon-nucleon realistic phenomenological potentials of Nijmegen group (Nijm1, Nijm2, Nijm93, Reid93) and Argonne group (Argonne v18) as well as other widely used and popular potentials (OBEPC, MT, Paris) are used for numerical calculations. The angular asymmetry is calculated in the range of momentums 0-7 fm$^{ - 1}$. The values of the asymmetry $t_{ij,}$ by polarization type and with each other, are analysed. This is the angular-momentum dependence of values vector $t_{1i} (p,\theta_e )$ and tensor $t_{2i} (p,\theta_e )$ polarizations in 3D format at momentums 0-7 fm$^{-1}$ and scattering angles 1-180 degrees were calculated and compared for Reid93 potential. The perspectives of further application of the obtained results for calculating the values of the cross-sections, asymmetries and other characteristics of processes with the participation of a deuteron are discussed.