Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable characterization of localizable entanglement in noisy topological quantum codes

Topological quantum error correcting codes have emerged as leading candidates towards the goal of achieving large-scale fault-tolerant quantum computers. However, quantifying entanglement in these systems of large size in the presence of noise is a challenging task. In this paper, we provide two different prescriptions to characterize noisy stabilizer states, including the surface and the color codes, in terms of localizable entanglement over a subset of qubits. In one approach, we exploit appropriately constructed entanglement witness operators to estimate a witness-based lower bound of localizable entanglement, which is directly accessible in experiments. In the other recipe, we use graph states that are local unitary equivalent to the stabilizer state to determine a computable measurement-based lower bound of localizable entanglement. If used experimentally, this translates to a lower bound of localizable entanglement obtained from single-qubit measurements in specific bases to be performed on the qubits outside the subsystem of interest. Towards computing these lower bounds, we discuss in detail the methodology of obtaining a local unitary equivalent graph state from a stabilizer state, which includes a new and scalable geometric recipe as well as an algebraic method that applies to general stabilizer states of arbitrary size. Moreover, as a crucial step of the latter recipe, we develop a scalable graph-transformation algorithm that creates a link between two specific nodes in a graph using a sequence of local complementation operations. We develop open-source Python packages for these transformations, and illustrate the methodology by applying it to a noisy topological color code, and study how the witness and measurement-based lower bounds of localizable entanglement varies with the distance between the chosen qubits.
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

<italic>Background:</italic> Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the <italic>data-variability</italic> and <italic>data-inefficiency</italic> issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. <italic>Methods:</italic> We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. <italic>Results:</italic> Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. <italic>Conclusions:</italic> These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. <italic>Significance:</italic> As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. <xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p>The source code and the pretrained models are published at <uri>https://github.com/pquochuy/sleep_transfer_learning</uri>. </p></fn>
Nonsmooth Composite Matrix Optimization: Strong Regularity, Constraint Nondegeneracy and Beyond

The nonsmooth composite matrix optimization problem (CMatOP), in particular, the matrix norm minimization problem, is a generalization of the matrix conic programming problem with wide applications in numerical linear algebra, computational statistics and engineering. This paper is devoted to the characterization of the strong regularity for the CMatOP via the generalized strong second order sufficient condition and constraint nondegeneracy for problems with nonsmooth objective functions. The derived result supplements the existing characterization of the strong regularity for the constrained optimization problems with twice continuously differentiable data.
General theorem of equilibrium status

Determination of stability and instability of singular points in nonlinear dynamical systems is an important issue that has attracted considerable attention in different fields of engineering and science. So far, different well-defined theories have been presented to study the stability of singular points among which the Lyapunov theory is well-known. However, the instability problem of singular points has been neglected to some extent in spite of its application in oscillator design. Besides, it is often difficult to achieve a proper Lyapunov function for a given complex system. This work presents a more general theorem based on defining two distinct functionals and some straightforward criteria that significantly facilitate the determination of equilibrium status at singular points without the requirement to analytical solution. Indeed, this method is applicable to both instability and stability problems of linear and nonlinear systems. In addition, the presented theorem is further generalized for linearization of dynamical systems based on averaging technique. The obtained results clearly show the effectiveness and generality of the proposed theorem for linear and nonlinear dynamic problems.
Attention-Wrapped Hierarchical BLSTMs for DDI Extraction

Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate hand-made or automatic tools to extract embedded information from text and literature in the biomedical domain. Because of restrictions in hand-made efforts and their lower speed, Machine-Learning, or Deep-Learning approaches have become more popular for extracting DDIs. In this study, we propose a novel and generic Deep-Learning model which wraps Hierarchical Bidirectional LSTMs with two Attention Mechanisms that outperforms state-of-the-art models for DDIs Extraction, based on the DDIExtraction-2013 corpora. This model has obtained the macro F1-score of 0.785, and the precision of 0.80.
Assisted Methods for Optimization in Robotics

Abstract—Optimization of the elements and systems from Robotics is one of the most complicated problem with the final goal to obtain the end-effecters' 3D extreme space precision trajectory. In the paper are shown some of the proper methods for optimization in Robotics: optimal Extenics choose of the precisionstability working point of one linear hydraulic cylinder; modelling and optimization of the DC electrical drive of the robots; optimising the dynamic behaviour of the robots by using the intelligent dampers; optimise the inverse kinematics results by using one complex proper method; optimise the multi robots application by using the algorithm to choose the optimal application points of the robots' bases and by construct the parallel robot structure using three arm type robots. All applied method solves one small part of the complex problems of the optimisation in robotics.
Ultralong-range Rydberg molecules

We review ultralong-range Rydberg molecules (ULRMs), which are bound states between a Rydberg atom and one or more ground-state atoms with bond lengths on the order of thousands of Bohr radii. The binding originates from multiple electron-atom scattering and leads to exotic oscillatory potential energy surfaces that reflect the probability density of the Rydberg electron. This unconventional binding mechanism opens fascinating possibilities to tune molecular properties via weak external fields, to study spin-resolved low-energy electron-atom scattering as well as to control and to probe interatomic forces in few- and many-body systems. Here, we provide an overview on recent theoretical and experimental progress in the field with an emphasis on polyatomic ULRMs, field control and spin interactions. GRAPHICAL ABSTRACT
Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks

Recent studies demonstrated that the average heart rate (HR) can be measured from facial videos based on non-contact remote photoplethysmography (rPPG). However for many medical applications (e.g., atrial fibrillation (AF) detection) knowing only the average HR is not sufficient, and measuring precise rPPG signals from face for heart rate variability (HRV) analysis is needed. Here we propose an rPPG measurement method, which is the first work to use deep spatio-temporal networks for reconstructing precise rPPG signals from raw facial videos. With the constraint of trend-consistency with ground truth pulse curves, our method is able to recover rPPG signals with accurate pulse peaks. Comprehensive experiments are conducted on two benchmark datasets, and results demonstrate that our method can achieve superior performance on both HR and HRV levels comparing to the state-of-the-art methods. We also achieve promising results of using reconstructed rPPG signals for AF detection and emotion recognition.
Resonant photoproduction of high-energy electron-positron pairs in the field of a nucleus and a weak electromagnetic wave

The resonant photoproduction of ultrarelativistic electron-positron pairs (PPP) in a nuclear field and a weak laser field is theoretically studied. Under resonance conditions, the intermediate virtual electron (positron) in the laser field becomes a real particle. As a result, the initial process of the second order in the fine structure constant in the laser field effectively reduces into two successive processes of the first order: single-photon production of electron-positron pair in a laser field (laser-stimulated Breit-Wheeler process) and laser-assisted process of electron (positron) scattering on a nucleus. Resonant kinematics of PPP is studied in details. It is shown that for the considered laser intensities resonance is possible only for the initial photon energies greater than the characteristic threshold energy. At the same time, the ultrarelativistic electron and positron propagate in a narrow cone along the direction of the initial photon momentum. The resonant energy of the positron (electron) can has two values for each radiation angle which varies from zero to some maximum value determined by the energy of the initial photon and the threshold energy. Resonant differential cross section of the studied process was obtained. It is shown that the resonant differential cross section of the PPP can significantly exceed the corresponding cross section of the PPP without an external field. The project calculations may be experimentally verified by the scientific facilities of pulsed laser radiation (SLAC, FAIR, XFEL, ELI, XCELS)
Expectation-Maximization Attention Networks for Semantic Segmentation

Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r. t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context, and COCO Stuff, on which we set new records.
Modelling the Effects of Dark Matter Substructure on Globular Cluster Evolution with the Tidal Approximation

We present direct N-body simulations of tidally filling 30,000 M⊙ star clusters orbiting between 10 kpc and 100 kpc in galaxies with a range of dark matter substructure properties. The time-dependent tidal force is determined based on the combined tidal tensor of the galaxy’s smooth and clumpy dark matter components, the latter of which causes fluctuations in the tidal field that can heat clusters. The strength and duration of these fluctuations are sensitive to the local dark matter density, substructure fraction, sub-halo mass function, and the sub-halo mass-size relation. Based on the cold dark matter framework, we initially assume sub-halos are Hernquist spheres following a power-law mass function between 105 and 1011M⊙ and find that tidal fluctuations are too weak and too short to affect star cluster evolution. Treating sub-halos as point masses, to explore how denser sub-halos affect clusters, we find that only sub-halos with masses greater than 106M⊙ will cause cluster dissolution times to decrease. These interactions can also decrease the size of a cluster while increasing the velocity dispersion and tangential anisotropy in the outer regions via tidal heating. Hence increased fluctuations in the tidal tensor, especially fluctuations that are due to low-mass halos, do not necessarily translate into mass loss. We further conclude that the tidal approximation can be used to model cluster evolution in the tidal fields of cosmological simulations with a minimum cold dark matter sub-halo mass of 106M⊙, as the effect of lower-mass sub-halos on star clusters is negligible.
Hybrid Spatio-Spectral Total Variation: A Regularization Technique for Hyperspectral Image Denoising and Compressed Sensing.

We propose a new regularization technique, named Hybrid Spatio-Spectral Total Variation (HSSTV), for hyperspectral (HS) image denoising and compressed sensing. Regularization techniques based on total variation (TV) focus on local differences of an HS image to model its underlying smoothness and have been recognized as a popular approach to HS image restoration. However, existing TVs do not fully exploit underlying spectral correlation in their designs and/or require a high computational cost in optimization. Our HSSTV is designed to simultaneously evaluates two types of local differences: direct local spatial differences and local spatio-spectral differences in a unified manner with a balancing weight. This design resolves the said drawbacks of existing TVs. Then, we formulate HS image restoration as a constrained convex optimization problem involving HSSTV and develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) for solving it. In the experiments, we illustrate the advantages of HSSTV over several state-of-the-art methods.
A note on projective toric codes

The aim of this work is to give degree formulas for the generalized Hamming weights of evaluation codes and to show lower bounds for these weights. In particular, we give degree formulas for the generalized Hamming weights of Reed--Muller-type codes, and we determine the minimum distance of toric codes over hypersimplices, and the 1st and 2nd generalized Hamming weights of squarefree evaluation codes.
Clustering and classification methods

This chapter provides a review of the field of clustering and classification as applied in the behavioral sciences. The intent is to provide a set of recommendations or best practices in the analysis of empirical data. Clustering and classification methods are presented within a context of exploratory data analysis, as opposed to theory development or confirmation. Some methods or strategies useful for theory confirmation are discussed as appropriate.
Testing and debugging: A reality check

Testing and debugging are important activities during software development and maintenance. Testing is performed to check if the code contains errors whereas debugging is done to locate and fix these errors. Testing can be manual or automated and can be of different types such as unit, integration, system, stress etc. Debugging can also be manual or automated. These two activities have drawn attention of researchers in the recent years. Past studies have proposed many testing techniques such as automated test generation, test minimization, test case selection etc. Studies related to debugging have proposed new techniques to find bugs using various fault localization schemes such as spectrum-based fault localization, IR-based fault localization, program slicing, delta debugging etc. to accurately and efficiently find bugs. However, even after years of research software continues to have bugs, which can have significant implications for the organization and economy. Often developers mention that the number of bugs they receive for the project overwhelms the resources they have. This brings forth the question of analyzing the current state of testing and debugging to understand its advantages and shortcomings. Also, many debugging techniques proposed in the past may ignore bias in data which can lead to wrong results. Furthermore, it is equally important to understand the expectations of practitioners who are currently using or will use these techniques. These analyses will help researchers understand pain points and expectations of practitioners which will help them design better techniques. In this thesis, I take a step in this direction by conducting large-scale data analysis and by interviewing and surveying large number of practitioners. By analysing the quantitative and qualitative data, I plan to bring forward the gap between practitioners’ expectations and the research ouput. My thesis sheds light on current state-of-practice in testing in open-source projects, the tools currently used by developers and challenges faced by them during testing. For bug localization, I find that files that are already localized can have an impact on the results and this bias must be removed before running a bug localization algorithm. Furthermore, practitioners have a high expectation when it comes to adopting a new bug localization tool. I also propose a technique to help developers find elements to test. Furthermore, through interviews and surveys, I provide suggestions for developers to create good test cases based on several characteristics such as size and complexity, coverage, maintainability, bug detection etc. In the future, I plan to perform a longitudinal study to understand the causal impact of testing on software quality. Furthermore, I plan to perform an empirical validation of good test cases based on the suggestions received from the practitioners.
Landmark Detection in Low Resolution Faces with Semi-Supervised Learning

Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images. This deters the performance of algorithms relying on quality landmarks, for example, face recognition. To the best of our knowledge, there does not exist any dataset consisting of low resolution face images along with their annotated landmarks, making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks on low resolution images by learning them from labeled high resolution images. The objective of this work is to show that predicting landmarks directly on low resolution images is more effective than the current practice of aligning images after rescaling or superresolution. In a two-step process, the proposed approach first learns to generate low resolution images by modeling the distribution of target low resolution images. In the second stage, the roles of generated images and real low resolution images are switched and the model learns to predict landmarks for real low resolution images from generated low resolution images. With extensive experimentation, we study the impact of each of the design choices and also show that prediction of landmarks directly on low resolution images improves the performance of important tasks such as face recognition in low resolution images.
Rate Splitting for Massive MIMO Multi-carrier system using Full Duplex Decode and Forward Relay with Hardware Impairments

In this paper, we address the power allocation problem for a decode and forward (DF) relay system, where a massive multiple-input-multiple-output (mMIMO) multi-carrier (MC) base station (BS) node communicates with a MC single antenna node directly and also through the single antenna full duplex (FD) MC relay, using rate splitting (RS) approach. Successive interference cancellation approach is adopted at the destination. We consider orthogonal frequency division multiplexing (OFDM) as our MC strategy. We take into account the impact of hardware distortions resulting in residual self-interference and inter-carrier leakage (ICL), and also imperfect channel state information (CSI). We formulate a joint sub-carrier and power allocation problem to maximize the total sum rate. An iterative optimization method is proposed, which follows successive inner approximation (SIA) framework to reach the convergence point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show the significance of distortion-aware design for such systems, and also the significant gain in terms of sum rate compared to its half duplex (HD) and also non-rate splitting scheme.
Learning Robust Heterogeneous Signal Features from Parallel Neural Network for Audio Sentiment Analysis

Audio Sentiment Analysis is a popular research area which extends the conventional text-based sentiment analysis to depend on the effectiveness of acoustic features extracted from speech. However, current progress on audio sentiment analysis mainly focuses on extracting homogeneous acoustic features or doesn't fuse heterogeneous features effectively. In this paper, we propose an utterance-based deep neural network model, which has a parallel combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based network, to obtain representative features termed Audio Sentiment Vector (ASV), that can maximally reflect sentiment information in an audio. Specifically, our model is trained by utterance-level labels and ASV can be extracted and fused creatively from two branches. In the CNN model branch, spectrum graphs produced by signals are fed as inputs while in the LSTM model branch, inputs include spectral features and cepstrum coefficient extracted from dependent utterances in audio. Besides, Bidirectional Long Short-Term Memory (BiLSTM) with attention mechanism is used for feature fusion. Extensive experiments have been conducted to show our model can recognize audio sentiment precisely and quickly, and demonstrate our ASV is better than traditional acoustic features or vectors extracted from other deep learning models. Furthermore, experimental results indicate that the proposed model outperforms the state-of-the-art approach by 9.33\% on Multimodal Opinion-level Sentiment Intensity dataset (MOSI) dataset.
Application of LSTM Neural Network in Forecasting Foreign Exchange Price

LSTM neural network and RNN neural network models in deep learning are used to forecast the price of foreign exchange financial time series. The existing foreign exchange price and technical analysis indexes are taken as input parameters. By comparing the evaluation indexes of two deep learning models, the optimal neural network model is selected. The experimental results show that the LSTM neural network model has smaller root mean square error (RMSE) and mean absolute error (MAE) than the RNN network model, and the predicted price is more accurate.