Reviewerly/exHarmony huggingface dataset:
Density-operator evolution: Complete positivity and the Keldysh real-time expansion

We study the reduced time-evolution of open quantum systems by combining quantum-information and statistical field theory. Inspired by prior work [EPL 102, 60001 (2013) and Phys. Rev. Lett. 111, 050402 (2013)] we establish the explicit structure guaranteeing the complete positivity (CP) and trace-preservation (TP) of the real-time evolution expansion in terms of the microscopic system-environment coupling. This reveals a fundamental two-stage structure of the coupling expansion: Whereas the first stage defines the dissipative timescales of the system --before having integrated out the environment completely-- the second stage sums up elementary physical processes described by CP superoperators. This allows us to establish the nontrivial relation between the (Nakajima-Zwanzig) memory-kernel superoperator for the density operator and novel memory-kernel operators that generate the Kraus operators of an operator-sum. Importantly, this operational approach can be implemented in the existing Keldysh real-time technique and allows approximations for general time-nonlocal quantum master equations to be systematically compared and developed while keeping the CP and TP structure explicit. Our considerations build on the result that a Kraus operator for a physical measurement process on the environment can be obtained by 'cutting' a group of Keldysh real-time diagrams 'in half'. This naturally leads to Kraus operators lifted to the system plus environment which have a diagrammatic expansion in terms of time-nonlocal memory-kernel operators. These lifted Kraus operators obey coupled time-evolution equations which constitute an unraveling of the original Schr\"odinger equation for system plus environment. Whereas both equations lead to the same reduced dynamics, only the former explicitly encodes the operator-sum structure of the coupling expansion.
Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior

Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).
A Non-Local Dual-Domain Approach to Cartoon and Texture Decomposition

This paper addresses the problem of cartoon and texture decomposition. Microtextures are characterized by their power spectrum, and we propose to extract cartoon and texture components from the information provided by the power spectrum of image patches. The contribution of texture to the spectrum of a patch is detected as statistically significant spectral components with respect to a null hypothesis modeling the power spectrum of a non-textured patch. The null-hypothesis model is built upon a coarse cartoon representation obtained by a basic yet fast filtering algorithm of the literature. Hence, the term “dual domain”: the coarse decomposition is obtained in the spatial domain and is an input of the proposed spectral approach. The statistical model is also built upon the power spectrum of patches with similar textures across the image. The proposed approach, therefore, falls within the family of non-local methods. Experimental results are shown in various application areas, including canvas pattern removal in fine arts painting, or periodic noise removal in remote sensing imaging.
Center-Emphasized Visual Saliency and a Contrast-Based Full Reference Image Quality Index

Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability. Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image. Psychological vision research suggests that human vision is biased to the center area of an image and display screen. As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts. To the best of our knowledge, previous IQA methods have not considered this fact. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region. We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ and LIVE), having a total of 3345 distorted images with 28 different kinds of distortions. Our method is compared with 13 state-of-the-art approaches. This comparison reveals the stronger correlation of our method with human-evaluated values. The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases. Moreover, faster processing makes it applicable to any real-time application. The MATLAB code is publicly available to test the algorithm and can be found online at.
Hardware-Aware Softmax Approximation for Deep Neural Networks

There has been a rapid development of custom hardware for accelerating the inference speed of deep neural networks (DNNs), by explicitly incorporating hardware metrics (e.g., area and energy) as additional constraints, in addition to application accuracy. Recent efforts mainly focused on linear functions (matrix multiplication) in convolutional (Conv) or fully connected (FC) layers, while there is no publicly available study on optimizing the inference of non-linear functions in DNNs, with hardware constraints. In this paper, we address the problem of cost-efficient inference for Softmax, a popular non-linear function in DNNs. We introduce a hardware-aware linear approximation framework by algorithm and hardware co-optimization, with the goal of minimizing the cost in terms of area and energy, without incurring significant loss in application accuracy. This is achieved by simultaneously reducing the operand bit-width and approximating cost-intensive operations in Softmax (e.g. exponential and division) with cost-effective operations (e.g. addition and bit shifts). We designed and synthesized a hardware unit for our approximation approach, to estimate the area and energy consumption. In addition, we introduce a training method to further save area and energy cost, by reduced precision. Our approach reduces area cost by 13 $$\times $$ and energy consumption by 2 $$\times $$ with 11-bit operand width, compared to baseline at 19-bit for VOC2007 dataset in Faster R-CNN.
Shoulder surfing: From an experimental study to a comparative framework

Shoulder surfing is an attack vector widely recognized as a real threat - enough to warrant researchers dedicating a considerable effort toward designing novel authentication methods to be shoulder surfing resistant. Despite a multitude of proposed solutions over the years, few have employed empirical evaluations and comparisons between different methods, and our understanding of the shoulder surfing phenomenon remains limited. Barring the challenges in experimental design, the reason for that can be primarily attributed to the lack of objective and comparable vulnerability measures. In this paper, we develop an ensemble of vulnerability metrics, a first endeavour toward a comprehensive assessment of a given method's susceptibility to observational attacks. In the largest on-site shoulder surfing experiment (n = 274) to date, we verify the model on four conceptually different authentication methods in two observation scenarios. On the example of a novel hybrid authentication method based on associations, we explore the effect of input type on the adversary's effectiveness. We provide first empirical evidence that graphical passwords are easier to observe; however, that does not necessarily mean that the observed information will allow the attacker to guess the victim's password easier. An in-depth analysis of individual metrics within the clusters offers insight into many additional aspects of the shoulder surfing attack not explored before. Our comparative framework makes an advancement in evaluation of shoulder surfing and furthers our understanding of observational attacks. The results have important implications for future shoulder surfing studies and the field of Password Security as a whole.
Online Nearest Neighbor Search Using Hamming Weight Trees

Nearest neighbor search is a basic and recurring proximity problem that has been studied for several decades. The goal is to preprocess a dataset of points so that we can quickly report the closet point(s) to any query point. Many recent applications of NNS involve datasets that are very large and dynamic, that is items of data items become available gradually. In this study, we propose a data structure for solving NNS for dynamic binary data where both query and dataset items are represented as binary strings. The proposed tree data structure, called the Hamming Weight Tree, is simple and as the names suggests, is based on partitioning the feature space of binary strings by exploiting the Hamming weights of the binary codes and their substrings. Given a Hamming Weight Tree of binary codes, we propose two search algorithms that accommodate nearest neighbor search for two different distance functions, the Hamming distance and the angular distance. Our empirical results show significant speedup in comparison with the best known large-scale solutions.
Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise

We perform a rigorous runtime analysis for the Univariate Marginal Distribution Algorithm on the LeadingOnes function, a well-known benchmark function in the theory community of evolutionary computation with a high correlation between decision variables. For a problem instance of size n, the currently best known upper bound on the expected runtime is O (nλ log λ + n2) (Dang and Lehre, GECCO 2015), while a lower bound necessary to understand how the algorithm copes with variable dependencies is still missing. Motivated by this, we show that the algorithm requires a eΩ(µ) runtime with high probability and in expectation if the selective pressure is low; otherwise, we obtain a lower bound of [MATH HERE] on the expected runtime. Furthermore, we for the first time consider the algorithm on the function under a prior noise model and obtain an O(n2) expected runtime for the optimal parameter settings. In the end, our theoretical results are accompanied by empirical findings, not only matching with rigorous analyses but also providing new insights into the behaviour of the algorithm.
A two-tiered curriculum to improve data management practices for researchers

Background Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher’s results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities. Materials and methods We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers’ satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class. Results and discussion The online curriculum increased librarians’ self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.
A framework development for the adoption of information and communication technology web technologies in higher education systems

Background: The adoption of information and communication technology (ICT) tools into educational systems has been at the forefront of the educational sector for decades. The integration of Web 2.0 and Web 3.0 technologies is progressively being encouraged worldwide across several universities to support teaching and learning processes and to offer students the possibility of learning experiences and engagements to suit their digital needs. Objectives: This article probes a framework development for the adoption of ICT web technologies in higher education systems (HES) and further suggests a framework for adoption with the aim of enhancing the mode of education delivery and improving business processes. An understanding of the benefits associated with Web 2.0 and Web 3.0 tools adoption is gained to support collaboration between students and educators and to build social presence through interactive learning. South African universities continue to experience circumstances in which many learners who enrol are novice users of Web 2.0 and Web 3.0 tools and require optimal support to bridge the gaps and the knowledge and skills exposure required. The problem with educators’ inability to incorporate Web 2.0 and Web 3.0 tools in their teaching and learning practices exists. Method: A mixed-method approach was applied in this study. The researchers conducted 15 separate interviews with educators coupled with randomly distributed questionnaires to students across three universities (North-West University [NWU], University of South Africa [UNISA] and University of Pretoria [UP]), a total of 969 was recoverable and analysed using analytical tool ATLAS.ti and SPSS. The researchers further validated the data consolidating both techniques used to generate a holistic assessment of the data analysed from the quantitative to support the qualitative findings. Results: Findings revealed that these tools are useful and will have a positive effect on the pedagogical environment, although there are challenges that may be considered during the adoption. These challenges relate to human factors (e.g. technophobia and cultural beliefs), security issues (e.g. privacy and intellectual property rights [IPRs]), ethical and legal issues, ICT infrastructures (e.g. cost implication, risk and ICT teaching facilities); and university policy frameworks. Conclusion: Despite these challenges, Web 2.0 and Web 3.0 technologies in HES offer varieties of teaching and learning platforms and an improved business administration process.
Visualizing probabilistic models and data with Intensive Principal Component Analysis

Unsupervised learning makes manifest the underlying structure of data without curated training and specific problem definitions. However, the inference of relationships between data points is frustrated by the “curse of dimensionality” in high dimensions. Inspired by replica theory from statistical mechanics, we consider replicas of the system to tune the dimensionality and take the limit as the number of replicas goes to zero. The result is intensive embedding, which not only is isometric (preserving local distances) but also allows global structure to be more transparently visualized. We develop the Intensive Principal Component Analysis (InPCA) and demonstrate clear improvements in visualizations of the Ising model of magnetic spins, a neural network, and the dark energy cold dark matter ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="normal">Λ</mml:mi> <mml:mtext>CDM</mml:mtext> </mml:math> ) model as applied to the cosmic microwave background.
Spectral Filter Tracking

Visual object tracking is a challenging computer vision task with numerous real-world applications. In this paper, we propose a simple but efficient Spectral Filter Tracking (SFT) method from the view of graph, where each candidate image region is modeled as a pixelwise grid graph. Instead of the conventional graph matching, we formulate the tracking as a plain least square regression problem of learning spectral filters on graphs to predict an optimal vertex, which indicates the center of the target. To bypass computationally expensive eigenvalue decomposition on graph Laplacian L, we parameterize spectral graph filters as a polynomial of L to aggregate local graph features according to spectral graph theory, in which Lk exactly encodes a k-hop local neighborhood of each vertex. Thus, different from the holistic regression in those correlation filter based methods, SFT can operate on localized regions around a pixel (i.e., a vertex), which can effectively reduce the influence of local variations and cluttered backgrounds. Furthermore, we observe that the correlation filter tracking may be viewed as a specific case of our proposed spectral filtering method. The implementation of SFT can simply boil down to only a few line codes, but surprisingly it beats the correlation filter based model with the same feature input, and achieves the state-of-the-art performance on OTB-2015 and VOT2016 under the same feature extraction strategy.
Multi-bit quantum random number generation from a single qubit quantum walk

Abstract We present a scheme for multi-bit quantum random number generation using a single qubit discrete-time quantum walk in one-dimensional space. Irrespective of the initial state of the qubit, quantum interference and entanglement of particle with the position space in the walk dynamics certifies high randomness in the system. Quantum walk in a position space of dimension 2 l + 1 ensures string of ( l + 2)-bits of random numbers from a single measurement. Bit commitment with the position space and control over the spread of the probability distribution in position space enable us with options to extract multi-bit random numbers. This highlights the power of one qubit , its practical importance in generating multi-bit string in single measurement and the role it can play in quantum communication and cryptographic protocols. This can be further extended with quantum walks in higher dimensions.
Improved Low-Memory Subset Sum and LPN Algorithms via Multiple Collisions

For enabling post-quantum cryptanalytic experiments on a meaningful scale, there is a strong need for low-memory algorithms. We show that the combination of techniques from representations, multiple collision finding, and the Schroeppel-Shamir algorithm leads to improved low-memory algorithms. For random subset sum instances $$(a_1, \ldots , a_n, t)$$ defined modulo $$2^n$$, our algorithms improve over the Dissection technique for small memory $$M < 2^{0.02n}$$ and in the mid-memory regime $$2^{0.13n}< M < 2^{0.2n}$$. An application of our technique to LPN of dimension k and constant error p yields significant time complexity improvements over the Dissection-BKW algorithm from Crypto 2018 for all memory parameters $$M< 2^{0.35 \frac{k}{\log k}}$$.
Hierarchy of temporal quantum correlations using a correlated spontaneous emission laser

As an active quantum system, the correlated spontaneous emission laser has many valuable applications in quantum information processing. Here, we report on temporal evolution of the quantum correlations such as quantum discord, entanglement, steering, and Bell non-locality for the field retrieved in the form of two-mode Gaussian state using a system of correlated spontaneous emission laser. We consider the initial modes of the cavity field as two independent arbitrary single-mode Gaussian states inside the cavity. The density matrix for the resulting cavity field is evaluated both analytically and numerically with respect to the time evolution of the laser system. The influences of the non-classicality and purity of the initial cavity modes, the Rabi frequency of the classical coupling field, and the cavity damping rates are studied thoroughly. We show explicitly that the boundaries for the four kinds of the temporal quantum correlations of the cavity field retrieved satisfy a strict hierarchy.
Two-Stage Training for Chinese Dialect Recognition

In this paper, we present a two-stage language identification (LID) system based on a shallow ResNet14 followed by a simple 2-layer recurrent neural network (RNN) architecture, which was used for Xunfei (iFlyTek) Chinese Dialect Recognition Challenge and won the first place among 110 teams. The system trains an acoustic model (AM) firstly with connectionist temporal classification (CTC) to recognize the given phonetic sequence annotation and then train another RNN to classify dialect category by utilizing the intermediate features as inputs from the AM. Compared with a three-stage system we further explore, our results show that the two-stage system can achieve high accuracy for Chinese dialects recognition under both short utterance and long utterance conditions with less training time.
Speech Replay Detection with x-Vector Attack Embeddings and Spectral Features

We present our system submission to the ASVspoof 2019 Challenge Physical Access (PA) task. The objective for this challenge was to develop a countermeasure that identifies speech audio as either bona fide or intercepted and replayed. The target prediction was a value indicating that a speech segment was bona fide (positive values) or spoofed (negative values). Our system used convolutional neural networks (CNNs) and a representation of the speech audio that combined x-vector attack embeddings with signal processing features. The x-vector attack embeddings were created from mel-frequency cepstral coefficients (MFCCs) using a time-delay neural network (TDNN). These embeddings jointly modeled 27 different environments and 9 types of attacks from the labeled data. We also used sub-band spectral centroid magnitude coefficients (SCMCs) as features. We included an additive Gaussian noise layer during training as a way to augment the data to make our system more robust to previously unseen attack examples. We report system performance using the tandem detection cost function (tDCF) and equal error rate (EER). Our approach performed better that both of the challenge baselines. Our technique suggests that our x-vector attack embeddings can help regularize the CNN predictions even when environments or attacks are more challenging.