Reviewerly/exHarmony huggingface dataset:
Sensitivity of entanglement measures in bipartite pure quantum states

Entanglement measures quantify the amount of quantum entanglement that is contained in quantum states. Typically, different entanglement measures do not have to be partially ordered. The presence of a definite partial order between two entanglement measures for all quantum states, however, allows for meaningful conceptualization of sensitivity to entanglement, which will be greater for the entanglement measure that produces the larger numerical values. Here, we have investigated the partial order between the normalized versions of four entanglement measures based on Schmidt decomposition of bipartite pure quantum states, namely, concurrence, tangle, entanglement robustness and Schmidt number. We have shown that among those four measures, the concurrence and the Schmidt number have the highest and the lowest sensitivity to quantum entanglement, respectively. Further, we have demonstrated how these measures could be used to track the dynamics of quantum entanglement in a simple quantum toy model composed of two qutrits. Lastly, we have employed state-dependent entanglement statistics to compute measurable correlations between the outcomes of quantum observables in agreement with the uncertainty principle. The presented results could be helpful in quantum applications that require monitoring of the available quantum resources for sharp identification of temporal points of maximal entanglement or system separability.
Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings

Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies.
Creating own NFT token using erc721 standard and solidity programming language

Blockchain as a technology in recent years is increasingly mentioned as a technology that will dominate in the future, through various industries. The paper analyzes in which industries blockchain can be an advantage and how it can improve and make the user experience better and more secure. Blockchain also carries with it potential "threats" related to the way data is stored, so the most common possible ways of damaging the blockchain are analyzed. In the practical part of the paper, the article describes how the reader can independently make an NFT token based on the ERC721 standard Ethereum blockchain and how to set it on the blockchain and distribute, and potentially one can make money on it, which is another case of using the blockchain.
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask

Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation.Most existing regression based methods resort to regress the masks or contour points of text regions to model the text instances.However, regressing the complete masks requires high training complexity, and contour points are not sufficient to capture the details of highly curved texts.To tackle the above limitations, we propose a novel light-weight anchor-free text detection framework called TextDCT, which adopts the discrete cosine transform (DCT) to encode the text masks as compact vectors.Further, considering the imbalanced number of training samples among pyramid layers, we only employ a single-level head for top-down prediction.To model the multi-scale texts in a single-level head, we introduce a novel positive sampling strategy by treating the shrunk text region as positive samples, and design a feature awareness module (FAM) for spatial-awareness and scale-awareness by fusing rich contextual information and focusing on more significant features.Moreover, we propose a segmented non-maximum suppression (S-NMS) method that can filter low-quality mask regressions.Extensive experiments are conducted on four challenging datasets, which demonstrate our TextDCT obtains competitive performance on both accuracy and efficiency.Specifically, TextDCT achieves F-measure of 85.1 at 17.2 frames per second (FPS) and F-measure of 84.9 at 15.1 FPS for CTW1500 and Total-Text datasets, respectively.Index Terms-Arbitrary-shaped scene text detection
Security Issues of Novel RSA Variant

The RSA is one of the current default cryptosystems that provides security with applications such as encryptions and digital signatures. It is important to further study the weak characteristics of the RSA to ensure correct utilisation in order not to be susceptible to adversaries. In this paper, we give detailed analysis on security of the Murru-Saettone variant of the RSA cryptosystem that utilised a cubic Pell ed – k ( p2 + p + 1 ) ( q2 + q + 1 ) = 1 as key equation and N = pq as RSA modulus. We propose some attacks on this variant when the prime difference |p – q| is small. Our first approach is to utilise the continued fractions algorithm to determine the parameter d which enables us to determine the secret p and q. Our second approach considers the Coppersmith’s method and lattice basis reduction to factor the modulus N. Our attacks improve recent cryptanalyses on the cubic Pell equation variant of RSA. Furthermore, our attacks prove that under small prime difference scenario, the number of susceptible private exponents for the cubic Pell equation variant of RSA is much larger than the standard RSA.
Joint Sample Enhancement and Instance-Sensitive Feature Learning for Efficient Person Search

Person search, consisting of jointly or separately trained person detection stage and person Re-ID stage, suffers from significant challenges such as inefficiency and difficulty in acquiring discriminative features. However, certain work has either turned to the end-to-end framework whose performance is limited by task conflicts or has consistently attempted to obtain more accurate bounding boxes (Bboxes). Few studies have focused on the impact of sample-specificity in person search datasets for training a fine-grain Re-ID model, and few have considered obtaining discriminative Re-ID features from Bboxes in a more efficient way. In this paper, a novel sample-enhanced and instance-sensitive (SEIE) framework is designed to boost performance. By analyzing the structure of person search framework, our method refines the two stages separately. For the detection stage, we re-design the usage of Bbox and a sample enhancement combination is proposed to further enhance the quality and quantity of Bboxes. SEC can suppress false positive detection results and randomly generate high-quality positive samples. For the Re-ID stage, we contribute an instance similarity loss to exploit the similarity between classless instances, and an Omni-scale Re-ID backbone is employed to learn more discriminative features. We obtain a more efficient and discriminative person search framework by concatenating the two stages. Extensive experiments demonstrate that our method achieves state-of-the-art performance with a high speed, and significantly outperforms other existing methods.
Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping

Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.
Reputation-Aware Federated Learning Client Selection based on Stochastic Integer Programming

Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users&#x0027; data privacy. However, due to the distributive nature of FL, it is vulnerable to misbehavior from participating worker nodes. Thus, it is important to select clients to participate in FL. Recent studies on FL client selection focus on the perspective of improving model training efficiency and performance, without holistically considering potential misbehavior and the cost of hiring. To bridge this gap, we propose a first-of-its-kind reputation-aware <u>S</u>tochastic integer programming-based FL <u>C</u>lient <u>S</u>election method (SCS). It can optimally select and compensate clients with different reputation profiles. Extensive experiments show that SCS achieves the most advantageous performance-cost trade-off compared to other existing state-of-the-art approaches.
Quality Prediction of Continuous Casting Slabs Based on Weighted Extreme Learning Machine

A slab quality prediction model based on machine learning plays an important role in improving final slab quality.However, the class imbalance of continuous casting datasets has a negative impact on the training of basic machine-learning models.In this study, weighted extreme learning machine (WELM) models are constructed to predict the slab quality of under different operation patterns by feeding millions of data.The results show that WELM models can achieve better prediction performance on the two types of continuous casting datasets than the basic algorithms.The superiority of WELM is demonstrated by the relatively high-precision identification of every kind of slab.The performance of WELM models with different weighting schemes is studied and the model with the golden section ratio weighting method is recommended for application as a quality prediction model.Meanwhile, WELM can still maintain a good predictive performance and generalization ability when training a large amount of data.This model can satisfy the demands for slab quality prediction and optimize the continuous casting process.
Convolutional Neural Networks Quantization with Double-Stage Squeeze-and-Threshold

It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low precision during inference, thereby saving memory space and power consumption. However, quantizing networks is always accompanied by an accuracy decrease. Here, we propose a method, double-stage Squeeze-and-Threshold (double-stage ST). It uses the attention mechanism to quantize networks and achieve state-of-art results. Using our method, the 3-bit model can achieve accuracy that exceeds the accuracy of the full-precision baseline model. The proposed double-stage ST activation quantization is easy to apply: inserting it before the convolution.
A hybrid Aquila optimizer and its K-means clustering optimization

Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hybrid Aquila optimizer (HAO) algorithm based on Gauss map and crisscross operator is proposed. First, Gauss map is introduced to initialize the Aquila population to improve the quality of the initial population. Then use the crisscross operator to promote the exchange of information within the population and maintain the diversity of the population in each iteration, which not only enhances the ability of the algorithm to jump out of the local optimum but also accelerates the global convergence of the algorithm. The results of experiments using 21 classical benchmark functions indicate that HAO has better global search ability, faster convergence speed, and better stability than AO. The overall optimization performance of HAO in different dimensions is better than particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm, whale optimization algorithm (WOA), and crisscross optimization (CSO) algorithm. Finally, the results of K-means clustering optimization on six University of California (UCI) standard data sets demonstrate that HAO has significant advantages over three algorithms that are good at clustering optimization.
Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks

The robustness of deep neural networks is crucial to modern AI-enabled systems and should be formally verified. Sigmoid-like neural networks have been adopted in a wide range of applications. Due to their non-linearity, Sigmoid-like activation functions are usually over-approximated for efficient verification, which inevitably introduces imprecision. Considerable efforts have been devoted to finding the so-called tighter approximations to obtain more precise verification results. However, existing tightness definitions are heuristic and lack theoretical foundations. We conduct a thorough empirical analysis of existing neuron-wise characterizations of tightness and reveal that they are superior only on specific neural networks. We then introduce the notion of network-wise tightness as a unified tightness definition and show that computing network-wise tightness is a complex non-convex optimization problem. We bypass the complexity from different perspectives via two efficient, provably tightest approximations. The results demonstrate the promising performance achievement of our approaches over state of the art: (i) achieving up to 251.28% improvement to certified lower robustness bounds; and (ii) exhibiting notably more precise verification results on convolutional networks.
A Self-Supervised, Differentiable Kalman Filter for Uncertainty-Aware Visual-Inertial Odometry

Visual-inertial odometry (VIO) systems traditionally rely on filtering or optimization-based techniques for egomotion estimation. While these methods are accurate under nominal conditions, they are prone to failure during severe illumination changes, rapid camera motions, or on low-texture image sequences. Learning-based systems have the potential to outperform classical implementations in challenging environments, but, currently, do not perform as well as classical methods in nominal settings. Herein, we introduce a framework for training a hybrid VIO system that leverages the advantages of learning and standard filtering-based state estimation. Our approach is built upon a differentiable Kalman filter, with an IMU-driven process model and a robust, neural network-derived relative pose measurement model. The use of the Kalman filter framework enables the principled treatment of uncertainty at training time and at test time. We show that our self-supervised loss formulation outperforms a similar, supervised method, while also enabling online retraining. We evaluate our system on a visually degraded version of the EuRoC dataset and find that our estimator operates without a significant reduction in accuracy in cases where classical estimators consistently diverge. Finally, by properly utilizing the metric information contained in the IMU measurements, our system is able to recover metric scene scale, while other self-supervised monocular VIO approaches cannot.
The XYZ<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi /><mml:mn>2</mml:mn></mml:msup></mml:math> hexagonal stabilizer code

We consider a topological stabilizer code on a honeycomb grid, the "XYZ$^2$" code. The code is inspired by the Kitaev honeycomb model and is a simple realization of a "matching code" discussed by Wootton [J. Phys. A: Math. Theor. 48, 215302 (2015)], with a specific implementation of the boundary. It utilizes weight-six ($XYZXYZ$) plaquette stabilizers and weight-two ($XX$) link stabilizers on a planar hexagonal grid composed of $2d^2$ qubits for code distance $d$, with weight-three stabilizers at the boundary, stabilizing one logical qubit. We study the properties of the code using maximum-likelihood decoding, assuming perfect stabilizer measurements. For pure $X$, $Y$, or $Z$ noise, we can solve for the logical failure rate analytically, giving a threshold of 50%. In contrast to the rotated surface code and the XZZX code, which have code distance $d^2$ only for pure $Y$ noise, here the code distance is $2d^2$ for both pure $Z$ and pure $Y$ noise. Thresholds for noise with finite $Z$ bias are similar to the XZZX code, but with markedly lower sub-threshold logical failure rates. The code possesses distinctive syndrome properties with unidirectional pairs of plaquette defects along the three directions of the triangular lattice for isolated errors, which may be useful for efficient matching-based or other approximate decoding.
Memory effects on bidirectional teleportation

In this contribution, we have investigated the bidirectional quantum teleportation (BQT) of single-qubit states using a Bell state influenced by decoherence channels with memory, dephasing and amplitude damping channels. The expressions of the negativity, as a measure of the entanglement remaining in the BQT quantum channel, the teleportation fidelities and the quantum Fisher information are also evaluated. We find that both these last quantities depend on the survival amount of entanglement in the BQT quantum channel, on the decoherence factor and on the correlation degree of the decoherence channel. We show that in the Markovian regime, the Negativity, the teleportation average fidelities and the quantum Fisher information are slightly enhanced by considering the classical channel correlations. Besides, in the non-Markovian regime, these three quantities could be improved for a long period of time.
Detecting anomalous referencing patterns in PubMed papers suggestive of author-centric reference list manipulation

Abstract Although citations are used as a quantifiable, objective metric of academic influence, references could be added to a paper solely to inflate the perceived influence of a body of research. This reference list manipulation (RLM) could take place during the peer-review process, or prior to it. Surveys have estimated how many people may have been affected by coercive RLM at one time or another, but it is not known how many authors engage in RLM, nor to what degree. By examining a subset of active, highly published authors (n = 20,803) in PubMed, we find the frequency of non-self-citations (NSC) to one author coming from a single paper approximates Zipf’s law. Author-centric deviations from it are approximately normally distributed, permitting deviations to be quantified statistically. Framed as an anomaly detection problem, statistical confidence increases when an author is an outlier by multiple metrics. Anomalies are not proof of RLM, but authors engaged in RLM will almost unavoidably create anomalies. We find the NSC Gini Index correlates highly with anomalous patterns across multiple “red flags”, each suggestive of RLM. Between 81 (0.4%, FDR &lt; 0.05) and 231 (1.1%, FDR &lt; 0.10) authors are outliers on the curve, suggestive of chronic, repeated RLM. Approximately 16% of all authors may have engaged in RLM to some degree. Authors who use 18% or more of their references for self-citation are significantly more likely to have NSC Gini distortions, suggesting a potential willingness to coerce others to cite them.
Proposal for low-power atom trapping on a GaN-on-sapphire chip

Hybrid photon-atom integrated circuits, which include photonic microcavities and trapped single neutral atoms in their evanescent field, have great potential for quantum information processing. In this platform, the atoms provide single-photon nonlinearity and long-lived memory, which are complementary to the excellent passive photonic devices in conventional quantum photonic circuits. In this work, we propose a stable platform for realizing hybrid photon-atom circuits based on an unsuspended photonic chip. By introducing high-order modes in the microring, a feasible evanescent-field trap potential well $\ensuremath{\sim}0.26\phantom{\rule{0.16em}{0ex}}\mathrm{mK}$ could be obtained by only 10-$\mathrm{mW}$-level power in the cavity, compared with the 100-mW-level power required in the scheme based on fundamental modes. Based on our scheme, stable single-atom trapping with relatively low laser power is feasible for future studies on high-fidelity quantum gates, single-photon sources, and many-body quantum physics based on a controllable atom array in a microcavity.
Many-body localization enables iterative quantum optimization

We suggest an iterative quantum protocol, allowing to solve optimization problems with a glassy energy landscape. It is based on a periodic cycling around the tricritical point of the many-body localization transition. This ensures that each iteration leads to a non-exponentially small probability to find a lower local energy minimum. The other key ingredient is to tailor the cycle parameters to a currently achieved optimal state (the "reference" state) and to reset them once a deeper minimum is found. We show that, if the position of the tricritical point is known, the algorithm allows to approach the absolute minimum with any given precision in a polynomial time.
Exploiting Augmented Reality Technology in Special Education: A Systematic Review

In this systematic review, research works pertaining to the use of augmented reality (AR) in special education are investigated. In recent years, the introduction of Information and Communication Technology (ICT) in education has transformed the teaching and learning process. Augmented reality is an emerging technology, which has been widely used in education during the past few years. However, only few studies focus on the advantages and limitations of AR use in special education. This review investigates research in AR educational systems addressed to students with special needs. In total, 14 studies between 2014 and 2022 were selected and analyzed. Specifically, this systematic review examines types of students that have been included in learning scenarios supported of AR, distribution of educational AR studies by field of education, types of technology developed to support the use of AR in special education, and the advantages and limitations of AR use in special education. The findings mainly show that AR technology, used on students with special educational needs, has multiple potential advantages.