Reviewerly/exHarmony huggingface dataset:
The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.
Say NO to Optimization: A Nonorthogonal Quantum Eigensolver

A balanced description of both static and dynamic correlations in electronic systems with nearly degenerate low-lying states presents a challenge for multiconfigurational methods on classical computers. We present here a quantum algorithm utilizing the action of correlating cluster operators to provide high-quality wave-function ansätze employing a nonorthogonal multireference basis that captures a significant portion of the exact wave function in a highly compact manner and that allows computation of the resulting energies and wave functions at polynomial cost with a quantum computer. This enables a significant improvement over the corresponding classical nonorthogonal solver, which incurs an exponential cost when evaluating off-diagonal matrix elements between the ansatz states and is therefore intractable. We implement the nonorthogonal quantum eigensolver (NOQE) here with an efficient ansatz parametrization inspired by classical quantum chemistry methods that succeed in capturing significant amounts of electronic correlation accurately. Crucially, we avoid the need to perform any optimization of the ansatz on the quantum device. By taking advantage of such classical approaches, NOQE provides a flexible, compact, and rigorous description of both static and dynamic electronic correlation, making it an attractive method for the calculation of electronic states of a wide range of molecular systems.7 MoreReceived 26 May 2022Revised 21 April 2023Accepted 23 May 2023DOI:https://doi.org/10.1103/PRXQuantum.4.030307Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElectronic structure of atoms & moleculesFirst-principles calculationsQuantum algorithms for chemical calculationsInterdisciplinary PhysicsQuantum InformationCondensed Matter, Materials & Applied Physics
Improving robustness of convolutional neural networks using element-wise activation scaling

Recent works reveal that re-calibrating intermediate activation of adversarial examples can improve the adversarial robustness of CNN models. The state of the arts exploit this feature at the channel level to help CNN models defend adversarial attacks, where each intermediate activation is uniformly scaled by a factor. However, we conduct a more fine-grained analysis on intermediate activation and observe that adversarial examples only change a portion of elements within an activation. This observation motivates us to investigate a new method to re-calibrate intermediate activation of CNNs to improve robustness. Instead of uniformly scaling each activation, we individually adjust each element within an activation and thus propose Element-Wise Activation Scaling, dubbed EWAS, to improve CNNs’ adversarial robustness. EWAS is a simple yet very effective method in enhancing robustness. Experimental results on ResNet-18 and WideResNet with CIFAR10 and SVHN show that EWAS significantly improves the robustness accuracy. Especially for ResNet18 on CIFAR10, EWAS increases the adversarial accuracy by 37.65% to 82.35% against C&W attack. The code and trained models are available at https://github.com/ieslab-ynu/EWAS.
Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterization of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS.
An optimal method for the Shortest Path Problem: The Dhouib-Matrix-SPP (DM-SPP)

This paper introduces an optimal method entitled Dhouib-Matrix-SPP (DM-SPP) in order to solve the Shortest Path Problem with a complexity time of On+m where n and m are respectively the number of vertices and edges. DM-SPP is a rapid method, it can reduce reasonably the research time and consequently the running time as well as the energy consumption. It is composed of five simple steps repeated in (n-1) iterations and for more clarification a guided step-by-step application of the novel method DM-SPP is presented. Moreover, several examples are solved wherein a fully complete and random distribute graphs are analyzed with the variation of the number of vertices from 20 to 6000 and the number of edges from 49 to 17097565. DM-SPP is coded under Python programming language and all experimental results demonstrate that DM-SPP can rapidly generate the optimal short path. Furthermore, by comparing the complexity time required by DM-SPP to the time prerequisite by Dijkstra algorithm, it can be concluded that DM-SPP and Dijkstra are concurrent for small instances and for larger instances DM-SPP can easily outperform Dijkstra and especially for the case of incomplete undirected graphs which are more realistic in the real-world viewing that all graphs are really not complete. The performance of DM-SPP is statistically confirmed with the Mann–Whitney U nonparametric test Further research trends will focus on the test of DM-SPP for the uncertain Shortest Path problem and the resolution of the autonomous mobile robot path planning problem.
A hybrid elliptic curve cryptography (HECC) technique for fast encryption of data for public cloud security

Cloud computing provides the users a centralized place for data storage and other commercial applications. To guarantee everything is secure, cloud security has to handle all the devices, apps, and data connected to the cloud. Because of the cloud's robust security, the data and applications may be accessed by the relevant people. The usage of public cloud security ensures that customers always have a trustworthy method of accessing apps and data, which in turn enables service providers to resolve any potential security problems as soon as they arise. This paper presents a public cloud security technique using Hybrid Elliptic Curve Cryptography (HECC). The proposed approach makes keys using a lightweight Edwards curve. The user's Identity Based Encryption is used to change the generated private keys. The proposed key reduction method is used to make the keys even shorter, which speeds up the Advanced Encryption standard (AES) encryption process. The public keys are exchanged using Diffie Hellman key exchange. Throughput and key generation, encryption, and decryption times are used to evaluate how well the suggested model performs. The suggested model fared better than the current models in every way. The suggested method's key creation process takes 0.000025 seconds, while the resulting encryption process takes 0.00349 seconds. The achieved throughput is 693.10 KB/sec..
Wave Matrix Lindbladization I: Quantum Programs for Simulating Markovian Dynamics

Density Matrix Exponentiation is a technique for simulating Hamiltonian dynamics when the Hamiltonian to be simulated is available as a quantum state. In this paper, we present a natural analogue to this technique, for simulating Markovian dynamics governed by the well known Lindblad master equation. For this purpose, we first propose an input model in which a Lindblad operator $L$ is encoded into a quantum state $\psi$. Then, given access to $n$ copies of the state $\psi$, the task is to simulate the corresponding Markovian dynamics for time $t$. We propose a quantum algorithm for this task, called Wave Matrix Lindbladization, and we also investigate its sample complexity. We show that our algorithm uses $n = O(t^2/\varepsilon)$ samples of $\psi$ to achieve the target dynamics, with an approximation error of $O(\varepsilon)$.
Artificial intelligence to enhance corporate governance: A conceptual framework

In this preliminary study, we explore the novel intersection of corporate governance (CG) and artificial intelligence (AI), addressing the crucial question: How can AI be leveraged to enhance ethical and transparent decision-making within the corporate environment? Drawing from current studies on organizational governance, AI ethics, and data science, our research raises the curtain on the potential of AI in augmenting traditional governance mechanisms, while also scrutinizing the ethical quandaries and challenges it may pose. We propose a novel conceptual framework, rooted in the principles of separation of ownership and control, and data ethics, to be underpinned and validated, in the future, through an empirical study. Given the current inception stage of the study, we expect the results will illustrate a significant positive impact of AI on CG effectiveness, particularly in enhancing transparency and fostering ethical decision-making. We also propose future studies to be done as a mix of econometric and machine learning methods to empirically test the framework with datasets gathered over a period of years.
Satisfiability Modulo Finite Fields

Abstract We study satisfiability modulo the theory of finite fields and give a decision procedure for this theory. We implement our procedure for prime fields inside the cvc5 SMT solver. Using this theory, we construct SMT queries that encode translation validation for various zero knowledge proof compilers applied to Boolean computations. We evaluate our procedure on these benchmarks. Our experiments show that our implementation is superior to previous approaches (which encode field arithmetic using integers or bit-vectors).
MVMA-GCN: Multi-view multi-layer attention graph convolutional networks

The accuracy of graph representation learning is highly dependent on the precise characterization of node relationships. However, representing the complex and diverse networks in the real world using a single type of node or link is challenging, often resulting in incomplete information. Moreover, different types of nodes and links convey rich information, which makes it difficult to design a graph network that can integrate diverse links. This paper introduces a novel multi-view and multi-layer attention model designed to optimize node embeddings for semi-supervised node classification. The proposed model exploits various types of inter-node links and employs the Hilbert–Schmidt independence criterion to maximize the dissimilarity between distinct node relationships. Furthermore, the multi-layer attention mechanism is used to discern the impact of different neighboring nodes and relationships between various node relationships. The performance of the proposed model, MVMA-GCN, was assessed on numerous real-world multi-view datasets. It was observed that MVMA-GCN consistently outperformed existing models, demonstrating superior accuracy in semi-supervised classification tasks. We have made our code publicly available at here to ensure the reproducibility of our results.
Improving news headline text generation quality through frequent POS-Tag patterns analysis

Original synthetic content writing is one of the human abilities that algorithms aspire to emulate. The advent of sophisticated algorithms, especially based on neural networks has shown promising results in recent times. A watershed moment was witnessed when the attention mechanism was introduced which paved the way for transformers, a new exciting architecture in natural language processing. Recent sensations like GPT and BERT for synthetic text generation rely on NLP transformers. Although, GPT and BERT-based models are capable of generating creative text given they are properly trained on abundant data, however, the generated text suffers the quality aspect when limited data is available. This is especially an issue for low-resource languages where labeled data is still scarce. In such cases, the generated text, more often than not, lacks the proper sentence structure, thus unreadable. This study proposes a post-processing step in text generation that improves the quality of generated text through the GPT model. The proposed post-processing step is based on the analysis of POS tagging patterns in the original text and accepts only those generated sentences from GPT which satisfy POS patterns that are originally learned from the data. We exploit the GPT model to generate English headlines by utilizing Australian Broadcasting Corporation (ABC) news dataset. Furthermore, for assessing the applicability of the model in low-resource languages, we also train the model on the Urdu news dataset for Urdu news headlines generation. The experiments presented in this paper on these datasets from high- and low-resource languages show that the performance of generated headlines has a significant improvement by using the proposed headline POS pattern extraction. We evaluate the performance through subjective evaluation as well as using text generation quality metrics like BLEU and ROUGE.
Coherence and entanglement in Grover and Harrow–Hassidim–Lloyd algorithm

Coherence, discord and geometric measure of entanglement are important tools for measuring physical resources. We compute them at every steps of the Grover's algorithm. We summarize these resources's patterns of change. These resources are getting smaller at the step oracle and are getting bigger or invariant at the step diffuser. This result is similar to the entanglement's pattern of change in Grover's algorithm. Furthermore, we compute GM at every steps of the Harrow-Hassidim-Lloyd algorithm.
Multi-Grained Topological Pre-Training of Language Models in Sponsored Search

Relevance models measure the semantic closeness between queries and the candidate ads, widely recognized as the nucleus of sponsored search systems. Conventional relevance models solely rely on the textual data within the queries and ads, whose performance is hindered by the scarce semantic information in these short texts. Recently, user behavior graphs have been incorporated to provide complementary information beyond pure textual semantics.Despite the promising performance, behavior-enhanced models suffer from exhausting resource costs due to the extra computations introduced by explicit topological aggregations. In this paper, we propose a novel Multi-Grained Topological Pre-Training paradigm, MGTLM, to teach language models to understand multi-grained topological information in behavior graphs, which contributes to eliminating explicit graph aggregations and avoiding information loss. Extensive experimental results over online and offline settings demonstrate the superiority of our proposal.
Multi-view Multi-aspect Neural Networks for Next-basket Recommendation

Next-basket recommendation (NBR) is a type of recommendation that aims to recommend a set of items to users according to their historical basket sequences. Existing NBR methods suffer from two limitations: (1) overlooking low-level item correlations, which results in coarse-grained item representation; and (2) failing to consider spurious interests in repeated behaviors, leading to suboptimal user interest learning. To address these limitations, we propose a novel solution named Multi-view Multi-aspect Neural Recommendation (MMNR) for NBR, which first normalizes the interactions from both the user-side and item-side, respectively, aiming to remove the spurious interests, and utilizes them as weights for items from different views to construct differentiated representations for each interaction item, enabling comprehensive user interest learning. Then, to capture low-level item correlations, MMNR models different aspects of items to obtain disentangled representations of items, thereby fully capturing multiple user interests. Extensive experiments on real-world datasets demonstrate the effectiveness of MMNR, showing that it consistently outperforms several state-of-the-art NBR methods.
Self-adaptive oversampling method based on the complexity of minority data in imbalanced datasets classification

Learning from imbalanced datasets is a nontrivial task for supervised learning community. Traditional classifiers may have difficulties to learn the concept related to the minority class when addressing imbalanced classification and the issues can become more deteriorated in the presence of other complicated aspects: overlapping, outliers and small disjuncts, etc. In this paper, we propose a self-adaptive oversampling algorithm based on the complexity of minority data for dealing with imbalanced datasets classification problems. In the proposed algorithm, various hyperspheres with different radii determined by imbalance ratio and the distances to the nearest enemy neighbors are firstly generated to cover all minority instances provided that they cannot contain any majority instance. Subsequently, the oversampling process is conducted only within these hyperspheres and thus the generated synthetic minority instances cannot intervene within the majority space, eventually avoiding overlapping issues during achieving between-class balance. In addition, a self-adaptive assignment strategy of oversampling sizes is developed based on the minority data complexity, where the hyperspheres with small radii and few instances in them are provided more chances to be oversampled. The strategy will favor addressing the outliers and small disjuncts issues since the hyperspheres covering the outliers and small disjuncts are usually of small sizes and contain few instances, which makes them have more chances to generate synthetic instances and thus eliminate within-class imbalance due to lack of density. Moreover, since the hyperspheres covering boundary minority instances are relatively small and thus are assigned with larger oversampling sizes, the proposed approach can also strengthen the boundary information of minority class, thus favoring the later learning tasks. The extensive experimental results on various simulated and real-world imbalanced datasets show that the proposed method significantly outperforms other state-of-the-art oversampling ones.
Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5

Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.