Reviewerly/exHarmony huggingface dataset:
PTB: Robust physical backdoor attacks against deep neural networks in real world

Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create a hidden backdoor in target DNN model, and trigger the malicious behaviors by submitting specific backdoor instance. However, almost all the existing backdoor works focused on the digital domain, while few studies investigate the backdoor attacks in real physical world. Restricted to a variety of physical constraints, the performance of backdoor attacks in the real physical world will be severely degraded. In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world. Specifically, in the training phase, we perform a series of physical transformations on these injected backdoor instances at each round of model training, so as to simulate various transformations that a backdoor may experience in real world, thus improves its physical robustness. Experimental results on the state-of-the-art face recognition model show that, compared with the backdoor methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world. Under various complex physical conditions, by injecting only a very small ratio (0.5%) of backdoor instances, the attack success rate of physical backdoor attacks with the PTB method on VGGFace is 82%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11%. Meanwhile, the normal performance of the target DNN model has not been affected.
One Hundred Second Bit-Flip Time in a Two-Photon Dissipative Oscillator

Current implementations of quantum bits (qubits) continue to undergo too many errors to be scaled into useful quantum machines. An emerging strategy is to encode quantum information in the two meta-stable pointer states of an oscillator exchanging pairs of photons with its environment, a mechanism shown to provide stability without inducing decoherence. Adding photons in these states increases their separation, and macroscopic bit-flip times are expected even for a handful of photons, a range suitable to implement a qubit. However, previous experimental realizations have saturated in the millisecond range. In this work, we aim for the maximum bit-flip time we could achieve in a two-photon dissipative oscillator. To this end, we design a Josephson circuit in a regime that circumvents all suspected dynamical instabilities, and employ a minimally invasive fluorescence detection tool, at the cost of a two-photon exchange rate dominated by single-photon loss. We attain bit-flip times of the order of 100 seconds for states pinned by two-photon dissipation and containing about 40 photons. This experiment lays a solid foundation from which the two-photon exchange rate can be gradually increased, thus gaining access to the preparation and measurement of quantum superposition states, and pursuing the route towards a logical qubit with built-in bit-flip protection.
ranx: A Blazing-Fast Python Library for Ranking Evaluation and Comparison

This paper presents ranx, a Python evaluation library for Information Retrieval built on top of Numba. ranx provides a user-friendly interface to the most common ranking evaluation metrics, such as MAP, MRR, and NDCG. Moreover, it offers a convenient way of managing the evaluation results, comparing different runs, performing statistical tests between them, and exporting LaTeX tables ready to be used in scientific publications, all in a few lines of code. The efficiency brought by Numba, a just-in-time compiler for Python code, makes the adoption ranx convenient even for industrial applications.
Quantum readout error mitigation via deep learning

Abstract Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network (NN) with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the NN and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices.
Utilizing parallelism in smart contracts on decentralized blockchains by taming application-inherent conflicts

Traditional public blockchain systems typically had very limited transaction throughput because of the bottleneck of the consensus protocol itself. With recent advances in consensus technology, the performance limit has been greatly lifted, typically to thousands of transactions per second. With this, transaction execution has become a new performance bottleneck. Exploiting parallelism in transaction execution is a clear and direct way to address this and to further increase transaction throughput. Although some recent literature introduced concurrency control mechanisms to execute smart contract transactions in parallel, the reported speedup that they can achieve is far from ideal. The main reason is that the proposed parallel execution mechanisms cannot effectively deal with the conflicts inherent in many blockchain applications.
Beyond automatic medical image segmentation—the spectrum between fully manual and fully automatic delineation

Abstract Semi-automatic and fully automatic contouring tools have emerged as an alternative to fully manual segmentation to reduce time spent contouring and to increase contour quality and consistency. Particularly, fully automatic segmentation has seen exceptional improvements through the use of deep learning in recent years. These fully automatic methods may not require user interactions, but the resulting contours are often not suitable to be used in clinical practice without a review by the clinician. Furthermore, they need large amounts of labelled data to be available for training. This review presents alternatives to manual or fully automatic segmentation methods along the spectrum of variable user interactivity and data availability. The challenge lies to determine how much user interaction is necessary and how this user interaction can be used most effectively. While deep learning is already widely used for fully automatic tools, interactive methods are just at the starting point to be transformed by it. Interaction between clinician and machine, via artificial intelligence, can go both ways and this review will present the avenues that are being pursued to improve medical image segmentation.
Digital Competence, Role Stress and Engagement: Towards positive mental health in Latin American teachers

The appearance of COVID-19 generated a new dynamic in the roles of the university teacher, who has had to rethink their pedagogical practice based on a hybrid education, assuming the commitment of the virtualization of formative processes; This new scenario demanded a deployment of their digital skills that required a readjustment to the work environment in exposure to stressors and psychosocial risk factors that may be affecting the health of the teacher. Therefore, the objective of this study is found in the evaluation of role stress (ambiguity, conflict and overload), engagement (vigor, dedication and absorption) and digital competences, of 300 teachers in six Latin American countries: Peru, Colombia, Chile, Brazil, Mexico and Ecuador. An applied research of a socio-critical paradigm and a mixed approach was developed, with a non-experimental, cross-sectional design, with a correlational and explanatory scope. Three questionnaires (processed through SPSS software) and one open question (processed through Atlas.ti software) were administered. The results indicate a medium level of digital skills, a high level of engagement and a medium level of role stress. Between engagement and role stress, a significant and negative relationship was found, and the statistical model allowed to verify that the higher the level of role stress, the lower the commitment, which is an alert. In turn, a significant positive relationship was found between engagement and digital skills, and between role stress and digital skills, no statistically significant relationship was found. The behavior of the three variables is similar in public and private universities, however, digital skills and engagement are different in men and women, although role stress is very similar between them. The qualitative question was allowed to systematize groups of codes that were grouped into three families (emerging categories), these are: promotion of teaching learning communities, ICT-based strategies for formative evaluation in virtual environments, and positive mental health self-management, This third factor being a dynamic element with a high preponderance in the teachers’ discourse, which is revealed as a real and felt need, and allows us to rethink psychosocial intervention alternatives for the post-pandemic period.
Sharded Blockchain-based Online Diagnostic System for Suspected Patients During Pandemics

During pandemics, diagnostic tests are essential to provide quick treatment of patients and limit the disease spread. The high demand for testing resources can stress the healthcare system. Thus, a remote collection of symptoms and reporting the results via an automated diagnostic system is highly desirable. However, such a system is challenged by privacy and scalability issues. Hence, we propose a sharded blockchain-based system that (a) introduces a set of shards that distributes the testing load among a group of local nodes (LNs), hence, offering high scalability for country-wide adoption, (b) uses ring signatures and unique random identifiers to ensure the anonymity of the users and the unlinkability of test requests, hence, supporting privacy-preservation, (c) deploys a detection strategy at the LNs based on deep neural networks, which is implemented on smart contracts, hence, enabling autonomous diagnosis, and (d) provides healthcare entities with authorized access to the symptoms and test results, hence, enabling efficient data sharing that supports future research. We provide an implementation of the proposed system and our experimental results demonstrate the high scalability and privacy of the system while achieving a testing accuracy up to 90%. We present a case study for U.S. wide deployment showing that a total daily test request of 2, 407, 462 can be performed and reported in 11 minutes compared to 63 days in absence of sharding. Moreover, sharding decreased the user storage requirement to be 0.18 MB at maximum instead of 723 MB without sharding.
Analysis of critical success factors for blockchain technology implementation in healthcare sector

Purpose Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare. Design/methodology/approach The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings. Findings Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential. Originality/value This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.
Underwater Acoustic Communication Channel Modeling Using Reservoir Computing

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of real-world measurement data. In this work, the capability of reservoir computing and deep learning has been explored for modeling the UWA communication channel accurately using real underwater data collected from a water tank with disturbance and from Lake Tahoe. We leverage the capability of reservoir computing for modeling dynamical systems and provided a data-driven approach to modeling the UWA channel using Echo State Network (ESN). In addition, the potential application of transfer learning to reservoir computing has been examined. Experimental results show that ESN is able to model chaotic UWA channels with better performance compared to popular deep learning models in terms of mean absolute percentage error (MAPE), specifically, ESN has outperformed deep neural network by 2% and as much as 40% in benign and chaotic UWA respectively.
Modeling cryptocurrency investment decision: evidence from Islamic emerging market

Purpose Digital currency investment has emerged as a result of global transformation toward technology-driven human lives. In Asia, Malaysia as an Islamic country is one of the early adopters with a high level of awareness on cryptocurrency. This paper aims to investigate the factors affecting the investment decision in cryptocurrency among potential investors in Malaysia. Design/methodology/approach Data was collected from 200 individuals aged 18 years and over. The hypotheses were tested using the partial least squares – structural equation modeling technique. Findings Results showed that attitude toward risk and perceived behavioral control have a significant positive effect on the investor’s investment decision in cryptocurrency. Interestingly, machine learning forecasting enhances the relationship between perceived benefits and the investment decision in cryptocurrency. Practical implications Results benefit investors and practitioners on the significant determinants of investment decision in cryptocurrency in emerging market. Originality/value Despite having high volatility and complexity in price determination, and being decentralized, cryptocurrency has managed to attract many investors due to reasons less explored. The outcome of this study extends the theory of planned behavior and confirms the role of machine learning forecasting as a moderator in the context of cryptocurrency investment.
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition

The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covariance Pooling (GCP) layer to learn powerful representations with second-order statistics, which can effectively model inter-class differences. In our previous conference paper, we show that truncating small eigenvalues of the GCP covariance can attain smoother gradient and improve the performance on large-scale benchmarks. However, on fine-grained datasets, truncating the small eigenvalues would make the model fail to converge. This observation contradicts the common assumption that the small eigenvalues merely correspond to the noisy and unimportant information. Consequently, ignoring them should have little influence on the performance. To diagnose this peculiar behavior, we propose two attribution methods whose visualizations demonstrate that the seemingly unimportant small eigenvalues are crucial as they are in charge of extracting the discriminative class-specific features. Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues. Without introducing any additional parameters, this branch simply amplifies the small eigenvalues and achieves state-of-the-art performances of GCP methods on three fine-grained benchmarks. Furthermore, the performance is also competitive against other FGVC approaches on larger datasets. Code is available at https://github.com/KingJamesSong/DifferentiableSVD.
Effects of laser-annealing on fixed-frequency superconducting qubits

As superconducting quantum processors increase in complexity, techniques to overcome constraints on frequency crowding are needed. The recently developed method of laser-annealing provides an effective post-fabrication method to adjust the frequency of superconducting qubits. Here, we present an automated laser-annealing apparatus based on conventional microscopy components and demonstrate preservation of highly coherent transmons. In one case, we observe a two-fold increase in coherence after laser-annealing and perform noise spectroscopy on this qubit to investigate the change in defect features, in particular two-level system defects. Finally, we present a local heating model as well as demonstrate aging stability for laser-annealing on the wafer scale. Our work constitutes an important first step towards both understanding the underlying physical mechanism and scaling up laser-annealing of superconducting qubits.
Subjective and Objective Quality of Experience of Free Viewpoint Videos

Free viewpoint videos (FVVs) provide immersive experiences for end-users, and they have been applied in many applications, such as movies, sports, and TV shows. However, the development of quantifying the quality of experience (QoE) of FVVs is still relatively slow due to the high costs of data collection and limited public databases. In this paper, we conduct a comprehensive study on FVV QoE. First, we construct the largest, to the best of our knowledge, FVV QoE database called Youku-FVV from two complex real scenarios, i.e., entertainment and sports. Specifically, Youku-FVV originates from the videos captured by dozens of real cameras arranged annularly. We use these videos to generate virtual viewpoints, which make up FVVs together with real views. In constructing the FVV QoE database, we consider both internal and external influencing factors of QoE, which correspond to FVV generation and playback, respectively. Besides, we make an initial attempt to train an efficient no reference FVV QoE prediction model using this database, where several sparse frame sampling strategies are validated. And we demonstrate the feasibility of striving for the balance between effectiveness and efficiency of FVV QoE prediction. The proposed FVV QoE database and source codes are publicly available at https://github.com/QTJiebin/FVV_QoE.
Modeling innovation in the cryptocurrency ecosystem

Blockchains are among the most relevant emerging technologies of recent times and, according to many, they will have a central role in shaping the future of our society. Since the introduction of Bitcoin in 2009, the first notorious blockchain system bound to a cryptocurrency, the blockchain ecosystem has experienced a huge growth, driven by innovations both in conceptual and algorithmic terms, and in the creation of a large number of new cryptocoins. New blockchains and their associated cryptocoins, emerge mostly as the result of forking already existing projects. Here, we show that the appearance of new cryptocoins can be well described by a sub-linear power-law (Heaps' law) of the total crypto-market capitalization. At the same time, we propose a model that well reproduces the evolution of the cryptocurrency ecosystem. Our model suggests that each cryptocurrency triggers, on average, the creation of ca. 1.58 novel cryptocoins, a result confirmed by the analysis of the Bitcoin historical forking tree. Moreover, we deduce that the largest cryptocurrency, nowadays Bitcoin, will comprise around the 50% of the whole crypto-market and that this fraction is going to stabilize in the near future, provided that the present fundamental macro-economic conditions do not change radically.
Reminiscence of Classical Chaos in Driven Transmons

Transmon qubits are ubiquitously used in superconducting quantum information processor architectures. Strong drives are required to realize fast, high-fidelity, gates and measurements, including parametrically activated processes. Here, we show that even off-resonant drives, in regimes routinely used in experiments, can cause strong modifications to the structure of the transmon spectrum rendering a large part of it chaotic. Accounting for the full nonlinear dynamics of the transmon in a Floquet-Markov formalism, we find that these chaotic states, often neglected through the hypothesis that the anharmonicity is weak, strongly impact the lifetime of the transmon’s computational states. In particular, we observe that chaos-assisted quantum phase slips greatly enhance band dispersions. In the presence of a measurement resonator, we find that approaching chaotic behavior correlates with strong transmon-resonator hybridization, and an average resonator response centered on the bare resonator frequency. These results lead to a photon-number threshold characterizing the appearance of chaos-induced quantum demolition effects during strong-drive operations, such as dispersive qubit readout. The phenomena described here are expected to be present in all circuits based on low-impedance Josephson junctions.13 MoreReceived 9 November 2022Accepted 21 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020312Published 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 AreasQuantum chaosPhysical SystemsSuperconducting qubitsQuantum InformationNonlinear DynamicsCondensed Matter, Materials & Applied Physics
AGP-based unitary coupled cluster theory for quantum computers

Electronic structure methods typically benefit from symmetry breaking and restoration, specially in the strong correlation regime. The same goes for Ans\"atze on a quantum computer. We develop a unitary coupled cluster method on the antisymmetrized geminal power (AGP) -- a state formally equivalent to the number-projected Bardeen--Cooper--Schrieffer wavefunction. We demonstrate our method for the single-band Fermi--Hubbard Hamiltonian in one and two dimensions. We also explore post-selection as a state preparation step to obtain correlated AGP and prove that it scales no worse than $\mathcal{O}(\sqrt{M})$ in the number of measurements, thereby making it a less expensive alternative to gauge integration to restore particle number symmetry.