Reviewerly/exHarmony huggingface dataset:
Multi-Objective Interpolation Training for Robustness to Label Noise

Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robust classification loss functions. Conversely, we propose a Multi-Objective Interpolation Training (MOIT) approach that jointly exploits contrastive learning and classification to mutually help each other and boost performance against label noise. We show that standard supervised contrastive learning degrades in the presence of label noise and propose an interpolation training strategy to mitigate this behavior. We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples. This detection allows treating noisy samples as unlabeled and training a classifier in a semi-supervised manner to prevent noise memorization and improve representation learning. We further propose MOIT+, a refinement of MOIT by fine-tuning on detected clean samples. Hyperparameter and ablation studies verify the key components of our method. Experiments on synthetic and real-world noise benchmarks demonstrate that MOIT/MOIT+ achieves state-of-the-art results. Code is available at https://git.io/JI40X.
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly according to the mixture proportion of image pixels. Due to the major discriminative information of a fine-grained image usually resides in subtle regions, these methods tend to introduce heavy label noise in fine-grained recognition. We propose Semantically Proportional Mixing (SnapMix) that exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. SnapMix generates the target label for a mixed image by estimating its intrinsic semantic composition. This strategy can adapt to asymmetric mixing operations and ensure semantic correspondence between synthetic images and target labels. Experiments show that our method consistently outperforms existing mixed-based approaches regardless of different datasets or network depths. Further, by incorporating the mid-level features, the proposed SnapMix achieves top-level performance, demonstrating its potential to serve as a strong baseline for fine-grained recognition.
Short-Term Photovoltaic Power Forecasting Based on VMD and ISSA-GRU

Photovoltaic (PV) power generation is affected by many meteorological factors and environmental factors, which has obvious intermittent, random, and volatile characteristics. To improve the accuracy of short-term PV power prediction, a hybrid model (VMD-ISSA-GRU) based on variational mode decomposition (VMD), improved sparrow search algorithm (ISSA) and gated recurrent unit (GRU) is proposed. First of all, the PV time series is decomposed into a series of different subsequences by VMD to reduce the non-stationarity of the original data. Then, the main factors affecting PV power generation are obtained by using the correlation coefficients of Spearman and Pearson, which reduces the computational complexity of the model. Finally, the GRU network optimized by ISSA is used to predict all the subsequences and residual error of VMD, and the prediction results are reconstructed. The results show that the hybrid VMD-ISSA-GRU model has stronger adaptability and higher accuracy than other traditional models. The mean absolute error (MAE) in the whole test set is 1.0128 kW, the root mean square error (RMSE) is 1.5511 kW, and the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adj</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can reach 0.9993.
Experimental feasibility of molecular two-photon absorption with isolated time-frequency-entangled photon pairs

Entangled photon pairs have promised to deliver a substantial quantum advantage for two-photon absorption spectroscopy. However, recent work has challenged the previously reported magnitude of quantum enhancement in two-photon absorption. We present a measurement of molecular absorption driven by isolated photon pairs using sum-frequency generation in a nonlinear optical crystal as a calibration device. We thereby establish an upper bound on the enhancement for entangled two-photon absorption in Rhodamine 6G, which lies well below previously reported values.
Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Abstract Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual sesimic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. We present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation‐Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid‐search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.
A Side-Channel Attack on a Masked IND-CCA Secure Saber KEM Implementation

In this paper, we present a side-channel attack on a first-order masked implementation of IND-CCA secure Saber KEM. We show how to recover both the session key and the long-term secret key from 24 traces using a deep neural network created at the profiling stage. The proposed message recovery approach learns a higher-order model directly, without explicitly extracting random masks at each execution. This eliminates the need for a fully controllable profiling device which is required in previous attacks on masked implementations of LWE/LWR-based PKEs/KEMs. We also present a new secret key recovery approach based on maps from error-correcting codes that can compensate for some errors in the recovered message. In addition, we discovered a previously unknown leakage point in the primitive for masked logical shifting on arithmetic shares.
The Impact of Process Quality and Quality Control on Organizational Competitiveness at 5-star hotels in Dubai

Hotel industry is seeing many challenges because of the market challenges due to perfect competition market. Among these challenges quality assurance and quality control has significant impact because these factors determine the competitiveness level of the organization. In this research study we explained the hotel industry in the light of process quality and quality control. Interview was arranged and data was collected for interpretation. Results stated that process quality and quality control has significant impact on attaining competitiveness in hotel industry. This study can contribute a lot not just in theoretical field but also in practical field where the hotels can evaluate their own performance and the lacking in their own strategies.
Optimal Resource Allocation of Cluster using Hybrid Grey Wolf and Cuckoo Search Algorithm in Cloud Computing

In the progress of the markets and enterprises, cloud computing is considered the novel technology that has a great well-known factor.In cloud computing, the main effort is the allocation of resources.Moreover, for the task, the optimal allocation of resources is that it allocates the optimal appropriate cluster resources to perform in contemplation for various parameters, like scalability, cost, time, availability, resource utilization, throughput, reliability, etc.For the allocation of resources, an optimization algorithm is presented in this paper for the cloud environment through the Hybrid Grey Wolf and Cuckoo Search Algorithm (Hybrid GW-CS).For cloud computing, the hybridization algorithm is on the basis of the resource allocation which accumulates the execution and run time; also it enhances the profits for the cloud provider.Finally, the proposed Hybrid GW-CS based allocation of resource algorithms are evaluated over the conventional PSO, GWO, and CS by exploiting the performance measures like CPU usage rate, profit, and memory usage rate.
Efficient Measure for the Expressivity of Variational Quantum Algorithms

The superiority of variational quantum algorithms (VQAs) such as quantum neural networks (QNNs) and variational quantum eigensolvers (VQEs) heavily depends on the expressivity of the employed Ansätze. Namely, a simple Ansatz is insufficient to capture the optimal solution, while an intricate Ansatz leads to the hardness of trainability. Despite its fundamental importance, an effective strategy of measuring the expressivity of VQAs remains largely unknown. Here, we exploit an advanced tool in statistical learning theory, i.e., covering number, to study the expressivity of VQAs. Particularly, we first exhibit how the expressivity of VQAs with an arbitrary Ansätze is upper bounded by the number of quantum gates and the measurement observable. We next explore the expressivity of VQAs on near-term quantum chips, where the system noise is considered. We observe an exponential decay of the expressivity with increasing circuit depth. We also utilize the achieved expressivity to analyze the generalization of QNNs and the accuracy of VQE. We numerically verify our theory employing VQAs with different levels of expressivity. Our Letter opens the avenue for quantitative understanding of the expressivity of VQAs.
Security and Privacy in Cloud Computing: Technical Review

Advances in the usage of information and communication technologies (ICT) has given rise to the popularity and success of cloud computing. Cloud computing offers advantages and opportunities for business users to migrate and leverage the scalability of the pay-as-you-go price model. However, outsourcing information and business applications to the cloud or a third party raises security and privacy concerns, which have become critical in adopting cloud implementation and services. Researchers and affected organisations have proposed different security approaches in the literature to tackle the present security flaws. The literature also provides an extensive review of security and privacy issues in cloud computing. Unfortunately, the works provided in the literature lack the flexibility in mitigating multiple threats without conflicting with cloud security objectives. The literature has further focused on only highlighting security and privacy issues without providing adequate technical approaches to mitigate such security and privacy threats. Conversely, studies that offer technical solutions to security threats have failed to explain how such security threats exist. This paper aims to introduce security and privacy issues that demand an adaptive solution approach without conflicting with existing or future cloud security. This paper reviews different works in the literature, taking into account its adaptiveness in mitigating against future reoccurring threats and showing how cloud security conflicts have invalidated their proposed models. The article further presents the security threats surrounding cloud computing from a user perspective using the STRIDE approach. Additionally, it provides an analysis of different inefficient solutions in the literature and offers recommendations in terms of implementing a secure, adaptive cloud environment.
Ytterbium Nuclear-Spin Qubits in an Optical Tweezer Array

We report on the realization of a fast, scalable, and high-fidelity qubit architecture, based on $^{171}$Yb atoms in an optical tweezer array. We demonstrate several attractive properties of this atom for its use as a building block of a quantum information processing platform. Its nuclear spin of 1/2 serves as a long-lived and coherent two-level system, while its rich, alkaline-earth-like electronic structure allows for low-entropy preparation, fast qubit control, and high-fidelity readout. We present a near-deterministic loading protocol, which allows us to fill a 10$\times$10 tweezer array with 92.73(8)% efficiency and a single tweezer with 96.0(1.4)% efficiency. In the future, this loading protocol will enable efficient and uniform loading of target arrays with high probability, an essential step in quantum simulation and information applications. Employing a robust optical approach, we perform submicrosecond qubit rotations and characterize their fidelity through randomized benchmarking, yielding 5.2(5)$\times 10^{-3}$ error per Clifford gate. For quantum memory applications, we measure the coherence of our qubits with $T_2^*$=3.7(4) s and $T_2$=7.9(4) s, many orders of magnitude longer than our qubit rotation pulses. We measure spin depolarization times on the order of tens of seconds and find that this can be increased to the 100 s scale through the application of a several-gauss magnetic field. Finally, we use 3D Raman-sideband cooling to bring the atoms near their motional ground state, which will be central to future implementations of two-qubit gates that benefit from low motional entropy.
An Adaptive Framework to Tune the Coordinate Systems in Nature-Inspired Optimization Algorithms

The performance of many nature-inspired optimization algorithms (NIOAs) depends strongly on their implemented coordinate system.However, the commonly used coordinate system is fixed and not well suited for different function landscapes, NIOAs thus might not search efficiently.To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems in NIOAs.In ACoS, an Eigen coordinate system is established by making use of the cumulative population distribution information, which can be obtained based on a covariance matrix adaptation strategy and an additional archiving mechanism.Since the population distribution information can reflect the features of the function landscape to some extent, NIOAs in the Eigen coordinate system have the capability to identify the modality of the function landscape.In addition, the Eigen coordinate system is coupled with the original coordinate system, and they are selected according to a probability vector.The probability vector aims to determine the selection ratio of each coordinate system for each individual, and is adaptively updated based on the collected information from the offspring.ACoS has been applied to two of the most popular paradigms of NIOAs, i.e., particle swarm optimization
A Generic Multi-Projection-Center Model and Calibration Method for Light Field Cameras

Light field cameras can capture both spatial and angular information of light rays, enabling 3D reconstruction by a single exposure. The geometry of 3D reconstruction is affected by intrinsic parameters of a light field camera significantly. In the paper, we propose a multi-projection-center (MPC) model with 6 intrinsic parameters to characterize light field cameras based on traditional two-parallel-plane (TPP) representation. The MPC model can generally parameterize light field in different imaging formations, including conventional and focused light field cameras. By the constraints of 4D ray and 3D geometry, a 3D projective transformation is deduced to describe the relationship between geometric structure and the MPC coordinates. Based on the MPC model and projective transformation, we propose a calibration algorithm to verify our light field camera model. Our calibration method includes a close-form solution and a non-linear optimization by minimizing re-projection errors. Experimental results on both simulated and real scene data have verified the performance of our algorithm.
SABRE: Protecting Bitcoin against Routing Attacks

Nowadays Internet routing attacks remain practically effective as existing countermeasures either fail to provide protection guarantees or are not easily deployable.Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communications to reach consensus.In particular, Bitcoin-the most widely-used cryptocurrency-can be split in half by any AS-level adversary using BGP hijacking.In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks.SABRE runs alongside the existing peer-to-peer network and is easily deployable.As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks.We built SABRE around two key technical insights.First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in networks that are inherently protected against routing attacks; and (ii) on paths that are economically-preferred by the majority of Bitcoin clients.These properties are generic and can be used to protect other Blockchain-based systems.Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy.This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language).Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients.We present a complete implementation of SABRE together with an extensive evaluation.Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments of as few as 6 nodes.
Engineering bilinear mode coupling in circuit QED: Theory and experiment

Photonic states of high-Q superconducting microwave cavities controlled by superconducting transmon ancillas provide a platform for encoding and manipulating quantum information. A key challenge in scaling up the platform towards practical quantum computation is the requirement to communicate on demand the quantum information stored in the cavities. It has been recently demonstrated that a tunable bilinear interaction between two cavity modes can be realized by coupling the modes to a bichromatically driven superconducting transmon ancilla, which allows swapping and interfering the multiphoton states stored in the cavity modes [Gao et al., Phys. Rev. X 8, 021073 (2018)]. Here we explore both theoretically and experimentally the regime of relatively strong drives on the ancilla needed to achieve fast swap gates but which can also lead to undesired nonperturbative effects that lower the swap fidelity. We develop a theoretical formalism based on linear response theory that allows one to calculate the rate of ancilla-induced interaction, decay, and frequency shift of the cavity modes in terms of a susceptibility matrix. We go beyond the usual perturbative treatment of the drives by using Floquet theory, and find that the interference of the two drives can strongly alter the system dynamics even in the regime where the standard rotating wave approximation applies. The drive-induced ac Stark shift on the ancilla depends nontrivially on the drive and ancilla parameters which in turn modify the strength of the engineered interaction. We identify two major sources of infidelity due to ancilla decoherence. (i) Ancilla dissipation and dephasing lead to incoherent hopping among Floquet states which occurs even when the ancilla is at zero temperature; this hopping results in a sudden change of the swap rate, thereby decohering the swap operation. (ii) The cavity modes inherit finite decay from the relatively lossy ancilla through the inverse Purcell effect; the effect becomes particularly strong when the ac Stark shift pushes certain ancilla transition frequencies to the vicinity of the cavity mode frequencies. The theoretical predictions agree quantitatively with the experimental results, paving the way for using the developed theory for optimizing future experiments and architecture designs.11 MoreReceived 23 August 2018DOI:https://doi.org/10.1103/PhysRevA.99.012314©2019 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information processing with continuous variablesQuantum InformationNonlinear DynamicsStatistical Physics
Cracking Bitcoin wallets: I want what you have in the wallets

Bitcoin is increasingly popular, which is partly evidenced by the significant increase in its value in recent years. This increase in popularity and value has led to malicious actors stealing, or attempting to steal, Bitcoin wallet private keys in order to gain ownership of the digital currency held in these wallets. To demonstrate this security threat, two Bitcoin wallet software are studied, namely: Multibit HD and Electrum. Each wallet is subject to a combination of a password exploits and a restore wallet exploit. Findings suggest that it is possible to gain access to Multibit HD and Electrum wallets through offline brute force password attempts. Once a password is found, it is possible to restore a wallet with all Bitcoins contained within. This restoration is possible even if access to the Bitcoin wallet has never occurred. These findings highlight the need for more research into Bitcoin wallet security and forensics.