Reviewerly/exHarmony huggingface dataset:
Correlated spectrum of distant semiconductor qubits coupled by microwave photons

We develop a new spectroscopic method to quickly and intuitively characterize the coupling of two microwave-photon-coupled semiconductor qubits via a high-impedance resonator. Highly distinctive and unique geometric patterns are revealed as we tune the qubit tunnel couplings relative to the frequency of the mediating photons. These patterns are in excellent agreement with a simulation using the Tavis-Cummings model, and allow us to readily identify different parameter regimes for both qubits in the detuning space. This method could potentially be an important component in the overall spectroscopic toolbox for quickly characterizing certain collective properties of multiple cavity quantum electrodynamics (QED) coupled qubits.
Improved and scalable online learning of spatial concepts and language models with mapping

Abstract We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.
Uncertainty and trade-offs in quantum multiparameter estimation

Uncertainty relations in quantum mechanics express bounds on our ability to simultaneously obtain knowledge about expectation values of non-commuting observables of a quantum system. They quantify trade-offs in accuracy between complementary pieces of information about the system. In Quantum multiparameter estimation, such trade-offs occur for the precision achievable for different parameters characterizing a density matrix: an uncertainty relation emerges between the achievable variances of the different estimators. This is in contrast to classical multiparameter estimation, where simultaneous optimal precision is attainable in the asymptotic limit. We study trade-off relations that follow from known tight bounds in quantum multiparameter estimation. We compute trade-off curves and surfaces from Cram\'er--Rao type bounds which provide a compelling graphical representation of the information encoded in such bounds, and argue that bounds on simultaneously achievable precision in quantum multiparameter estimation should be regarded as measurement uncertainty relations. From the state-dependent bounds on the expected cost in parameter estimation, we derive a state independent uncertainty relation between the parameters of a qubit system.
3D Skeletal Gesture Recognition via Hidden States Exploration

Temporal dynamics is an open issue for modeling human body gestures. A solution is resorting to the generative models, such as the hidden Markov model (HMM). Nevertheless, most of the work assumes fixed anchors for each hidden state, which make it hard to describe the explicit temporal structure of gestures. Based on the observation that a gesture is a time series with distinctly defined phases, we propose a new formulation to build temporal compositions of gestures by the low-rank matrix decomposition. The only assumption is that the gesture's "hold" phases with static poses are linearly correlated among each other. As such, a gesture sequence could be segmented into temporal states with semantically meaningful and discriminative concepts. Furthermore, different to traditional HMMs which tend to use specific distance metric for clustering and ignore the temporal contextual information when estimating the emission probability, we utilize the long short-term memory to learn probability distributions over states of HMM. The proposed method is validated on multiple challenging datasets. Experiments demonstrate that our approach can effectively work on a wide range of gestures, and achieve state-of-the-art performance.
Optimal design of error-tolerant reprogrammable multiport interferometers

Photonic information processing demands programmable multiport interferometers capable of implementing arbitrary transfer matrices, for which planar meshes of error-sensitive Mach–Zehnder interferometers are usually exploited. We propose an alternative design that uses a single-static beam splitter (BS) and a variable phase shift as the building blocks. The design possesses superior resilience to manufacturing errors and losses without extra elements added into the scheme. Namely, the power transmissivities of the static BSs can take arbitrary values in the range from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>≈<!-- ≈ --></mml:mo> </mml:mrow> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>≈<!-- ≈ --></mml:mo> </mml:mrow> <mml:mn>4</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>5</mml:mn> </mml:math> . In this Letter, we show that the fraction of transfer matrices non-implementable by the interferometers of our design diminishes rapidly with its size.
Updateable Inner Product Argument with Logarithmic Verifier and Applications

We propose an improvement for the inner product argument of Bootle et al. (EUROCRYPT'16). The new argument replaces the unstructured common reference string (the commitment key) by a structured one. We give two instantiations of this argument, for two different distributions of the CRS. In the designated verifier setting, this structure can be used to reduce verification from linear to logarithmic in the circuit size. The argument can be compiled to the publicly verifiable setting in asymmetric bilinear groups. The new common reference string can easily be updateable. The argument can be directly used to improve verification of Bulletproofs range proofs (IEEE SP'18). On the other hand, to use the improved argument to prove circuit satisfiability with logarithmic verification, we adapt recent techniques from Sonic (ACM CCS'19) to work with the new common reference string. The resulting argument is secure under standard assumptions (in the Random Oracle Model), in contrast with Sonic and recent works that improve its efficiency (Plonk, Marlin, AuroraLight), which, apart from the Random Oracle Model, need either the Algebraic Group Model or Knowledge Type assumptions.
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).
Deep multi-metric learning for text-independent speaker verification

Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled utterances originate from the same speaker or not. Extracting speech features for each speaker using deep neural networks is a promising direction to explore and a straightforward solution is to train the discriminative feature extraction network by using a metric learning loss function. However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss. The three loss functions work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on the large-scale VoxCeleb2 dataset, which contains over a million utterances from over 6,000 speakers, and the proposed deep neural network obtains an equal error rate of 3.48%, which is a very competitive result. Codes for both training and testing and pretrained models are available at https://github.com/GreatJiweix/DmmlTiSV, which is the first publicly available code repository for large-scale text-independent speaker verification with performance on par with the state-of-the-art systems.
Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets

In path planning problem of a mobile robot, the objective is to find an optimal collision free path from a source to single or multiple targets. Domain knowledge based genetic algorithms have been proposed to address the path planning problem having single as well as multiple independent targets. Four new domain knowledge based operators namely “circuit removal operator”, “insertion-deletion operator”, “refinement Operator”, and “target alignment operator” have been introduced in this work. Among these four operators, first three have been used for the path planning problem having single target, whereas all these four operators have been used for path planning problem having multiple independent targets. Proposed methods have been deployed on several simulated environments of varying sizes. From the experimental results, it has been established that the domain knowledge based operators enhance the capability of conventional genetic algorithm. Our proposed method for Mobile Robot’s Path Planning Problem having single target has outperformed some previously proposed evolutionary algorithm based methods.
Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models

The bias-variance trade-off is a central concept in supervised learning. In classical statistics, increasing the complexity of a model (e.g., number of parameters) reduces bias but also increases variance. Until recently, it was commonly believed that optimal performance is achieved at intermediate model complexities which strike a balance between bias and variance. Modern Deep Learning methods flout this dogma, achieving state-of-the-art performance using "over-parameterized models" where the number of fit parameters is large enough to perfectly fit the training data. As a result, understanding bias and variance in over-parameterized models has emerged as a fundamental problem in machine learning. Here, we use methods from statistical physics to derive analytic expressions for bias and variance in two minimal models of over-parameterization (linear regression and two-layer neural networks with nonlinear data distributions), allowing us to disentangle properties stemming from the model architecture and random sampling of data. In both models, increasing the number of fit parameters leads to a phase transition where the training error goes to zero and the test error diverges as a result of the variance (while the bias remains finite). Beyond this threshold, the test error of the two-layer neural network decreases due to a monotonic decrease in \emph{both} the bias and variance in contrast with the classical bias-variance trade-off. We also show that in contrast with classical intuition, over-parameterized models can overfit even in the absence of noise and exhibit bias even if the student and teacher models match. We synthesize these results to construct a holistic understanding of generalization error and the bias-variance trade-off in over-parameterized models and relate our results to random matrix theory.
Non-classical computing problems: Toward novel type of quantum computing problems

Quantum teleportation draws our attention to propose a new type of problems which can not be solved using classical computers. In this paper, we propose one of these problems. Concretely, this paper extends the definition of Deutsch's problem to decide whether a black box Uf applied on a given unknown qubit α|0〉+β|1〉, such that |α|>0,|β|>0,and|α|≠|β|, is constant or balanced Boolean function, besides, estimation of |α| and |β|. Although, this problem is very simple but it can not be solved using classical computers, because qubit can not be implemented physically using classical computers. A novel quantum algorithm based on principle of entanglement measure is proposed to solve this problem. IBMs 5-qubit quantum computer (ibmqx4) is used to realize the proposed algorithm experimentally.
Design of Anonymous Endorsement System in Hyperledger Fabric

Permissioned Blockchain has become quite popular with enterprises forming consortium since it prioritizes trust over privacy. One of the popular platforms for distributed ledger solution, <i>Hyperledger Fabric</i> , requires a transaction to be <i>endorsed</i> or approved by a group of special members known as endorsers before undergoing validation. To endorse a transaction, an endorser mentions its identity along with the signature so that it can be verified later. However, for certain transactions, difference in opinion may exist among endorsers. Disclosing the identity of an endorser may lead to conflict within the consortium. In such cases, an endorsement policy which not only allows an endorser to support a transaction discreetly, but at the same time takes into account the decision of the majority is preferred. Thus we propose an Anonymous Endorsement System which uses a threshold endorsement policy in order to address the issue. To realize a <i>t-out-of-n</i> endorsement policy, using any of the existing threshold ring signature for our endorsement system would have violated the privacy of endorsers as either the identity or the secret key of the endorsers get revealed to the party who recombines the signature after collecting each signature share. All these factors motivated us to design a new ring signature scheme, called <i>Fabric’s Constant-Sized Linkable Ring Signature</i> (FCsLRS) with <i>Transaction-Oriented</i> linkability for hiding identity of the endorsers. We have implemented the signature scheme in Golang and analyzed its security and performance by varying the Rivest-Shamir-Adleman (RSA) modulus size. Feasibility of implementation is supported by experimental analysis. Signature and tag generation time is quite fast and remains constant irrespective of change in message length or endorsement set size for a given RSA modulus value, assuming all the endorsers generates their signature in parallel. Each verifier is required to count and check individual valid ring signature. If the aggregate is above the threshold value, stated by the endorsement policy, then it confirms that the transaction is valid. This increases the verification time depending on the threshold value, but has very little effect on the scalability since generally <inline-formula><tex-math notation="LaTeX">$t&lt;\!\!\!&lt;n$</tex-math></inline-formula> . Lastly, we also discuss the integration of the scheme on v1.2 Hyperledger Fabric.
Phase-Matching Quantum Cryptographic Conferencing

Quantum cryptographic conferencing (QCC) holds promise for distributing information-theoretic secure keys among multiple users over a long distance. Limited by the fragility of Greenberger-Horne-Zeilinger (GHZ) states, QCC networks based on directly distributing GHZ states over a long distance still face a big challenge. Another two potential approaches are measurement device-independent QCC and conference-key agreement with single-photon interference, which were proposed on the basis of the postselection of GHZ states and the postselection of the W state, respectively. However, implementations of the former protocol are still heavily constrained by the transmission rate $\ensuremath{\eta}$ of optical channels and the complexity of the setups for postselecting GHZ states. Meanwhile, the latter protocol cannot be cast as a measurement device-independent prepare-and-measure scheme. Combining the idea of postselecting GHZ states and recently proposed twin-field quantum-key-distribution protocols, we report a QCC protocol based on weak coherent-state interferences named ``phase-matching quantum cryptographic conferencing,'' which is immune to all detector side-channel attacks. The proposed protocol can improve the key-generation rate from $O({\ensuremath{\eta}}^{N})$ to $O({\ensuremath{\eta}}^{N\ensuremath{-}1})$ compared with the measurement device-independent QCC protocols. Meanwhile, it can be easily scaled up to multiple parties due to its simple setup.
Quantum Data-Syndrome Codes

Performing active quantum error correction to protect fragile quantum states highly depends on the correctness of error information--error syndromes. To obtain reliable error syndromes using imperfect physical circuits, we propose the idea of quantum data-syndrome (DS) codes that are capable of correcting both data qubits and syndrome bits errors. We study fundamental properties of quantum DS codes, including split weight enumerators, generalized MacWilliams identities, and linear programming bounds. In particular, we derive Singleton and Hamming-type upper bounds on degenerate quantum DS codes. Then we study random DS codes and show that random DS codes with a relatively small additional syndrome measurements achieve the Gilbert-Varshamov bound of stabilizer codes. Constructions of quantum DS codes are also discussed. A family of quantum DS codes is based on classical linear block codes, called syndrome measurement codes, so that syndrome bits are encoded in additional redundant stabilizer measurements. Another family of quantum DS codes is CSS-type quantum DS codes based on classical cyclic codes, and this includes the Steane code and the quantum Golay code.
MOCCA: Multilayer One-Class Classification for Anomaly Detection

Anomalies are ubiquitous in all scientific fields and can express an unexpected event due to incomplete knowledge about the data distribution or an unknown process that suddenly comes into play and distorts the observations. Usually, due to such events' rarity, to train deep learning (DL) models on the anomaly detection (AD) task, scientists only rely on "normal" data, i.e., nonanomalous samples. Thus, letting the neural network infer the distribution beneath the input data. In such a context, we propose a novel framework, named multilayer one-class classification (MOCCA), to train and test DL models on the AD task. Specifically, we applied our approach to autoencoders. A key novelty in our work stems from the explicit optimization of the intermediate representations for the task at hand. Indeed, differently from commonly used approaches that consider a neural network as a single computational block, i.e., using the output of the last layer only, MOCCA explicitly leverages the multilayer structure of deep architectures. Each layer's feature space is optimized for AD during training, while in the test phase, the deep representations extracted from the trained layers are combined to detect anomalies. With MOCCA, we split the training process into two steps. First, the autoencoder is trained on the reconstruction task only. Then, we only retain the encoder tasked with minimizing the L2 distance between the output representation and a reference point, the anomaly-free training data centroid, at each considered layer. Subsequently, we combine the deep features extracted at the various trained layers of the encoder model to detect anomalies at inference time. To assess the performance of the models trained with MOCCA, we conduct extensive experiments on publicly available datasets, namely CIFAR10, MVTec AD, and ShanghaiTech. We show that our proposed method reaches comparable or superior performance to state-of-the-art approaches available in the literature. Finally, we provide a model analysis to give insights regarding the benefits of our training procedure.
Phase Asymmetry Ultrasound Despeckling With Fractional Anisotropic Diffusion and Total Variation

We propose an ultrasound speckle filtering method for not only preserving various edge features but also filtering tissue-dependent complex speckle noises in ultrasound images. The key idea is to detect these various edges using a phase congruence-based edge significance measure called phase asymmetry (PAS), which is invariant to the intensity amplitude of edges and takes 0 in non-edge smooth regions and 1 at the idea step edge, while also taking intermediate values at slowly varying ramp edges. By leveraging the PAS metric in designing weighting coefficients to maintain a balance between fractional-order anisotropic diffusion and total variation (TV) filters in TV cost function, we propose a new fractional TV framework to not only achieve the best despeckling performance with ramp edge preservation but also reduce the staircase effect produced by integral-order filters. Then, we exploit the PAS metric in designing a new fractional-order diffusion coefficient to properly preserve low-contrast edges in diffusion filtering. Finally, different from fixed fractional-order diffusion filters, an adaptive fractional order is introduced based on the PAS metric to enhance various weak edges in the spatially transitional areas between objects. The proposed fractional TV model is minimized using the gradient descent method to obtain the final denoised image. The experimental results and real application of ultrasound breast image segmentation show that the proposed method outperforms other state-of-the-art ultrasound despeckling filters for both speckle reduction and feature preservation in terms of visual evaluation and quantitative indices.