Reviewerly/exHarmony huggingface dataset:
A Drift Region-Based Data Sample Filtering Method

Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called drift understanding, has rarely been studied in this area. To fill this gap, this article develops a drift region-based data sample filtering method to update the obsolete model and track the new data pattern accurately. The proposed method can effectively identify the drift region and utilize information on the drift region to filter the data sample for training models. The theoretical proof guarantees the identified drift region converges uniformly to the real drift region as the sample size increases. Experimental evaluations based on four synthetic datasets and two real-world datasets demonstrate our method improves the learning accuracy when dealing with data streams involving concept drift.
Neural ODE Machine Learning Method with Embedded Numerical Method

In the study, the authors examine the application of the Neural ODE machine learning method to reduce the number of neural network layers in order to minimize the problem of fading gradients. It is proposed to implement in Neural ODE a new scheme of the numerical Runge-Kutta method developed by the authors. The mathematical substantiation of the proposed numerical method, which guarantees the accuracy of the third order, is presented in detail. A comparative analysis of the training and computation time for Neural ODE with other machine learning methods using a computational experiment. The results of the experiment show that the proposed modification of the Neural ODE can reduce the computation time and the network training time.
Noise-robust exploration of many-body quantum states on near-term quantum devices

Abstract We describe a resource-efficient approach to studying many-body quantum states on noisy, intermediate-scale quantum devices. We employ a sequential generation model that allows us to bound the range of correlations in the resulting many-body quantum states. From this, we characterize situations where the estimation of local observables does not require the preparation of the entire state. Instead smaller patches of the state can be generated from which the observables can be estimated. This can potentially reduce circuit size and number of qubits for the computation of physical properties of the states. Moreover, we show that the effect of noise decreases along the computation. Our results apply to a broad class of widely studied tensor network states and can be directly applied to near-term implementations of variational quantum algorithms.
Bayesian Optimization of Bose-Einstein Condensates

Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained grid-based simulations. In this paper, we investigate data-driven modelling of Bose-Einstein Condensates (BECs). In particular, we use Gaussian Processes (GPs) to model the ground state wave function of BECs as a function of scattering parameters from the dimensionless Gross Pitaveskii Equation (GPE). Experimental results illustrate the ability of GPs to accurately reproduce ground state wave functions using a limited number of data points from simulations. Consistent performance across different configurations of BECs, namely Scalar and Vectorial BECs generated under different potentials, including harmonic, double well and optical lattice potentials pronounces the versatility of our method. Comparison with existing data-driven models indicates that our model achieves similar accuracy with only a small fraction 1/50th of data points used by existing methods, in addition to modelling uncertainty from data. When used as a simulator post-training, our model generates ground state wave functions $36 \times $ faster than Trotter Suzuki, a numerical approximation technique that uses Imaginary time evolution. Our method is quite general; with minor changes it can be applied to similar quantum many-body problems.
Constructing a ball of separable and absolutely separable states for $$2\otimes d$$ quantum system

Absolute separable states is a kind of separable state that remain separable under the action of any global unitary transformation. These states may or may not have quantum correlation and these correlations can be measured by quantum discord. We find that the absolute separable states are useful in quantum computation even if it contains infinitesimal quantum correlation in it. Thus to search for the class of two-qubit absolute separable states with zero discord, we have derived an upper bound for $Tr(\varrho^{2})$, where $\varrho$ denoting all zero discord states. In general, the upper bound depends on the state under consideration but if the state belong to some particular class of zero discord states then we found that the upper bound is state independent. Later, it is shown that among these particular classes of zero discord states, there exist sub-classes which are absolutely separable. Furthermore, we have derived necessary conditions for the separability of a given qubit-qudit states. Then we used the derived conditions to construct a ball for $2\otimes d$ quantum system described by $Tr(\rho^{2})\leq Tr(X^{2})+2Tr(XZ)+Tr(Z^{2})$, where the $2\otimes d$ quantum system is described by the density operator $\rho$ which can be expressed by block matrices $X,Y$ and $Z$ with $X,Z\geq 0$. In particular, for qubit-qubit system, we show that the newly constructed ball contain larger class of absolute separable states compared to the ball described by $Tr(\rho^{2})\leq \frac{1}{3}$. Lastly, we have derived the necessary condition in terms of purity for the absolute separability of a qubit-qudit system under investigation.
Islamophobes are not all the same! A study of far right actors on Twitter

Far-right actors are often purveyors of Islamophobic hate speech online, using social media to spread divisive and prejudiced messages which can stir up intergroup tensions and conflict. Hateful content can inflict harm on targeted victims, create a sense of fear amongst communities and stir up intergroup tensions and conflict. Accordingly, there is a pressing need to better understand at a granular level how Islamophobia manifests online and who produces it. We investigate the dynamics of Islamophobia amongst followers of a prominent UK far right political party on Twitter, the British National Party. Analysing a new data set of five million tweets, collected over a period of one year, using a machine learning classifier and latent Markov modelling, we identify seven types of Islamophobic far right actors, capturing qualitative, quantitative and temporal differences in their behaviour. Notably, we show that a small number of users are responsible for most of the Islamophobia that we observe. We then discuss the policy implications of this typology in the context of social media regulation.
Studying Catastrophic Forgetting in Neural Ranking Models

Several deep neural ranking models have been proposed in the recent IR literature. While their transferability to one target domain held by a dataset has been widely addressed using traditional domain adaptation strategies, the question of their cross-domain transferability is still under-studied. We study here in what extent neural ranking models catastrophically forget old knowledge acquired from previously observed domains after acquiring new knowledge, leading to performance decrease on those domains. Our experiments show that the effectiveness of neural IR ranking models is achieved at the cost of catastrophic forgetting and that a lifelong learning strategy using a cross-domain regularizer successfully mitigates the problem. Using an explanatory approach built on a regression model, we also show the effect of domain characteristics on the rise of catastrophic forgetting. We believe that the obtained results can be useful for both theoretical and practical future work in neural IR.
Human-Inspired Multi-Agent Navigation using Knowledge Distillation

Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this paper, we propose a framework for learning a human-like general collision avoidance policy for agent-agent interactions in fully decentralized, multi-agent environments. Our approach uses knowledge distillation with reinforcement learning to shape the reward function based on expert policies extracted from human trajectory demonstrations through behavior cloning. We show that agents trained with our approach can take human-like trajectories in collision avoidance and goal-directed steering tasks not provided by the demonstrations, outperforming the experts as well as learning-based agents trained without knowledge distillation.
On The Radon--Nikodym Spectral Approach With Optimal Clustering

Problems of interpolation, classification, and clustering are considered. In the tenets of Radon--Nikodym approach $\langle f(\mathbf{x})\psi^2 \rangle / \langle\psi^2\rangle$, where the $\psi(\mathbf{x})$ is a linear function on input attributes, all the answers are obtained from a generalized eigenproblem $|f|\psi^{[i]}\rangle = \lambda^{[i]} |\psi^{[i]}\rangle$. The solution to the interpolation problem is a regular Radon-Nikodym derivative. The solution to the classification problem requires prior and posterior probabilities that are obtained using the Lebesgue quadrature[1] technique. Whereas in a Bayesian approach new observations change only outcome probabilities, in the Radon-Nikodym approach not only outcome probabilities but also the probability space $|\psi^{[i]}\rangle$ change with new observations. This is a remarkable feature of the approach: both the probabilities and the probability space are constructed from the data. The Lebesgue quadrature technique can be also applied to the optimal clustering problem. The problem is solved by constructing a Gaussian quadrature on the Lebesgue measure. A distinguishing feature of the Radon-Nikodym approach is the knowledge of the invariant group: all the answers are invariant relatively any non-degenerated linear transform of input vector $\mathbf{x}$ components. A software product implementing the algorithms of interpolation, classification, and optimal clustering is available from the authors.
Term-Community-Based Topic Detection with Variable Resolution

Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.
Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders

The mechanical contraction of the pumping heart is driven by electrical excitation waves running across the heart muscle due to the excitable electrophysiology of heart cells. With cardiac arrhythmias these waves turn into stable or chaotic spiral waves (also called rotors) whose observation in the heart is very challenging. While mechanical motion can be measured in 3D using ultrasound, electrical activity can (so far) not be measured directly within the muscle and with limited resolution on the heart surface, only. To bridge the gap between measurable and not measurable quantities we use two approaches from machine learning, echo state networks and convolutional autoencoders, to solve two relevant data modelling tasks in cardiac dynamics: Recovering excitation patterns from noisy, blurred or undersampled observations and reconstructing complex electrical excitation waves from mechanical deformation. For the synthetic data sets used to evaluate both methods we obtained satisfying solutions with echo state networks and good results with convolutional autoencoders, both clearly indicating that the data reconstruction tasks can in principle be solved by means of machine learning.
Transmon in a semi-infinite high-impedance transmission line: Appearance of cavity modes and Rabi oscillations

In this paper, we investigate the dynamics of a single superconducting artificial atom capacitively coupled to a transmission line with a characteristic impedance comparable to or larger than the quantum resistance. In this regime, microwaves are reflected from the atom also at frequencies far from the atom's transition frequency. Adding a single mirror in the transmission line then creates cavity modes between the atom and the mirror. Investigating the spontaneous emission from the atom, we then find Rabi oscillations, where the energy oscillates between the atom and one of the cavity modes.Received 8 December 2020Accepted 11 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023003Published 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 AreasLight-matter interactionQuantum opticsSpontaneous emissionAtomic, Molecular & OpticalCondensed Matter, Materials & Applied PhysicsQuantum Information
Anchor pruning for object detection

This paper proposes anchor pruning for object detection in one-stage anchor-based detectors. While pruning techniques are widely used to reduce the computational cost of convolutional neural networks, they tend to focus on optimizing the backbone networks where often most computations are. In this work we demonstrate an additional pruning technique, specifically for object detection: anchor pruning. With more efficient backbone networks and a growing trend of deploying object detectors on embedded systems where post-processing steps such as non-maximum suppression can be a bottleneck, the impact of the anchors used in the detection head is becoming increasingly more important. In this work, we show that many anchors in the object detection head can be removed without any loss in accuracy. With additional retraining, anchor pruning can even lead to improved accuracy. Extensive experiments on SSD and MS COCO show that the detection head can be made up to 44% more efficient while simultaneously increasing accuracy. Further experiments on RetinaNet and PASCAL VOC show the general effectiveness of our approach. We also introduce ‘overanchorized’ models that can be used together with anchor pruning to eliminate hyperparameters related to the initial shape of anchors. Code and models are available at .
Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic

This paper describes our approach to detecting Sentiment and Sarcasm for Arabic in the ArSarcasm 2021 shared task. Data preprocessing is a crucial task for a successful learning, that is why we applied a set of preprocessing steps to the dataset before training two classifiers, namely Linear Support Vector Classifier (LSVC) and Bidirectional Long Short Term Memory (BiLSTM). The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33.71% and 57.80% for sarcasm and sentiment detection, respectively.
Spatial|Data Justice: Mapping and Digitised Strolling against Moral Police in Iran

Through a case study of women’s resistance against the moral police in Iran, this paper contends that claims to data justice cannot be investigated unless they are situated in broader political frames. Whilst the current literature uses a single axis analysis of data justice as well as conceptual tools that are appropriated for democratic power relations, this research positions data justice in a matrix of injustices in an unequal and undemocratic political apparatus.The paper scrutinises the intersection of data and spatial injustice in Iran by analysing the way traffic camera footage is used against female drivers with improper veiling. Considering compulsory hijab and policing of it as a spatial injustice that limits and disturbs women’s access to public places, the case study examines ways of resistance that address spatial|data injustice: firstly, a mobile phone application called Gershad that uses collective mapping to pin moral police patrols on maps by users; secondly, a social media campaign called White Wednesdays that encourages women to film and share their public strolls without hijab, their confrontations with religious pro-regime people, and videos of singing, dancing and cycling in public spaces.Using Fraser’s theory of “abnormal justice,” this research draws attention to particularities of each case of data justice; taking into account the intersections of socio-political axes of injustice in different layers of local, regional and global analysis. The paper offers a “situated” analytical framework by bringing in space as an inquisitive component and moves from a sole discussion of data justice to a more intersectional study of spatial and data justice combined. Participation in “small data” projects is introduced as one resistance strategy against injustices of big data systems, fulfilling the principle of “parity of participation” to achieve justice, especially in undemocratic political contexts.
Tight finite-key analysis for generalized high-dimensional quantum key distribution

Due to the capability of tolerating high error rate and generating more key bits per trial, high-dimensional quantum key distribution attracts wide interest. Despite great progresses in high-dimensional quantum key distribution, there are still some gaps between theory and experiment. One of these is that the security of the secret key heavily depends on the number of the emitted signals. So far, the existing security proofs are only suitable in the case with an infinite or unpractically large number of emitted signals. Here, by introducing the idea of "key classification" and developing relevant techniques based on the uncertainty relation for smooth entropies, we propose a tight finite-key analysis suitable for generalized high-dimensional quantum key distribution protocols. Benefitting from our theory, high-dimensional quantum key distribution protocols with finite resources become experimentally feasible.
IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and English

This paper describes the IIITK’s team submissions to the hope speech detection for equality, diversity and inclusion in Dravidian languages shared task organized by LT-EDI 2021 workshop@EACL 2021. Our best configurations for the shared tasks achieve weighted F1 scores of 0.60 for Tamil, 0.83 for Malayalam, and 0.93 for English. We have secured ranks of 4, 3, 2 in Tamil, Malayalam and English respectively.
Task Scheduling Strategy for Heterogeneous Multicore Systems

For heterogeneous computing systems, various types of processor cores cause system performance degradation due to uneven load. In addition, the inability of multitasking to match the appropriate processor core is also an urgent problem. This article proposes a swarm intelligence task scheduling strategy based on the genetic algorithm (GA) for high-performance heterogeneous multicore processors. In order to avoid the falling into local optimal solutions, we employ an adaptive mutation and injection strategy in the algorithm design. This swarm intelligence solution detects the computing capacities of different cores by processing specified tasks beforehand, and then an appropriate solution will be explored by introducing an adaptive mutation GA. Our technique aims to execute various types of tasks on heterogeneous processing cores for optimal performance. Experimental results show that this scheduling strategy can reduce the additional overhead and improve parallel computing efficiency and system performance.
Experimental demonstration of a quantum controlled-SWAP gate with multiple degrees of freedom of a single photon

Optimizing the physical realization of quantum gates is important to build a quantum computer. The controlled-SWAP gate, also named Fredkin gate, can be widely applicable in various quantum information processing schemes. In the present research, we propose and experimentally implement quantum Fredkin gate in a single-photon hybrid-degrees-of-freedom system. Polarization is used as the control qubit, and SWAP operation is achieved in a four-dimensional Hilbert space spanned by photonic orbital angular momentum. The effective conversion rate $\mathcal{P}$ of the quantum Fredkin gate in our experiment is $(95.4\pm 2.6)\%$. Besides, we find that a kind of Greenberger-Horne-Zeilinger-like states can be prepared by using our quantum Fredkin gate, and these nonseparale states can show its quantum contextual characteristic by the violation of Mermin inequality. Our experimental design and coding method are useful for quantum computing and quantum fundamental study in high-dimensional and hybrid coding quantum systems.
Eco-Digital Supply Chains Through Blockchains

This paper shows how the implementation of Eco-digital Supply Chain can be realized through the adoption of a Blockchain technology. The focal company pushes suppliers at all tiers through the adoption of green practices for reducing the CO2 emissions and the related costs. Furthermore, logistics costs involved in the Supply Chain make the final assessment highly challenging. We identify the changes in both the Supply Chain structure and performance occurring when the Blockchain enables the creation of an Eco-digital Supply Chain.
Memory effects and quantum speedup for a quantum emitter near a molybdenum disulfide nanodisk

We study memory effects (non-Markovian evolution) in the spontaneous emission dynamics of a quantum emitter near a molybdenum disulfide nanodisk by going beyond the analysis of the reversible population dynamics of the excited state and considering the dynamics of the non-Markovian open system. Specifically, we quantify the non-Markovian spontaneous emission dynamics using different non-Markovianity measures and also investigate the potential quantum speed up of the dynamics in relation to its non-Markovian features. We find that the values of the non-Markovianity measures and of the potential quantum speed up are large under strong coupling conditions, while in both cases the non-Markovianity measures and the quantum speed up decreases as the coupling between the emitter and the nanodisk becomes weaker. A comparison of the different non-Markovianity measures is also presented for the system under study. • Memory effects in the photon emission of a quantum emitter next to a MoS 2 nanodisk are analyzed. • The system is treated as a non-Markovian open quantum system . • For obtaining the results, we use a combination of quantum dynamics calculations with electromagnetic calculations. • Results for different non-Markovianity measures are presented. • The quantum speed limit of the quantum emitter next to the MoS 2 nanodisk is also calculated.