Reviewerly/exHarmony huggingface dataset:
Bangla hate speech detection on social media using attention-based recurrent neural network

Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder decoder based machine learning model, a popular tool in NLP, to classify user's Bengali comments on Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU based decoders have been used for predicting hate speech categories. Among the three encoder decoder algorithms, the attention-based decoder obtained the best accuracy (77%).
Classical variational simulation of the Quantum Approximate Optimization Algorithm

A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum systems is an important component of addressing this question. We introduce a method to simulate layered quantum circuits consisting of parametrized gates, an architecture behind many variational quantum algorithms suitable for near-term quantum computers. A neural-network parametrization of the many-qubit wave function is used, focusing on states relevant for the Quantum Approximate Optimization Algorithm (QAOA). For the largest circuits simulated, we reach 54 qubits at 4 QAOA layers, approximately implementing 324 RZZ gates and 216 RX gates without requiring large-scale computational resources. For larger systems, our approach can be used to provide accurate QAOA simulations at previously unexplored parameter values and to benchmark the next generation of experiments in the Noisy Intermediate-Scale Quantum (NISQ) era.
The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem

Deep learning (DL) has had unprecedented success and is now entering scientific computing with full force. However, current DL methods typically suffer from instability, even when universal approximation properties guarantee the existence of stable neural networks (NNs). We address this paradox by demonstrating basic well-conditioned problems in scientific computing where one can prove the existence of NNs with great approximation qualities; however, there does not exist any algorithm, even randomized, that can train (or compute) such a NN. For any positive integers K>2 and L, there are cases where simultaneously 1) no randomized training algorithm can compute a NN correct to K digits with probability greater than 1/2; 2) there exists a deterministic training algorithm that computes a NN with K –1 correct digits, but any such (even randomized) algorithm needs arbitrarily many training data; and 3) there exists a deterministic training algorithm that computes a NN with K –2 correct digits using no more than L training samples. These results imply a classification theory describing conditions under which (stable) NNs with a given accuracy can be computed by an algorithm. We begin this theory by establishing sufficient conditions for the existence of algorithms that compute stable NNs in inverse problems. We introduce fast iterative restarted networks (FIRENETs), which we both prove and numerically verify are stable. Moreover, we prove that only O(|log (ϵ)|) layers are needed for an ϵ-accurate solution to the inverse problem.
A context hierarchical integrated network for medical image segmentation

Arising from low contrast, high similarity, and different scales between diverse tissues in 2D medical images, it is challenging to accurately segment the regions of interest. To address these issues, we propose a context hierarchical integrated network named CHI-Net for medical image segmentation, which can accurately segment salient regions from medical images in a purely task-driven manner. The proposed CHI-Net consists of two key modules, i.e., a dense dilated convolution (DDC) and a stacked residual pooling (SRP). Specifically, The DDC module can capture substantial complementary features hierarchically by combining four cascaded branches of hybrid dilated convolutions, which is conducive to extracting the features of diverse scales. The SRP module integrates encoder detail features by multiple effective field-of-views, which aims at generating more discriminative features. Extensive experimental results on five benchmark datasets with different objects illustrate that the proposed CHI-Net is superior to the state-of-the-art object segmentation methods.
Doing (Bi)lingualism: Language alternation as performative construction of online identities

The purpose of this paper is to examine practices of language alternation in email communication among native speakers of Greek and to argue that such practices are a facet of the performative construction of an ‘online’ communicative identity. In the slowly-growing body of literature on linguistic practices in computer-mediated communication (CMC) or computer-mediated discourse (CMD) it is emerging that concomitant aspects of linguistic performance relate to the construction of particular sociolinguistic identities relevant to the medium, or, to adopt a less radical perspective, that sociolinguistic identities typical of face-to-face or written interaction are mediated by the social/communicative practices and norms relevant to, or accruing to, types of CMD. Language alternation features prominently among the mechanisms used in constructing such novel linguistic/social-performative identities. In this context, the research presented in this paper examines the performance of a group of six native speakers of Greek, who are also part of a relatively closely-knit social network. The analysis reveals extensive code-switching between Greek and English, both inter- and intra-sentential, with English covering around 20% of the total of words used. The qualitative analysis shows that expressions of affect and evaluative comments are mostly in English, while Greek is reserved for the transmission of factual/referential information. The data further reveal that extensive style- or register-shifting and mixing is a favored strategy among members of the group; such mixing includes shifting among dialects or sociolects of Greek, the use of other languages, and, notably, the use of constructed words and structures with humorous overtones. This complex type of language play is an overarching feature of the group’s (socio)linguistic performance in asynchronous electronic communication, which may single them out as a localized community of practice. The data highlight the theoretical and methodological necessity for fine-grained accounts of specific types of CMD, which can be tackled not in terms of overarching macro-contextual linguistic and extralinguistic variables, but as dynamic reflexes both of specific participant constellations and of the negotiation of emerging generic norms within localized communities of practice. The paper also presents and discusses a quantitative study of views and attitudes on language alternation expressed by subjects who code-switch systematically on email, in an attempt to gauge the types of metalinguistic awareness involved. It emerges from the quantitative study that users abstract away from ‘phobic’ attitudes towards the use of English and that they treat language alternation as a manifestation of balanced or functional bilingualism, which is furthermore situation-specific and ‘genre’-appropriate.
Generic Bound Coherence under Strictly Incoherent Operations

We compute analytically the maximal rates of distillation of quantum coherence under strictly incoherent operations (SIO) and physically incoherent operations (PIO), showing that they coincide for all states, and providing a complete description of the phenomenon of bound coherence. In particular, we establish a simple, analytically computable necessary and sufficient criterion for the asymptotic distillability under SIO and PIO. We use this result to show that almost every quantum state is undistillable --- only pure states as well as states whose density matrix contains a rank-one submatrix allow for coherence distillation under SIO or PIO, while every other quantum state exhibits bound coherence. This demonstrates fundamental operational limitations of SIO and PIO in the resource theory of quantum coherence. We show that the fidelity of distillation of a single bit of coherence under SIO can be efficiently computed as a semidefinite program, and investigate the generalization of this result to provide an understanding of asymptotically achievable distillation fidelity.
Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation

With the rapid political and economic developments, energy saving and carbon dioxide emission reduction are now recognized as the most important goals worldwide. Therefore, this paper presents a production capacity analysis and energy optimization model that uses novel extreme learning machine integrating affinity propagation clustering. By using the affinity propagation method, clusters of raw data can be obtained automatically to reduce multi-dimensional data and fuse the high-similarity data. Then, the clustering results are taken as the training and testing sets of the extreme learning machine to improve prediction accuracy and analyze production capacity. Through comparisons with the extreme learning machine, back propagation, radical basis function, and extreme learning machine integrated K-means clustering algorithm, the accuracy and validity of the proposed method are verified by using the University of California Irvine data sets. Finally, the proposed method is applied to build the production capacity analysis and energy optimization model of the ethylene and purified terephthalic acid production systems in complex petrochemical industries. The prediction accuracy of the ethylene and purified terephthalic acid production is approximately 99%, thus improving the energy efficiency of these complex petrochemical processes and achieving energy saving and carbon dioxide emission reduction.
A Uniform Architecture Design for Accelerating 2D and 3D CNNs on FPGAs

Three-dimensional convolutional neural networks (3D CNNs) have gained popularity in many complicated computer vision applications. Many customized accelerators based on FPGAs are proposed for 2D CNNs, while very few are for 3D CNNs. Three-D CNNs are far more computationally intensive and the design space for 3D CNN acceleration has been further expanded since one more dimension is introduced, making it a big challenge to accelerate 3D CNNs on FPGAs. Motivated by the finding that the computation patterns of 2D and 3D CNNs are very similar, we propose a uniform architecture design for accelerating both 2D and 3D CNNs in this paper. The uniform architecture is based on the idea of mapping convolutions to matrix multiplications. A customized mapping module is developed to generate the feature matrix tilings with no need to store the entire enlarged feature matrix on-chip or off-chip, a splitting strategy is adopted to reconstruct a convolutional layer to adapt to the on-chip memory capacity, and a 2D multiply-and-accumulate (MAC) array is adopted to compute matrix multiplications efficiently. For demonstration, we implement an accelerator prototype with a high-level synthesis (HLS) methodology on a Xilinx VC709 board and test the accelerator on three typical CNN models: AlexNet, VGG16, and C3D. Experimental results show that the accelerator achieves state-of-the-art throughput performance on both 2D and 3D CNNs, with much better energy efficiency than the CPU and GPU.
Optimal verification of two-qubit pure states

In a recent work [Phys. Rev. Lett. 120, 170502 (2018)], Pallister et al. proposed an optimal strategy to verify non-maximally entangled two-qubit pure states under the constraint that the accessible measurements being locally projective and non-adaptive. Their nice result leads naturally to the question: What is the optimal strategy among general LOCC measurements? In this Letter, we answer this problem completely for two-qubit pure states. To be specific, we give the optimal strategy for each of the following available classes of measurements: (i) local operations and one-way classical communication (one-way LOCC) measurements; (ii) local operations and two-way classical communication (two-way LOCC) measurements; and (iii) separable measurements. Surprisingly, our results reveal that for the two-qubit pure state verification problem, two-way LOCC measurements remarkably outperforms one-way LOCC measurements and has the same power as the separable measurements.
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Pouya Pezeshkpour, Yifan Tian, Sameer Singh. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A Multi-Pass GAN for Fluid Flow Super-Resolution

We propose a novel method to up-sample volumetric functions with generative neural networks using several orthogonal passes. Our method decomposes generative problems on Cartesian field functions into multiple smaller sub-problems that can be learned more efficiently. Specifically, we utilize two separate generative adversarial networks: the first one up-scales slices which are parallel to the XY-plane, whereas the second one refines the whole volume along the Z---axis working on slices in the YZ-plane. In this way, we obtain full coverage for the 3D target function and can leverage spatio-temporal supervision with a set of discriminators. Additionally, we demonstrate that our method can be combined with curriculum learning and progressive growing approaches. We arrive at a first method that can up-sample volumes by a factor of eight along each dimension, i.e., increasing the number of degrees of freedom by 512. Large volumetric up-scaling factors such as this one have previously not been attainable as the required number of weights in the neural networks renders adversarial training runs prohibitively difficult. We demonstrate the generality of our trained networks with a series of comparisons to previous work, a variety of complex 3D results, and an analysis of the resulting performance.
Computational Investigations of the Lithium Superoxide Dimer Rearrangement on Noisy Quantum Devices

Currently available noisy intermediate-scale quantum (NISQ) devices are limited by the number of qubits that can be used for quantum chemistry calculations on molecules. We show herein that the number of qubits required for simulations on a quantum computer can be reduced by limiting the number of orbitals in the active space. Thus, we have utilized ans\"atze that approximate exact classical matrix eigenvalue decomposition methods (Full Configuration Interaction). Such methods are appropriate for computations with the Variational Quantum Eigensolver algorithm to perform computational investigations on the rearrangement of the lithium superoxide dimer with both quantum simulators and quantum devices. These results demonstrate that, even with a limited orbital active space, quantum simulators are capable of obtaining energy values that are similar to the exact ones. However, calculations on quantum hardware underestimate energies even after the application of readout error mitigation.
Optimal control for the quantum simulation of nuclear dynamics

We propose a method for enacting the unitary time propagation of two interacting neutrons at leading order of chiral effective-field theory by efficiently encoding the nuclear dynamics into a single multilevel quantum device. The emulated output of the quantum simulation shows that, by applying a single gate that draws on the underlying characteristics of the device, it is possible to observe multiple cycles of the nuclear dynamics before the onset of decoherence. Owing to the signal's longevity, we can then extract spectroscopic properties of the simulated nuclear system. This allows us to validate the encoding of the nuclear Hamiltonian and the robustness of the simulation in the presence of quantum-hardware noise by comparing the extracted spectroscopic information to exact calculations. This work paves the way for transformative calculations of the dynamical properties of nuclei on near-term quantum devices.
Semisupervised Text Classification by Variational Autoencoder

Semisupervised text classification has attracted much attention from the research community. In this paper, a novel model, the semisupervised sequential variational autoencoder (SSVAE), is proposed to tackle this problem. By treating the categorical label of unlabeled data as a discrete latent variable, the proposed model maximizes the variational evidence lower bound of the data likelihood, which implicitly derives the underlying label distribution for the unlabeled data. Analytical work indicates that the autoregressive nature of the sequential model is the crucial issue that renders the vanilla model ineffective. To remedy this, two types of decoders are investigated in the SSVAE model and verified. In addition, a reweighting approach is proposed to circumvent the credit assignment problem that occurs during the reconstruction procedure, which can further improve performance for sparse text data. Experimental results show that our method significantly improves the classification accuracy compared with other modern methods.
A theory of learning to infer.

Bayesian theories of cognition assume that people can integrate probabilities rationally. However, several empirical findings contradict this proposition: human probabilistic inferences are prone to systematic deviations from optimality. Puzzlingly, these deviations sometimes go in opposite directions. Whereas some studies suggest that people underreact to prior probabilities (base rate neglect), other studies find that people underreact to the likelihood of the data (conservatism). We argue that these deviations arise because the human brain does not rely solely on a general-purpose mechanism for approximating Bayesian inference that is invariant across queries. Instead, the brain is equipped with a recognition model that maps queries to probability distributions. The parameters of this recognition model are optimized to get the output as close as possible, on average, to the true posterior. Because of our limited computational resources, the recognition model will allocate its resources so as to be more accurate for high probability queries than for low probability queries. By adapting to the query distribution, the recognition model learns to infer. We show that this theory can explain why and when people underreact to the data or the prior, and a new experiment demonstrates that these two forms of underreaction can be systematically controlled by manipulating the query distribution. The theory also explains a range of related phenomena: memory effects, belief bias, and the structure of response variability in probabilistic reasoning. We also discuss how the theory can be integrated with prior sampling-based accounts of approximate inference. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
A Dynamic DNA Color Image Encryption Method Based on SHA-512

This paper presents a dynamic deoxyribonucleic acid (DNA) image encryption based on Secure Hash Algorithm-512 (SHA-512), having the structure of two rounds of permutation-diffusion, by employing two chaotic systems, dynamic DNA coding, DNA sequencing operations, and conditional shifting. We employed the SHA-512 algorithm to generate a 512-bit hash value and later utilized this value with the natural DNA sequence to calculate the initial values for the chaotic systems and the eight intermittent parameters. We implemented a two-dimensional rectangular transform (2D-RT) on the permutation. We used four-wing chaotic systems and Lorentz systems to generate chaotic sequences and recombined three channel matrices and chaotic matrices with intermittent parameters. We calculated hamming distances of DNA matrices, updated the initial values of two chaotic systems, and generated the corresponding chaotic matrices to complete the diffusion operation. After diffusion, we decoded and decomposed the DNA matrices, and then scrambled and merged these matrices into an encrypted image. According to experiments, the encryption method in this paper not only was able to withstand statistical attacks, plaintext attacks, brute-force attacks, and a host of other attacks, but also could reduce the complexity of the algorithm because it adopted DNA sequencing operations that were different from traditional DNA sequencing operations.
A Comparison of Eye Tracking Latencies Among Several Commercial Head-Mounted Displays

A number of virtual reality head-mounted displays (HMDs) with integrated eye trackers have recently become commercially available. If their eye tracking latency is low and reliable enough for gaze-contingent rendering, this may open up many interesting opportunities for researchers. We measured eye tracking latencies for the Fove-0, the Varjo VR-1, and the High Tech Computer Corporation (HTC) Vive Pro Eye using simultaneous electrooculography measurements. We determined the time from the occurrence of an eye position change to its availability as a data sample from the eye tracker (delay) and the time from an eye position change to the earliest possible change of the display content (latency). For each test and each device, participants performed 60 saccades between two targets 20° of visual angle apart. The targets were continuously visible in the HMD, and the saccades were instructed by an auditory cue. Data collection and eye tracking calibration were done using the recommended scripts for each device in Unity3D. The Vive Pro Eye was recorded twice, once using the SteamVR SDK and once using the Tobii XR SDK. Our results show clear differences between the HMDs. Delays ranged from 15 ms to 52 ms, and the latencies ranged from 45 ms to 81 ms. The Fove-0 appears to be the fastest device and best suited for gaze-contingent rendering.