Reviewerly/exHarmony huggingface dataset:
Classical Optical Analogue of Quantum Discord

Quantum discord has been shown to be a resource for quantum advantage in addition to quantum entanglement. While many experiments have demonstrated classical analogies of entanglement, none have done so for discord. We present a proof-of-concept demonstration for creating a classical analogue of quantum discord using classical light that takes advantage of the analogy between the state of two qubits and the spatial modes of a Laguerre-Gauss beam. We demonstrate the validity of this approach by comparing the intensity profiles of theoretical simulations to experimental results for different values of discord. Such a classical analogue of quantum discord may provide further insight in understanding and development of quantum information technologies that make use of discord.
Gradient Descent, Stochastic Optimization, and Other Tales

The goal of this paper is to debunk and dispel the magic behind black-box optimizers and stochastic optimizers. It aims to build a solid foundation on how and why the techniques work. This manuscript crystallizes this knowledge by deriving from simple intuitions, the mathematics behind the strategies. This tutorial doesn't shy away from addressing both the formal and informal aspects of gradient descent and stochastic optimization methods. By doing so, it hopes to provide readers with a deeper understanding of these techniques as well as the when, the how and the why of applying these algorithms. Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize machine learning tasks. Its stochastic version receives attention in recent years, and this is particularly true for optimizing deep neural networks. In deep neural networks, the gradient followed by a single sample or a batch of samples is employed to save computational resources and escape from saddle points. In 1951, Robbins and Monro published \textit{A stochastic approximation method}, one of the first modern treatments on stochastic optimization that estimates local gradients with a new batch of samples. And now, stochastic optimization has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network. The sole aim of this article is to give a self-contained introduction to concepts and mathematical tools in gradient descent and stochastic optimization.
Recognition-Aware Learned Image Compression

Learned image compression methods generally optimize a rate-distortion loss, trading off improvements in visual distortion for added bitrate.Increasingly, however, compressed imagery is used as an input to deep learning networks for various tasks such as classification, object detection, and super-resolution.We propose a recognition-aware learned compression method, which optimizes a rate-distortion loss alongside a task-specific loss, jointly learning compression and recognition networks.We augment a hierarchical autoencoder-based compression network with an EfficientNet recognition model and use two hyperparameters to trade off between distortion, bitrate, and recognition performance.We characterize the classification accuracy of our proposed method as a function of bitrate and find that for low bitrates our method achieves as much as 26% higher recognition accuracy at equivalent bitrates compared to traditional methods such as Better Portable Graphics (BPG).
The KFIoU Loss for Rotated Object Detection

Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics. In contrast, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this paper, we propose an effective approximate SkewIoU loss based on Gaussian modeling and Gaussian product, which mainly consists of two items. The first term is a scale-insensitive center point loss, which is used to quickly narrow the distance between the center points of the two bounding boxes. In the distance-independent second term, the product of the Gaussian distributions is adopted to inherently mimic the mechanism of SkewIoU by its definition, and show its alignment with the SkewIoU loss at trend-level within a certain distance (i.e. within 9 pixels). This is in contrast to recent Gaussian modeling based rotation detectors e.g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors. The resulting new loss called KFIoU loss is easier to implement and works better compared with exact SkewIoU loss, thanks to its full differentiability and ability to handle the non-overlapping cases. We further extend our technique to the 3-D case which also suffers from the same issues as 2-D. Extensive results on various public datasets (2-D/3-D, aerial/text/face images) with different base detectors show the effectiveness of our approach.
Complexity of Bose-Hubbard Model : Quantum Phase Transition

The operator approach is applied to investigate the complexity of Bose-Hubbard model. We present a systematic method to expand the quantum complexity in series of coupling constant. We first study 2-sites system. For the ground state we can find the exact value of complexity which is the summation of all order. The complexity is divergent at critical value of coupling constant that indicates the quantum phase transition at this point. We then generalize the method to the N-sites closed chain and any dimensional system. The found properties are similar to those in 2-sites system. We also study the excited state and present the general formulas of Bose-Hubbard model complexity, which shows a similar form as that in $\lambda \phi^4$ theory studied in our previous paper.
Comparative Analysis of RSA and NTRU Algorithms and Implementation in the Cloud

The emergence of cloud computing platforms makes it easier to connect and collaborate globally without setting up additional infrastructures such as servers and data centers.This causes the emergence of threats to data security against digital information.This security threat can be overcome by cryptography.Examples of cryptographic algorithms are RSA and NTRU.The main concern that arises in this research is how to perform a comparative analysis between asymmetric cryptographic algorithms, RSA (Rivest-Shamir-Adleman) and NTRU (Nth-Degree Truncated Polynomial Ring) algorithms and their implementation in cloud storage.Comparison of performance between the RSA and NTRU algorithms at security levels 80, 112, 128, 160, 192, and 256 bits by running 5 -1000 data, the results obtained that the running time of the key generation process and encryption of the NTRU algorithm is more efficient than the RSA algorithm.Wiener's Attack test on the RSA algorithm and LLL Lattice Basis Reduction on the NTRU algorithm.NTRU algorithm has a more secure level of resilience, so that it can be said that the NTRU algorithm is more recommended for cloud storage security.
Staking Pools on Blockchains

On several proof-of-stake blockchains, agents engaged in validating transactions can open a pool to which others can delegate their stake in order to earn higher returns. We develop a model of staking pool formation in the presence of malicious agents and establish existence and uniqueness of equilibria. We then identify potential and risk of staking pools. First, allowing for staking pools lowers blockchain security. Yet, honest stake holders obtain higher returns. Second, by choosing welfare optimal distribution rewards, staking pools prevent that malicious agents receive large rewards. Third, when pool owners can freely distribute the returns from validation to delegators, staking pools disrupt blockchain operations, since malicious agents attract most delegators by offering generous returns.
Remote Teaching Strategies in Writing Skill Development Due to Covid-19 Pandemic

Covid-19 pandemic inevitably affects educational practices including in teaching writing. It leads to the unfamiliar challenges for those who are in charge, in this case English instructors. Conducted using descriptive qualitative design, this study aims at describing teaching writing strategies to make high-impact improvement for students' writing performance despite their teaching and learning process has to deal with technological and technical challenges. Thus, the researchers suggested several points to focus on; they were punchy topic selection, meaningful purpose, detailed instruction, excellent feedback, peer review to improve metacognition, and reasonable-targetted grading. The researchers found that teaching strategies that were reasonably effective in virtual classroom were choosing punchy topic to gather the students' interest in developing their writing, delivering meaningful purpose of writing to set the boundaries to make a high quality writing, preparing detailed instruction to minimize any misinterpretation from the students, giving excellent feedback so that the students' writing were thoroughly appreciated and corrected, including peer review section to gain metacognition practice, and setting reasonable-targetted grading to motivate the students without giving burden. Even though the strategies may be particular to remote context, additional research result in face-to-face setting is welcomed to enrich the knowledge development especially in teaching writing.
Reality in a sphere: A direct comparison of social attention in the laboratory and the real world

Humans often show reduced social attention in real situations, a finding rarely replicated in controlled laboratory studies. Virtual reality is supposed to allow for ecologically valid and at the same time highly controlled experiments. This study aimed to provide initial insights into the reliability and validity of using spherical videos viewed via a head-mounted display (HMD) to assess social attention. We chose five public places in the city of Würzburg and measured eye movements of 44 participants for 30 s at each location twice: Once in a real environment with mobile eye-tracking glasses and once in a virtual environment playing a spherical video of the location in an HMD with an integrated eye tracker. As hypothesized, participants demonstrated reduced social attention with less exploration of passengers in the real environment as compared to the virtual one. This is in line with earlier studies showing social avoidance in interactive situations. Furthermore, we only observed consistent gaze proportions on passengers across locations in virtual environments. These findings highlight that the potential for social interactions and an adherence to social norms are essential modulators of viewing behavior in social situations and cannot be easily simulated in laboratory contexts. However, spherical videos might be helpful for supplementing the range of methods in social cognition research and other fields. Data and analysis scripts are available at https://osf.io/hktdu/ .
Tell me what you see: A zero-shot action recognition method based on natural language descriptions

This paper presents a novel approach to Zero-Shot Action Recognition. Recent works have explored the detection and classification of objects to obtain semantic information from videos with remarkable performance. Inspired by them, we propose using video captioning methods to extract semantic information about objects, scenes, humans, and their relationships. To the best of our knowledge, this is the first work to represent both videos and labels with descriptive sentences. More specifically, we represent videos using sentences generated via video captioning methods and classes using sentences extracted from documents acquired through search engines on the Internet. Using these representations, we build a shared semantic space employing BERT-based embedders pre-trained in the paraphrasing task on multiple text datasets. The projection of both visual and semantic information onto this space is straightforward, as they are sentences, enabling classification using the nearest neighbor rule. We demonstrate that representing videos and labels with sentences alleviates the domain adaptation problem. Additionally, we show that word vectors are unsuitable for building the semantic embedding space of our descriptions. Our method outperforms the state-of-the-art performance on the UCF101 dataset by 3.3 p.p. in accuracy under the TruZe protocol and achieves competitive results on both the UCF101 and HMDB51 datasets under the conventional protocol (0/50\% - training/testing split). Our code is available at https://github.com/valterlej/zsarcap.
Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty

Abstract Although state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy deep neural network models in the areas where security is a critical concern. In recent years, many research studies have been conducted to develop new attack methods and come up with new defense techniques that enable more robust and reliable models. In this study, we use the quantified epistemic uncertainty obtained from the model’s final probability outputs, along with the model’s own loss function, to generate more effective adversarial samples. And we propose a novel defense approach against attacks like Deepfool which result in adversarial samples located near the model’s decision boundary. We have verified the effectiveness of our attack method on MNIST (Digit), MNIST (Fashion) and CIFAR-10 datasets. In our experiments, we showed that our proposed uncertainty-based reversal method achieved a worst case success rate of around 95% without compromising clean accuracy.
See360: Novel Panoramic View Interpolation

We present See360, which is a versatile and efficient framework for 360° panoramic view interpolation using latent space viewpoint estimation. Most of the existing view rendering approaches only focus on indoor or synthetic 3D environments and render new views of small objects. In contrast, we suggest to tackle camera-centered view synthesis as a 2D affine transformation without using point clouds or depth maps, which enables an effective 360° panoramic scene exploration. Given a pair of reference images, the See360 model learns to render novel views by a proposed novel Multi-Scale Affine Transformer (MSAT), enabling the coarse-to-fine feature rendering. We also propose a Conditional Latent space AutoEncoder (C-LAE) to achieve view interpolation at any arbitrary angle. To show the versatility of our method, we introduce four training datasets, namely UrbanCity360, Archinterior360, HungHom360 and Lab360, which are collected from indoor and outdoor environments for both real and synthetic rendering. Experimental results show that the proposed method is generic enough to achieve real-time rendering of arbitrary views for all four datasets. In addition, our See360 model can be applied to view synthesis in the wild: with only a short extra training time (approximately 10 mins), and is able to render unknown real-world scenes. The superior performance of See360 opens up a promising direction for camera-centered view rendering and 360° panoramic view interpolation.
Improve few-shot voice cloning using multi-modal learning

Recently, few-shot voice cloning has achieved a significant improvement. However, most models for few-shot voice cloning are single-modal, and multi-modal few-shot voice cloning has been understudied. In this paper, we propose to use multi-modal learning to improve the few-shot voice cloning performance. Inspired by the recent works on unsupervised speech representation, the proposed multi-modal system is built by extending Tacotron2 with an unsupervised speech representation module. We evaluate our proposed system in two few-shot voice cloning scenarios, namely few-shot text-to-speech(TTS) and voice conversion(VC). Experimental results demonstrate that the proposed multi-modal learning can significantly improve the few-shot voice cloning performance over their counterpart single-modal systems.
An Optimized Bidirectional Quantum Teleportation Scheme with the Use of Bell States

Bidirectional quantum teleportation scheme is a two-way quantum communication process, in which two parties simultaneously receive each other information. Recently, in paper (Zhou et al., IEEE Access, 7, 44269 2019), a six-qubit cluster state has been used to teleport a three-qubit entangled state (Alice) and arbitrary single-qubit unknown quantum state (Bob) bidirectionally. In experimental point of view, the preparation and the maintenance of cluster type entangled state is very difficult and costly. So, in this paper we have designed an optimal scheme (less amount of quantum resource) of bidirectional quantum teleportation scheme and shown that proposed scheme requires an optimized amount of quantum resource.
Design, Implementation and Evaluation of Azmer Online Quiz Application Based on Technology Acceptance Model (TAM): A pilot study

Design, Implementation and Evaluation of Azmer Online Quiz Application Based on Technology Acceptance Model (TAM): A pilot study
Memory-Efficient Training of RNN-Transducer with Sampled Softmax

RNN-Transducer has been one of promising architectures for end-to-end automatic speech recognition. Although RNN-Transducer has many advantages including its strong accuracy and streaming-friendly property, its high memory consumption during training has been a critical problem for development. In this work, we propose to apply sampled softmax to RNN-Transducer, which requires only a small subset of vocabulary during training thus saves its memory consumption. We further extend sampled softmax to optimize memory consumption for a minibatch, and employ distributions of auxiliary CTC losses for sampling vocabulary to improve model accuracy. We present experimental results on LibriSpeech, AISHELL-1, and CSJ-APS, where sampled softmax greatly reduces memory consumption and still maintains the accuracy of the baseline model.
Memorizing Transformers

Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend language models with the ability to memorize the internal representations of past inputs. We demonstrate that an approximate kNN lookup into a non-differentiable memory of recent (key, value) pairs improves language modeling across various benchmarks and tasks, including generic webtext (C4), math papers (arXiv), books (PG-19), code (Github), as well as formal theorems (Isabelle). We show that the performance steadily improves when we increase the size of memory up to 262K tokens. On benchmarks including code and mathematics, we find that the model is capable of making use of newly defined functions and theorems during test time.
Successive Consensus Clustering for Unsupervised Video-Based Person Re-Identification

Person re-identification is to match the same person between non-overlapping cameras. This paper focuses on unsupervised video-based person re-identification. The mainstream approach is to obtain pseudo-labels by clustering samples for training the classification model. In this scheme, a potential threat is that noisy pseudo-labels may damage the optimization of the model. To mitigate this danger, we propose using a Successive Consensus Clustering framework for optimizing the pseudo-labels and the model iteratively. First, we leverage consensus clustering with respect to multiple frames of a video, which can generate high-quality pseudo-labels for pedestrians. Secondly, we develop contrastive learning based on the cluster successive memory mechanism, which can establish the correlation between different epochs of clustering so that makes the training of the model stable. Experiments on three large-scale data sets show that our method outperforms the previous state-of-the-art method, surpassing 10.6% for rank-1 and 18.6% for mAP on Mars, and 9.6% for rank-1 and 13.3% for mAP on DukeMTMC-VideoReID.
Coherent Control of Collective Spontaneous Emission through Self-Interference

As one of the central topics in quantum optics, collective spontaneous emission such as superradiance has been realized in a variety of systems. This Letter proposes an innovative scheme to coherently control collective emission rates via a self-interference mechanism in a nonlinear waveguide setting. The self-interference is made possible by photon backward scattering incurred by quantum scatterers in a waveguide working as quantum switches. Whether the interference is constructive or destructive is found to depend strongly on the distance between the scatterers and the emitters. The interference between two propagation pathways of the same photon leads to controllable superradiance and subradiance, with their collective decay rates much enhanced or suppressed (also leading to hyperradiance or population trapping). Furthermore, the self-interference mechanism is manifested by an abrupt change in the emission rates in real time. An experimental setup based on superconducting transmission line resonators and transmon qubits is further proposed to realize controllable collective emission rates.
autohaem: 3D printed devices for automated preparation of blood smears

The process of making blood smears is common in both research and clinical settings, for investigating the health of blood cells and the presence of blood-borne parasites. It is very often carried out manually. We focus here on smears for malaria diagnosis and research which are frequently analyzed by optical microscopy and require a high quality. Automating the smear preparation promises to increase throughput and to improve the quality and consistency of the smears. We present here two devices (manual and motorized) designed to aid in the making of blood smears. These are fully documented, open-source hardware, and an important principle was to make them easily fabricated locally anywhere. Designs and assembly instructions are freely available under an open license. We also describe an image analysis pipeline for characterizing the quality of smears, and use it to optimize the settings and tunable parameters in the two devices. The devices perform as well as expert human operators, while not requiring a trained operator and offering potential advantages in reproducibility and standardization across facilities.
Interactive Machine Learning for Image Captioning

We propose an approach for interactive learning for an image captioning model. As human feedback is expensive and modern neural network based approaches often require large amounts of supervised data to be trained, we envision a system that exploits human feedback as good as possible by multiplying the feedback using data augmentation methods, and integrating the resulting training examples into the model in a smart way. This approach has three key components, for which we need to find suitable practical implementations: feedback collection, data augmentation, and model update. We outline our idea and review different possibilities to address these tasks.