Reviewerly/exHarmony huggingface dataset:
Investigation on Motion Sickness in Virtual Reality Environment from the Perspective of User Experience

When wearing a virtual reality (VR) headset for games, different people may behave differently. Common symptoms include general discomfort, headache, upset stomach, nausea, sweating, tiredness and disorientation. This study investigates the side effects of VR game experiences with different frames per second (FPS), refresh rates, and scene scenarios by a questionnaire survey, and discusses possible improvements of current VR devices from the perspective of user experience. The results show that low FPS, low refresh rates, and realistic pictures lead to high probability that players feel uncomfortable; and fighting and shooting games also tend to make players uncomfortable.
Contextualized Translation of Automatically Segmented Speech

Direct speech-to-text translation (ST) models are usually trained on corpora segmented at sentence level, but at inference time they are commonly fed with audio split by a voice activity detector (VAD).Since VAD segmentation is not syntaxinformed, the resulting segments do not necessarily correspond to well-formed sentences uttered by the speaker but, most likely, to fragments of one or more sentences.This segmentation mismatch degrades considerably the quality of ST models' output.So far, researchers have focused on improving audio segmentation towards producing sentence-like splits.In this paper, instead, we address the issue in the model, making it more robust to a different, potentially sub-optimal segmentation.To this aim, we train our models on randomly segmented data and compare two approaches: fine-tuning and adding the previous segment as context.We show that our context-aware solution is more robust to VAD-segmented input, outperforming a strong base model and the fine-tuning on different VAD segmentations of an English-German test set by up to 4.25 BLEU points.
Artificial Coherent States of Light by Multiphoton Interference in a Single-Photon Stream

Coherent optical states consist of a quantum superposition of different photon number (Fock) states, but because they do not form an orthogonal basis, no photon number states can be obtained from it by linear optics. Here we demonstrate the reverse, by manipulating a random continuous single-photon stream using quantum interference in an optical Sagnac loop, we create engineered quantum states of light with tunable photon statistics, including approximate weak coherent states. We demonstrate this experimentally using a true single-photon stream produced by a semiconductor quantum dot in an optical microcavity, and show that we can obtain light with $g^{(2)}(0)\rightarrow1$ in agreement with our theory, which can only be explained by quantum interference of at least 3 photons. The produced artificial light states are, however, much more complex than coherent states, containing quantum entanglement of photons, making them a resource for multi-photon entanglement.
Interpretable Visual Reasoning via Probabilistic Formulation Under Natural Supervision

Visual reasoning is crucial for visual question answering (VQA). However, without labelled programs, implicit reasoning under natural supervision is still quite challenging and previous models are hard to interpret. In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer. Accordingly, we derive a Temporal Reasoning Network (TRN) framework which models the implicit reasoning process as sequential planning in latent space. Our model is interpretable on both model design in probabilist and reasoning process via visualization. We experimentally demonstrate that TRN can support implicit reasoning across various datasets. The experimental results of our model are competitive to existing implicit reasoning models and surpass baseline by a large margin on complicated reasoning tasks without extra computation cost in forward stage.
Local COVID-19 Severity and Social Media Responses: Evidence From China

Unexpected but exceedingly consequential, the COVID-19 outbreak has undermined livelihoods, disrupted the economy, induced upheavals, and posed challenges to government decision-makers. Under various behavioural regulations, such as social distancing and transport limitations, social media has become the central platform on which people from all regions, regardless of local COVID-19 severity, share their feelings and exchange thoughts. Our study illustrates the evolution of moods expressed on social media regarding COVID-19-related issues and empirically confirms the hypothesis that the severity of the pandemic substantially correlates with these sentiments by analysing tweets on Sina Weibo (China's central social media platform). Methodologically, we leveraged Sentiment Knowledge Enhanced Pre-training, the most state-of-the-art natural language processing pre-trained sentiment-related multipurpose model, to label Sina Weibo tweets during the most distressed period in 2020. Given that the model itself does not provide a feature explanation, we utilize a random forest and linear probit model with the labelled sample to demonstrate how each word plays a role in the prediction. Finally, we demonstrate a strong negative linear relationship between the local severity of COVID-19 and the local sentiment response by incorporating miscellaneous geo-economic control variables. In short, our study reveals how pandemics affect local sentiment and, in a broader sense, provides an easy-to-implement and explanatory pipeline to classify sentiments and resolve related socioeconomic issues.
Exchangeable Deep Neural Networks for Set-to-Set Matching and Learning

Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of sets, as well as the items in each set, should be exchangeable. In this study, we propose a novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching. We evaluate the methods through experiments based on two industrial applications: fashion set recommendation and group re-identification. In these experiments, we show that the proposed method provides significant improvements and results compared with the state-of-the-art methods, thereby validating our architecture for the heterogeneous set matching problem.
A universal simulating framework for quantum key distribution systems

Quantum key distribution (QKD) provides a physical-based way to conciliate keys between remote users securely. Simulation is an essential method for designing and optimizing QKD systems. We develop a universal simulation framework based on quantum operator descriptions of photon signals and optical devices. The optical devices can be freely combined and driven by the photon excitation events, which make it appropriate for arbitrary QKD systems in principle. Our framework focuses on realistic characters of optical devices and system structures. The imperfections of the devices and the non-local properties of a quantum system are taken into account when modeling. We simulate the single-photon and Hong-Ou-Mandel (HOM) interference optical units, which are fundamental of QKD systems. The results using this event-driven framework agree well with the theoretical results, which indicate its feasibility for QKD.
Learning Deep Analysis Dictionaries for Image Super-Resolution

Inspired by the recent success of deep neural networks and the recent efforts to develop multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) which is optimized to address a specific regression task known as single image super-resolution. Contrary to other multi-layer dictionary models, our architecture contains L layers of analysis dictionary and soft-thresholding operators to gradually extract high-level features and a layer of synthesis dictionary which is designed to optimize the regression task at hand. In our approach, each analysis dictionary is partitioned into two sub-dictionaries: an Information Preserving Analysis Dictionary (IPAD) and a Clustering Analysis Dictionary (CAD). The IPAD together with the corresponding soft-thresholds is designed to pass the key information from the previous layer to the next layer, while the CAD together with the corresponding soft-thresholding operator is designed to produce a sparse feature representation of its input data that facilitates discrimination of key features. DeepAM uses both supervised and unsupervised setup. Simulation results show that the proposed deep analysis dictionary model achieves better performance compared to a deep neural network that has the same structure and is optimized using back-propagation when training datasets are small.
Simultaneous Feedback and Feedforward Control and Its Application to Realize a Random Walk on the Bloch Sphere in an Xmon-Superconducting-Qubit System

Measurement-based feedback control is central in quantum computing and precise quantum control. Here we realize a fast and flexible field-programmable-gate-array-based feedback control in an Xmon- superconducting-qubit system. The latency of room-temperature electronics is custom-optimized to be as short as 140 ns. Projective measurement of a signal qubit produces a feedback tag to implement a conditional pulse gate on the qubit. In a feedforward process, the measurement-based feedback tag is brought to a different target qubit for conditional control. In a two-qubit experiment, the feedback and feedforward controls are simultaneously implemented in consecutive steps. A quantum number is then generated by the signal qubit, and a random walk of the target qubit is correspondingly triggered and realized on the Bloch sphere. Our experiment provides a conceptually simple and intuitive benchmark for feedback control in a multiqubit system. The feedback system can be further scaled up for more-complex feedback-control experiments.
Designs over finite fields by difference methods

One of the very first results about designs over finite fields, by S. Thomas, is the existence of a cyclic 2-$(n,3,7)$ design over $\mathbb{F}_{2}$ for every integer $n$ coprime with 6. Here, by means of difference methods, we reprove and improve a little bit this result showing that it is true, more generally, for every odd $n$. In this way, we also find the first infinite family of non-trivial cyclic group divisible designs over $\mathbb{F}_{2}$.
Interaction of light and semiconductor can generate quantum states required for solid-state quantum computing: entangled, steered and other nonclassical states

Proposals for solid-state quantum computing are extremely promising as they can be used to build room temperature quantum computers. If such a quantum computer is ever built, it would require built-in sources of nonclassical states required for various quantum information processing tasks. Possibilities of generation of such nonclassical states are investigated here for a physical system composed of a monochromatic light coupled to a two-band semiconductor with direct band gap. The model Hamiltonian includes both photon–exciton and exciton–exciton interactions. Time evolution of the relevant bosonic operators is obtained analytically by using a perturbative technique that provides operator solution for the coupled Heisenberg’s equations of motion corresponding to the system Hamiltonian. The bosonic operators are subsequently used to study the possibilities of observing single- and two-mode squeezing and antibunching after interaction in the relevant modes of light and semiconductor. Further, entanglement between the exciton and photon modes is reported. Finally, the nonclassical effects have been studied numerically for the open quantum system scenario. In this situation, the nonlocal correlations between two modes are shown to violate EPR steering inequality. The observed nonclassical features, induced due to exciton–exciton pair interaction, can be controlled by the phase of input field, and the correlations between two modes are shown to enhance due to nonclassicality in the input field.
Classification of Cancer Pathology Reports: A Large-Scale Comparative Study

We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports. We present results on a large dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports written in Italian and collected from hospitals in Tuscany over more than a decade) and with a large number of classes (134 morphological classes and 61 topographical classes). We compare alternative architectures in terms of prediction accuracy and interpretability and show that our best model achieves a multiclass accuracy of 90.3% on topography site assignment and 84.8% on morphology type assignment. We found that in this context hierarchical models are not better than flat models and that an element-wise maximum aggregator is slightly better than attentive models on site classification. Moreover, the maximum aggregator offers a way to interpret the classification process.
Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates

We analyze the performance of the 2-rate (1 + λ) Evolutionary Algorithm (EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a (1 + λ) EA variant using multiplicative update rules on the OneMax problem. We compare their efficiency for offspring population sizes ranging up to λ = 3, 200 and problem sizes up to n = 100,000.
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters

In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allowing it to learn the data distribution based on the important edges in the input image. We validate our technique on Convolutional DNNs against the state-of-the-art attacks from the open-source Cleverhans library using the MNIST, the CIFAR-10, and the CIFAR-100 datasets. Our experimental results show that the attack success rate, as well as the imperceptibility of the adversarial images, can be significantly reduced by adding effective pre-processing functions, i.e., Sobel filtering.
Pattern generation and symbolic dynamics in a nanocontact vortex oscillator

Abstract Harnessing chaos or intrinsic nonlinear behaviours of dynamical systems is a promising avenue toward unconventional information processing technologies. In this light, spintronic devices are promising because of the inherent nonlinearity of magnetization dynamics. Here, we demonstrate experimentally the potential for chaos-based schemes using nanocontact vortex oscillators by unveiling and characterizing their waveform patterns and symbolic dynamics using time-resolved electrical measurements. We dissociate nonlinear deterministic patterns from thermal fluctuations and show that the emergence of chaos results in the unpredictable alternation of well-defined patterns. With phase-space reconstruction techniques, we perform symbolic analyses of the time series and show that the oscillator exhibits maximal entropy and complexity at the centre of its incommensurate region. This suggests that such vortex-based systems are promising nanoscale sources of entropy that could be exploited for information processing.
Fine-structure classification of multiqubit entanglement by algebraic geometry

We present a fine-structure entanglement classification under stochastic local operation and classical communication (SLOCC) for multiqubit pure states. To this end, we employ specific algebraic-geometry tools that are SLOCC invariants, secant varieties, to show that for $n$-qubit systems there are $\lceil\frac{2^{n}}{n+1}\rceil$ entanglement families. By using another invariant, $\ell$-multilinear ranks, each family can be further split into a finite number of subfamilies. Not only does this method facilitate the classification of multipartite entanglement, but it also turns out to be operationally meaningful as it quantifies entanglement as a resource.
On nonlinear amplification: improved quantum limits for photon counting

We show that detection of single photons is not subject to the fundamental limitations that accompany quantum linear amplification of bosonic mode amplitudes, even though a photodetector does amplify a few-photon input signal to a macroscopic output signal.Alternative limits are derived for nonlinear photon-number amplification schemes with optimistic implications for singlephoton detection.Four commutator-preserving transformations are presented: one idealized (which is optimal) and three more realistic (less than optimal).Our description makes clear that nonlinear amplification takes place, in general, at a different frequency ω than the frequency ω of the input photons.This can be exploited to suppress thermal noise even further up to a fundamental limit imposed by amplification into a single bosonic mode.A practical example that fits our description very well is electron-shelving. I. QUANTUM AMPLIFICATION AND NOISE
Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding

In this paper, we develop an unsupervised generative clustering framework that combines the variational information bottleneck and the Gaussian mixture model. Specifically, in our approach, we use the variational information bottleneck method and model the latent space as a mixture of Gaussians. We derive a bound on the cost function of our model that generalizes the Evidence Lower Bound (ELBO) and provide a variational inference type algorithm that allows computing it. In the algorithm, the coders’ mappings are parametrized using neural networks, and the bound is approximated by Markov sampling and optimized with stochastic gradient descent. Numerical results on real datasets are provided to support the efficiency of our method.
Motion Prediction Using Temporal Inception Module

Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different temporal scales for different length inputs. We argue that the diverse temporal scales are important as they allow us to look at the past frames with different receptive fields, which can lead to better predictions. In this paper, we propose a Temporal Inception Module (TIM) to encode human motion. Making use of TIM, our framework produces input embeddings using convolutional layers, by using different kernel sizes for different input lengths. The experimental results on standard motion prediction benchmark datasets Human3.6M and CMU motion capture dataset show that our approach consistently outperforms the state of the art methods.
Computational Model of Motion Sickness Describing the Effects of Learning Exogenous Motion Dynamics

The existing computational models used to estimate motion sickness are incapable of describing the fact that the predictability of motion patterns affects motion sickness. Therefore, the present study proposes a computational model to describe the effect of the predictability of dynamics or the pattern of motion stimuli on motion sickness. In the proposed model, a submodel, in which a recursive Gaussian process regression is used to represent human features of online learning and future prediction of motion dynamics, is combined with a conventional model of motion sickness based on an observer theory. A simulation experiment was conducted in which the proposed model predicted motion sickness caused by a 900 s horizontal movement. The movement was composed of a 9 m repetitive back-and-forth movement pattern with a pause. Regarding the motion condition, the direction and timing of the motion were varied as follows: a) Predictable motion (M_P): the direction of the motion and duration of the pause were set to 8 s; b) Motion with unpredicted direction (M_dU): the pause duration was fixed as in (P), but the motion direction was randomly determined; c) Motion with unpredicted timing (M_tU): the motion direction was fixed as in (M_P), but the pause duration was randomly selected from 4 to 12 s. The results obtained using the proposed model demonstrated that the predicted motion sickness incidence for (M_P) was smaller than those for (M_dU) and (M_tU). This tendency agrees with the sickness patterns observed in a previous experimental study in which the human participants were subject to motion conditions similar to those used in our simulations. Moreover, no significant differences were found in the predicted motion sickness incidences at different conditions when the conventional model was used.
LabelEnc: A New Intermediate Supervision Method for Object Detection

In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems. The key idea is to introduce a novel label encoding function, mapping the ground-truth labels into latent embedding, acting as an auxiliary intermediate supervision to the detection backbone during training. Our approach mainly involves a two-step training procedure. First, we optimize the label encoding function via an AutoEncoder defined in the label space, approximating the “desired” intermediate representations for the target object detector. Second, taking advantage of the learned label encoding function, we introduce a new auxiliary loss attached to the detection backbones, thus benefiting the performance of the derived detector. Experiments show our method improves a variety of detection systems by around 2% on COCO dataset, no matter one-stage or two-stage frameworks. Moreover, the auxiliary structures only exist during training, i.e. it is completely cost-free in inference time.
Single-mode fiber coupling with a M-SPGD algorithm for long-range quantum communications

Satellite-based quantum communication is a promising approach for realizing global-scale quantum networks. For free-space quantum channel, single-mode fiber coupling is particularly important for improving signal-to-noise ratio of daylight quantum key distribution (QKD) and compatibility with standard fiber-based QKD. However, achieving a highly efficient and stable single-mode coupling efficiency under strong atmospheric turbulence remains experimentally challenging. Here, we develop a single-mode receiver with an adaptive optics (AO) system based on a modal version of the stochastic parallel gradient descent (M-SPGD) algorithm and test its performance over an 8 km urban terrestrial free-space channel. Under strong atmospheric turbulence, the M-SPGD AO system obtains an improvement of about 3.7 dB in the single-mode fiber coupling efficiency and a significant suppression of fluctuation, which can find its applications in free-space long-range quantum communications.