Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Based on Motion Sensor Using U-Net

Traditional human activity recognition (HAR) based on a motion sensor adopts sliding window labeling and prediction. This method faces the multi-class window problem, which mistakenly labels different classes of sampling points within a window as a class. In this paper, we propose a novel HAR method based on U-Net to overcome the multi-class problem, performing activity labeling and prediction of each sampling point. The motion sensor data collected from the wearable sensors are mapped into an image with the single-pixel column and multi-channel, and then, it is input into the U-Net network to complete the pixel-level activity recognition function. We design a complete HAR framework based on U-Net to realize the dense prediction of motion sensor data, including data preprocessing, dense prediction, and post analysis. In order to further improve the dense prediction performance, we propose the post-correction algorithm for the dense prediction results on the basis of the activity misalignment analysis. The extensive experimental results demonstrate that our U-Net method performs better than the traditional machine learning and deep learning methods based on the sliding window prediction. And it also outperforms full convolutional network (FCN), SegNet, and Mask R-CNN based on the dense prediction on the four datasets. Moreover, it also shows the better robustness and excellent performance of recognition on the short-term activities and minority classes. We release a new dataset named Sanitation, which includes seven types of daily work activity data of sanitation workers to evaluate the HAR algorithm’s performance.
Ensemble Kalman inversion: a derivative-free technique for machine learning tasks

The standard probabilistic perspective on machine learning gives rise to empirical risk-minimization tasks that are frequently solved by stochastic gradient descent (SGD) and variants thereof. We present a formulation of these tasks as classical inverse or filtering problems and, furthermore, we propose an efficient, gradient-free algorithm for finding a solution to these problems using ensemble Kalman inversion (EKI). The method is inherently parallelizable and is applicable to problems with non-differentiable loss functions, for which back-propagation is not possible. Applications of our approach include offline and online supervised learning with deep neural networks, as well as graph-based semi-supervised learning. The essence of the EKI procedure is an ensemble based approximate gradient descent in which derivatives are replaced by differences from within the ensemble. We suggest several modifications to the basic method, derived from empirically successful heuristics developed in the context of SGD. Numerical results demonstrate wide applicability and robustness of the proposed algorithm.
Learning and inference on generative adversarial quantum circuits

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to the lack of an efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed-up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuits using the prototypical bars-and-stripes dataset. Generative adversarial quantum circuits are a fresh approach to machine learning which may enjoy the practically useful quantum advantage of near-term quantum devices.
Repeaterless quantum key distribution with efficient finite-key analysis overcoming the rate-distance limit

Abstract Quantum key distribution (QKD) over a point-to-point link enables us to benefit from a genuine quantum effect even with conventional optics tools such as lasers and photon detectors, but its capacity is limited to a linear scaling of the repeaterless bound. Recently, twin-field (TF) QKD was conjectured to beat the limit by using an untrusted central station conducting a single-photon interference detection. So far, the effort to prove the conjecture was confined to the infinite key limit which neglected the time and cost for monitoring an adversary’s act. Here we propose a variant of TF-type QKD protocol equipped with a simple methodology of monitoring to reduce its cost and provide an information-theoretic security proof applicable to finite communication time. We simulate the key rate to show that the protocol beats the linear bound in a reasonable running time of sending 10 12 pulses, which positively solves the conjecture.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

We focus on the cross-domain contextdependent text-to-SQL generation task.Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality.Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner.It is flexible to change individual tokens and robust to error propagation.Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema.We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch.Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention

The general trend in NLP is towards increasing model capacity and performance via deeper neural networks.However, simply stacking more layers of the popular Transformer architecture for machine translation results in poor convergence and high computational overhead.Our empirical analysis suggests that convergence is poor due to gradient vanishing caused by the interaction between residual connections and layer normalization.We propose depth-scaled initialization (DS-Init), which decreases parameter variance at the initialization stage, and reduces output variance of residual connections so as to ease gradient back-propagation through normalization layers.To address computational cost, we propose a merged attention sublayer (MAtt) which combines a simplified averagebased self-attention sublayer and the encoderdecoder attention sublayer on the decoder side.Results on WMT and IWSLT translation tasks with five translation directions show that deep Transformers with DS-Init and MAtt can substantially outperform their base counterpart in terms of BLEU (+1.1 BLEU on average for 12-layer models), while matching the decoding speed of the baseline model thanks to the efficiency improvements of MAtt. 1
Input-Output Theory with Quantum Pulses

We present a formalism that accounts for the interaction of a local quantum system, such as an atom or a cavity, with traveling pulses of quantized radiation. We assume Markovian coupling of the stationary system to the input and output fields and nondispersive asymptotic propagation of the pulses before and after the interaction. This permits derivation of a master equation where the input and output pulses are treated as single oscillator modes that both couple to the local system in a cascaded manner. As examples of our theory, we analyze reflection by an empty cavity with phase noise, stimulated atomic emission by a quantum light pulse, and formation of a Schr\"odinger-cat state by the dispersive interaction of a coherent pulse and a single atom in a cavity.
An attempt at beating the 3D U-Net

The U-Net is arguably the most successful segmentation architecture in the medical domain.Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks.Cross-validation results on the training cases suggest only very minor, barely measurable improvements.Due to marginally higher dice scores, the residual 3D U-Net is chosen for test set prediction.With a Composite Dice score of 91.23 on the test set, our method outperformed all 105 competing teams and won the KiTS2019 challenge by a small margin.
Progressive Retinex

Contrast enhancement and noise removal are coupled problems for low-light image enhancement. The existing Retinex based methods do not take the coupling relation into consideration, resulting in under or over-smoothing of the enhanced images. To address this issue, this paper presents a novel progressive Retinex framework, in which illumination and noise of low-light image are perceived in a mutually reinforced manner, leading to noise reduction low-light enhancement results. Specifically, two fully pointwise convolutional neural networks are devised to model the statistical regularities of ambient light and image noise respectively, and to leverage them as constraints to facilitate the mutual learning process. The proposed method not only suppresses the interference caused by the ambiguity between tiny textures and image noises, but also greatly improves the computational efficiency. Moreover, to solve the problem of insufficient training data, we propose an image synthesis strategy based on camera imaging model, which generates color images corrupted by illumination-dependent noises. Experimental results on both synthetic and real low-light images demonstrate the superiority of our proposed approaches against the State-Of-The-Art (SOTA) low-light enhancement methods.
Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation.In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations.We target expository language describing causes and effects (e.g., "animal pollinators increase efficiency of fertilization in flowers"), as they have clear implications for new situations.A system is presented a background passage containing at least one of these relations, a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation.We collect background passages from science textbooks and Wikipedia that contain such phenomena, and ask crowd workers to author situations, questions, and answers, resulting in a 14,322 question dataset.We analyze the challenges of this task and evaluate the performance of state-of-the-art reading comprehension models.The best model performs only slightly better than randomly guessing an answer of the correct type, at 61.6% F1, well below the human performance of 89.0%.
A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran

Mapping hydrothermal alteration minerals using multispectral remote sensing satellite imagery provides vital information for the exploration of porphyry and epithermal ore mineralizations. The Ahar-Arasbaran region, NW Iran, contains a variety of porphyry, skarn and epithermal ore deposits. Gold mineralization occurs in the form of epithermal veins and veinlets, which is associated with hydrothermal alteration zones. Thus, the identification of hydrothermal alteration zones is one of the key indicators for targeting new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. In this study, Landsat Enhanced Thematic Mapper+ (Landsat-7 ETM+), Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing datasets were processed to detect hydrothermal alteration zones associated with epithermal gold mineralization in the Ahar-Arasbaran region. Band ratio techniques and principal component analysis (PCA) were applied on Landsat-7 ETM+ and Landsat-8 data to map hydrothermal alteration zones. Advanced argillic, argillic-phyllic, propylitic and hydrous silica alteration zones were detected and discriminated by implementing band ratio, relative absorption band depth (RBD) and selective PCA to ASTER data. Subsequently, the Bayesian network classifier was used to synthesize the thematic layers of hydrothermal alteration zones. A mineral potential map was generated by the Bayesian network classifier, which shows several new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. Besides, comprehensive field surveying and laboratory analysis were conducted to verify the remote sensing results and mineral potential map produced by the Bayesian network classifier. A good rate of agreement with field and laboratory data is achieved for remote sensing results and consequential mineral potential map. It is recommended that the Bayesian network classifier can be broadly used as a valuable model for fusing multi-sensor remote sensing results to generate mineral potential map for reconnaissance stages of epithermal gold exploration in the Ahar-Arasbaran region and other analogous metallogenic provinces around the world.
Adversarial Cross-Domain Action Recognition with Co-Attention

Action recognition has been a widely studied topic with a heavy focus on supervised learning involving sufficient labeled videos. However, the problem of cross-domain action recognition, where training and testing videos are drawn from different underlying distributions, remains largely under-explored. Previous methods directly employ techniques for cross-domain image recognition, which tend to suffer from the severe temporal misalignment problem. This paper proposes a Temporal Co-attention Network (TCoN), which matches the distributions of temporally aligned action features between source and target domains using a novel cross-domain co-attention mechanism. Experimental results on three cross-domain action recognition datasets demonstrate that TCoN improves both previous single-domain and cross-domain methods significantly under the cross-domain setting.
A^3: Accelerating Attention Mechanisms in Neural Networks with Approximation

With the increasing computational demands of the neural networks, many hardware accelerators for the neural networks have been proposed. Such existing neural network accelerators often focus on popular neural network types such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs); however, not much attention has been paid to attention mechanisms, an emerging neural network primitive that enables neural networks to retrieve most relevant information from a knowledge-base, external memory, or past states. The attention mechanism is widely adopted by many state-of-the-art neural networks for computer vision, natural language processing, and machine translation, and accounts for a large portion of total execution time. We observe today's practice of implementing this mechanism using matrix-vector multiplication is suboptimal as the attention mechanism is semantically a content-based search where a large portion of computations ends up not being used. Based on this observation, we design and architect A3, which accelerates attention mechanisms in neural networks with algorithmic approximation and hardware specialization. Our proposed accelerator achieves multiple orders of magnitude improvement in energy efficiency (performance/watt) as well as substantial speedup over the state-of-the-art conventional hardware.
Shared Surfaces and Spaces: Collaborative Data Visualisation in a Co-located Immersive Environment

Immersive technologies offer new opportunities to support collaborative visual data analysis by providing each collaborator a personal, high-resolution view of a flexible shared visualisation space through a head mounted display. However, most prior studies of collaborative immersive analytics have focused on how groups interact with surface interfaces such as tabletops and wall displays. This paper reports on a study in which teams of three co-located participants are given flexible visualisation authoring tools to allow a great deal of control in how they structure their shared workspace. They do so using a prototype system we call FIESTA: the Free-roaming Immersive Environment to Support Team-based Analysis. Unlike traditional visualisation tools, FIESTA allows users to freely position authoring interfaces and visualisation artefacts anywhere in the virtual environment, either on virtual surfaces or suspended within the interaction space. Our participants solved visual analytics tasks on a multivariate data set, doing so individually and collaboratively by creating a large number of 2D and 3D visualisations. Their behaviours suggest that the usage of surfaces is coupled with the type of visualisation used, often using walls to organise 2D visualisations, but positioning 3D visualisations in the space around them. Outside of tightly-coupled collaboration, participants followed social protocols and did not interact with visualisations that did not belong to them even if outside of its owner's personal workspace.
Detecting single infrared photons toward optimal system detection efficiency

Superconducting nanowire single-photon detector (SNSPD) with near-unity system efficiency is a key enabling, but still elusive, technology for numerous quantum fundamental theory verifications and quantum information applications. The key challenge is to have both a near-unity photon-response probability and absorption efficiency simultaneously for the meandered nanowire with a finite filling ratio, which is more crucial for NbN than other superconducting materials (e.g., WSi) with lower transition temperatures. Here, we overcome the above challenge and produce NbN SNSPDs with a record system efficiency by replacing a single-layer nanowire with twin-layer nanowires on a dielectric mirror. The detector at 0.8 K shows a maximal system detection efficiency (SDE) of 98% at 1590 nm and a system efficiency of over 95% in the wavelength range of 1530–1630 nm. Moreover, the detector at 2.1 K demonstrates a maximal SDE of 95% at 1550 nm using a compacted two-stage cryocooler. This type of detector also shows the robustness against various parameters, such as the geometrical size of the nanowire and the spectral bandwidth, enabling a high yield of 73% (36%) with an SDE of &gt;80% (90%) at 2.1 K for 45 detectors fabricated in the same run. These SNSPDs made of twin-layer nanowires are of important practical significance for batch production.
Hyperledger Fabric Blockchain for Securing the Edge Internet of Things

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
Guidelines for the Development of Immersive Virtual Reality Software for Cognitive Neuroscience and Neuropsychology: The Development of Virtual Reality Everyday Assessment Lab (VR-EAL), a Neuropsychological Test Battery in Immersive Virtual Reality

Virtual reality (VR) head-mounted displays (HMD) appear to be effective research tools, which may address the problem of ecological validity in neuropsychological testing. However, their widespread implementation is hindered by VR induced symptoms and effects (VRISE) and the lack of skills in VR software development. This study offers guidelines for the development of VR software in cognitive neuroscience and neuropsychology, by describing and discussing the stages of the development of Virtual Reality Everyday Assessment Lab (VR-EAL), the first neuropsychological battery in immersive VR. Techniques for evaluating cognitive functions within a realistic storyline are discussed. The utility of various assets in Unity, software development kits, and other software are described so that cognitive scientists can overcome challenges pertinent to VRISE and the quality of the VR software. In addition, this pilot study attempts to evaluate VR-EAL in accordance with the necessary criteria for VR software for research purposes. The VR neuroscience questionnaire (VRNQ; Kourtesis et al., 2019b) was implemented to appraise the quality of the three versions of VR-EAL in terms of user experience, game mechanics, in-game assistance, and VRISE. Twenty-five participants aged between 20 and 45 years with 12-16 years of full-time education evaluated various versions of VR-EAL. The final version of VR-EAL achieved high scores in every sub-score of the VRNQ and exceeded its parsimonious cut-offs. It also appeared to have better in-game assistance and game mechanics, while its improved graphics substantially increased the quality of the user experience and almost eradicated VRISE. The results substantially support the feasibility of the development of effective VR research and clinical software without the presence of VRISE during a 60-minute VR session.
Theory of Quantum System Certification

The precise control of complex quantum systems promises numerous technological applications including digital quantum computing. The complexity of such devices renders the certification of their correct functioning a challenge. To address this challenge, numerous methods were developed in the last decade. In this tutorial, we explain prominent protocols for certifying the physical layer of quantum devices described by quantum states and processes. Such protocols are particularly important in the development of near-term devices. Specifically, we discuss methods of direct quantum state certification, direct fidelity estimation, shadow fidelity estimation, direct quantum process certification, randomized benchmarking and cross-entropy benchmarking. Moreover, we provide an introduction to powerful mathematical methods, which are widely used in quantum information theory, in order to derive theoretical guarantees for the protocols.