Reviewerly/exHarmony huggingface dataset:
Surface Code Compilation via Edge-Disjoint Paths

We provide an efficient algorithm to compile quantum circuits for fault-tolerant execution. We target surface codes, which form a two-dimensional grid of logical qubits with nearest-neighbor logical operations. Embedding an input circuit’s qubits in surface codes can result in long-range two-qubit operations across the grid. We show how to prepare many long-range Bell pairs on qubits connected by edge-disjoint paths of ancillae in constant depth that can be used to perform these long-range operations. This forms one core part of our edge-disjoint path compilation (EDPC) algorithm, by easily performing many parallel long-range Clifford operations in constant depth. It also allows us to establish a connection between surface code compilation and several well-studied edge-disjoint path problems. Similar techniques allow us to perform non-Clifford single-qubit rotations far from magic state distillation factories. In this case, we can easily find the maximum set of paths by a max-flow reduction, which forms the other major part of EDPC. EDPC has the best asymptotic worst-case performance guarantees on the circuit depth for compiling parallel operations when compared to related compilation methods based on swap gates and network coding. EDPC also shows a quadratic depth improvement over sequential Pauli-based compilation for parallel rotations requiring magic resources. We implement EDPC and find significantly improved performance for circuits built from parallel controlled-not (cnot) gates, and for circuits that implement the multicontrolled X gate CkNOT.19 MoreReceived 31 December 2021Accepted 16 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020342Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum gatesSurface code quantum computingQuantum Information
Representation learning for neural population activity with Neural Data Transformers

Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT’s ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics. Code: https://github.com/snel-repo/neural-data-transformers
Age and Gender Prediction Using Deep CNNs and Transfer Learning

The last decade or two has witnessed a boom of images. With the increasing ubiquity of cameras and with the advent of selfies, the number of facial images available in the world has skyrocketed. Consequently, there has been a growing interest in automatic age and gender prediction of a person using facial images. We in this paper focus on this challenging problem. Specifically, this paper focuses on age estimation, age classification and gender classification from still facial images of an individual. We train different models for each problem and we also draw comparisons between building a custom CNN (Convolutional Neural Network) architecture and using various CNN architectures as feature extractors, namely VGG16 pre-trained on VGGFace, Res-Net50 and SE-ResNet50 pre-trained on VGGFace2 dataset and training over those extracted features. We also provide baseline performance of various machine learning algorithms on the feature extraction which gave us the best results. It was observed that even simple linear regression trained on such extracted features outperformed training CNN, ResNet50 and ResNeXt50 from scratch for age estimation.
A New Product Development Model for SMEs: Introducing Agility to the Plan-Driven Concurrent Product Development Approach

In order to survive in today’s highly competitive global market, small and medium-sized enterprises (SMEs) have had to transition from sequential to concurrent product development, which significantly shortens development cycles, reduces costs, and ensures high product quality. Despite its many benefits, concurrent product development is still based on detailed upfront planning and cannot address the challenges related to today’s ever-growing uncertainty, constantly changing environment, and unstable requirements. A potential solution to this problem could be in more flexible and value-driven agile project management (APM) approaches, typical of software development. In this paper, we propose a new product development model specifically appropriate for SMEs that combines concurrent product development principles with APM elements. It is designed as a loop of five repetitive steps (macroplan, microplan, iteration activities, review, and retrospective) that are being executed within individual concurrent development loops. The application of the model is presented on a real case example of process development and small batch manufacture of a complex wiring harness. The study reveals many benefits of the proposed model, such as improved communication, faster detection of discrepancies, more effective problem solving, and greater flexibility. A positive impact on project success is also observed.
A prediction method of regional water resources carrying capacity based on artificial neural network

To better predict the water resources carrying capacity and guide the social and economic activities, a prediction method of regional water resources carrying capacity is proposed based on an artificial neural network. Zhaozhou County is selected as the research area of water resources carrying capacity prediction, and its natural geographical characteristics, social economy, and water resources situation are explored. According to the regional water resources quantity and utilization characteristics and evaluation emphasis, the evaluation index system of water resources carrying capacity is constructed to evaluate the importance and correlation of water resource carrying capacity. The pressure degree of water resources carrying capacity is divided into five grades. According to the evaluation standard of bearing capacity, the artificial intelligence BP neural network model is constructed. Based on the main impact factors of water resources carrying capacity in this area, the water resources carrying capacity grade is obtained by weight calculation and convergence iteration by using neural network model and influence factor data to realize the prediction of water resources carrying capacity. The research results show that the network model can meet the demand for precision. The prediction results have a high degree of fit with the actual data, indicating that human intelligence can obtain accurate prediction results in water resources carrying capacity prediction.
A systematic review on application of deep learning in digestive system image processing

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.
Improved Random Forest Method for Ultra-short-term Prediction of the Output Power of a Photovoltaic Cluster

Current models for the prediction of the output power of photovoltaic (PV) clusters suffer from low prediction accuracy and are prone to overfitting. To address these problems, we propose an improved random forest (RF)-based method for ultra-short-term prediction of PV cluster output power. The total output power data for the PV clusters are used as the training dataset and fed into the RF model to obtain preliminary predictions. The error and accuracy of the preliminary predictions for individual sampling points concerning the actual values of the PV cluster output power are assessed. Each of the daily time series of preliminary predictions is divided into two phases according to whether the output power is increasing (morning) or decreasing (afternoon). The final ultra-short-term predictions of the PV cluster output power are obtained by correcting the two phases of preliminary predictions through trend correction and peak correction, respectively. The results show that, compared with the unimproved model, the accuracy of the stochastic forest model is 1.48% higher than that of the modified random forest model., which proves the effectiveness and practicability of the proposed method.
Human Rationales as Attribution Priors for Explainable Stance Detection

As NLP systems become better at detecting opinions and beliefs from text, it is important to ensure not only that models are accurate but also that they arrive at their predictions in ways that align with human reasoning.In this work, we present a method for imparting human-like rationalization to a stance detection model using crowdsourced annotations on a small fraction of the training data.We show that in a data-scarce setting, our approach can improve the reasoning of a state-of-the-art classifierparticularly for inputs containing challenging phenomena such as sarcasm-at no cost in predictive performance.Furthermore, we demonstrate that attention weights surpass a leading attribution method in providing faithful explanations of our model's predictions, thus serving as a computationally cheap and reliable source of attributions for our model.
Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification

As labeling schemas evolve over time, small differences can render datasets following older schemas unusable.This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small classimbalanced datasets.In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification.We show an improvement of 4.9% Micro F1-score over current state-of-the-art benchmarks on the NewsDiscourse dataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes.We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting 1 .
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions as queries.Natural language and molecules encode information in very different ways, which leads to the exciting but challenging problem of integrating these two very different modalities.Although some work has been done on text-based retrieval and structurebased retrieval, this new task requires integrating molecules and natural language more directly.Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar.We construct a paired dataset of molecules and their corresponding text descriptions, which we use to learn an aligned common semantic embedding space for retrieval.We extend this to create a cross-modal attention-based model for explainability and reranking by interpreting the attentions as association rules.We also employ an ensemble approach to integrate our different architectures, which significantly improves results from 0.372 to 0.499 MRR.This new multimodal approach opens a new perspective on solving problems in chemistry literature understanding and molecular machine learning. 11 The programs and data are publicly available at github.com/cnedwards/text2mol for research purposes.Water is an oxygen hydride consisting of an oxygen atom that is covalently bonded to two hydrogen atoms.
Systematic Analysis and Computational Intelligence Based Modeling of Photovoltaic Power Generation in Snow Conditions

Power prediction for photovoltaic (PV) installations in northern snow-prone areas remains a challenging problem. The behavior of a partially/fully snow-covered PV panel can be complex depending on the snow-related factors, disregarding, which causes large prediction errors in snow conditions. To address this issue, a systematic analysis of the effects of 16 meteorological parameters on the hourly power prediction for systems in a snow-prone area is conducted in this article. According to the best of our knowledge, such comprehensive analysis on various snow-related conditions datasets, i.e., full, snow-free condition, snow condition, and snow-cover condition datasets, for several PV systems is performed for the first time in the literature. The three latter datasets are extracted from the full dataset using a proposed three-step procedure. Moreover, different computational intelligence techniques are implemented to develop hourly prediction models for each dataset of each system. A detailed comparison is then performed between the performance of the proposed models, the Marion model, a modified version of the Marion model, the classic PV model, and a computational intelligence model combined with the Marion model's snow cover detection method. The hourly values of the electrical and meteorologicalparameters for 17 PV systems across Canada, with an aggregated time period of more than 55 years, have been extracted to perform the study. As the results show, categorizing data using the proposed three-step procedure and developing specific computational intelligence models for each condition can significantly improve the prediction accuracy especially when a full/partial snow cover is probable on the panels.
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose

Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant appearance variations across viewpoints that make the recognition tasks challenging. To address this, we explore recognizing similarity in 3D human body poses from 2D information, which has not been well-studied in existing works. Here, we propose an approach to learning a compact view-invariant embedding space from 2D body joint keypoints, without explicitly predicting 3D poses. Input ambiguities of 2D poses from projection and occlusion are difficult to represent through a deterministic mapping, and therefore we adopt a probabilistic formulation for our embedding space. Experimental results show that our embedding model achieves higher accuracy when retrieving similar poses across different camera views, in comparison with 3D pose estimation models. We also show that by training a simple temporal embedding model, we achieve superior performance on pose sequence retrieval and largely reduce the embedding dimension from stacking frame-based embeddings for efficient large-scale retrieval. Furthermore, in order to enable our embeddings to work with partially visible input, we further investigate different keypoint occlusion augmentation strategies during training. We demonstrate that these occlusion augmentations significantly improve retrieval performance on partial 2D input poses. Results on action recognition and video alignment demonstrate that using our embeddings without any additional training achieves competitive performance relative to other models specifically trained for each task.
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization

One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role.In this paper, we propose a semi-automatic framework for generating disentangled shifts by introducing a controllable visual question-answer generation (VQAG) module that is capable of generating highly-relevant and diverse questionanswer pairs with the desired dataset style.We use it to create CrossVQA, a collection of test splits for assessing VQA generalization based on the VQA2, VizWiz, and Open Images datasets.We provide an analysis of our generated datasets and demonstrate its utility by using them to evaluate several state-of-theart VQA systems.One important finding is that the visual shifts in cross-dataset VQA matter more than the language shifts.More broadly, we present a scalable framework for systematically evaluating the machine with little human intervention.
The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

In this paper, we provide an overview of the CODI-CRAC 2021 Shared Task.The shared task focuses on detecting anaphoric relations in different genres of conversations.Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple tasks focusing individually on these key relations.We discuss the evaluation scripts used to assess the system performance on these tasks, and provide a brief summary of the participating systems and the results obtained across 115 runs from six teams, with most submissions achieving significantly better results than our baseline methods.
An oversampling method for multi-class imbalanced data based on composite weights

To solve the oversampling problem of multi-class small samples and to improve their classification accuracy, we develop an oversampling method based on classification ranking and weight setting. The designed oversampling algorithm sorts the data within each class of dataset according to the distance from original data to the hyperplane. Furthermore, iterative sampling is performed within the class and inter-class sampling is adopted at the boundaries of adjacent classes according to the sampling weight composed of data density and data sorting. Finally, information assignment is performed on all newly generated sampling data. The training and testing experiments of the algorithm are conducted by using the UCI imbalanced datasets, and the established composite metrics are used to evaluate the performance of the proposed algorithm and other algorithms in comprehensive evaluation method. The results show that the proposed algorithm makes the multi-class imbalanced data balanced in terms of quantity, and the newly generated data maintain the distribution characteristics and information properties of the original samples. Moreover, compared with other algorithms such as SMOTE and SVMOM, the proposed algorithm has reached a higher classification accuracy of about 90%. It is concluded that this algorithm has high practicability and general characteristics for imbalanced multi-class samples.
Entropy production and the role of correlations in quantum Brownian motion

We perform a study on quantum entropy production, different kinds of correlations, and their interplay in the driven Caldeira-Leggett model of quantum Brownian motion. The model, taken with a large but finite number of bath modes, is exactly solvable, and the assumption of a Gaussian initial state leads to an efficient numerical simulation of all desired observables in a wide range of model parameters. Our study is composed of three main parts. We first compare two popular definitions of entropy production, namely the standard weak-coupling formulation originally proposed by Spohn and later on extended to the driven case by Deffner and Lutz, and the always-positive expression introduced by Esposito, Lindenberg and van den Broeck, which relies on the knowledge of the evolution of the bath. As a second study, we explore the decomposition of the Esposito et al. entropy production into system-environment and intra-environment correlations for different ranges of couplings and temperatures. Lastly, we examine the evolution of quantum correlations between the system and the environment, measuring entanglement through logarithmic negativity.
Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation (quasi-LFM) radar waveforms (LFM, Frank code, P1-P4 code) have similar time-frequency distributions, and it is difficult to identify such signals using traditional timefrequency analysis methods.To solve this problem, this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and timefrequency analysis.First of all, fractional Fourier transform and the Wigner-Ville distribution (WVD) are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next, the standard deviation of the target component's width in the signal's WVD is calculated.Finally, an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17% under 0 dB.When the training data set and the test data set are mixed with noise, the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.