Reviewerly/exHarmony huggingface dataset:
Erasure conversion in a high-fidelity Rydberg quantum simulator

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices1 and for the quest towards fault-tolerant quantum computation2,3. Rydberg arrays have emerged as a prominent platform in this context4 with impressive system sizes5,6 and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution7,8, a form of erasure error conversion9-12. However, two-qubit entanglement fidelities in Rydberg atom arrays13,14 have lagged behind competitors15,16 and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator5,6,17,18. When excising data with erasure errors observed via fast imaging of alkaline-earth atoms19-22, we achieve a Bell state fidelity of [Formula: see text], which improves to [Formula: see text] when correcting for remaining state-preparation errors. We further apply erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, and reveal the otherwise hidden impact of these errors on the simulation outcome. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the 0.999 regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices. These techniques could be translated directly to quantum-error-correction codes with the addition of long-lived qubits7,22-24.
CLIP-Driven Fine-Grained Text-Image Person Re-Identification

Text-Image Person Re-identification (TIReID) aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training to facilitate learning, but lack multi-modal correspondence information. Vision-Language Pre-training, such as CLIP (Contrastive Language-Image Pretraining), can address the limitation. However, CLIP falls short in capturing fine-grained information, thereby not fully leveraging its powerful capacity in TIReID. Besides, the popular explicit local matching paradigm for mining fine-grained information heavily relies on the quality of local parts and cross-modal inter-part interaction/guidance, leading to intra-modal information distortion and ambiguity problems. Accordingly, in this paper, we propose a CLIP-driven Fine-grained information excavation framework (CFine) to fully utilize the powerful knowledge of CLIP for TIReID. To transfer the multi-modal knowledge effectively, we conduct fine-grained information excavation to mine modality-shared discriminative details for global alignment. Specifically, we propose a multi-level global feature learning (MGF) module that fully mines the discriminative local information within each modality, thereby emphasizing identity-related discriminative clues through enhanced interaction between global image (text) and informative local patches (words). MGF generates a set of enhanced global features for later inference. Furthermore, we design cross-grained feature refinement (CFR) and fine-grained correspondence discovery (FCD) modules to establish cross-modal correspondence at both coarse and fine-grained levels (image-word, sentence-patch, word-patch), ensuring the reliability of informative local patches/words. CFR and FCD are removed during inference to optimize computational efficiency. Extensive experiments on multiple benchmarks demonstrate the superior performance of our method in TIReID.
Automatic genre identification: a survey

Abstract Automatic genre identification (AGI) is a text classification task focused on genres, i.e., text categories defined by the author’s purpose, common function of the text, and the text’s conventional form. Obtaining genre information has been shown to be beneficial for a wide range of disciplines, including linguistics, corpus linguistics, computational linguistics, natural language processing, information retrieval and information security. Consequently, in the past 20 years, numerous researchers have collected genre datasets with the aim to develop an efficient genre classifier. However, their approaches to the definition of genre schemata, data collection and manual annotation vary substantially, resulting in significantly different datasets. As most AGI experiments are dataset-dependent, a sufficient understanding of the differences between the available genre datasets is of great importance for the researchers venturing into this area. In this paper, we present a detailed overview of different approaches to each of the steps of the AGI task, from the definition of the genre concept and the genre schema, to the dataset collection and annotation methods, and, finally, to machine learning strategies. Special focus is dedicated to the description of the most relevant genre schemata and datasets, and details on the availability of all of the datasets are provided. In addition, the paper presents the recent advances in machine learning approaches to automatic genre identification, and concludes with proposing the directions towards developing a stable multilingual genre classifier.
Seven Pillars for the Future of Artificial Intelligence

In recent years, artificial intelligence (AI) research has showcased tremendous potential to positively impact humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision making, sense disambiguation, sarcasm detection, and narrative understanding as these require advanced kinds of reasoning, e.g., common-sense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely, multidisciplinarity, task decomposition, parallel analogy, symbol grounding, similarity measure, intention awareness, and trustworthiness.
AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at https://github.com/gyyang23/AFPN.
Causal discovery from sequential data in ALS disease based on entropy criteria

One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters in a natural phenomenon can be accomplished through checking the parameters' changes in consecutive events. In addition, using information and probabilistic theory help better conception of causal relationships of a phenomenon. Therefore, probabilistic causal discovery from sequential data of a natural phenomenon can be useful for dimension reduction and predicting the future trend of a process. In this paper, we introduce a novel method for causal discovery from a sequential data based on a probabilistic causal graph. In this method, first, Causal Feature Dependency matrix (CFD matrix) is generated based on the features' changes in consecutive events. Then, a probabilistic causal graph is created from CFD matrix. In this graph, some valueless features will be eliminated on the basis of entropy value of each conditional density function. Finally, prediction operation is performed based on the output of causal graph. Experimental results on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) sequential data set from Amyotrophic Lateral Sclerosis (ALS) disease show that our proposed algorithm can predict the progression rate of ALS disease properly with high precision.
Tracking Sparse Linear Classifiers

In this paper, we investigate the problem of sparse online linear classification in changing environments. We first analyze the tracking performance of standard online linear classifiers, which use gradient descent for minimizing the regularized hinge loss. The derived shifting bounds highlight the importance of choosing appropriate step sizes in the presence of concept drifts. Notably, we show that a better adaptability to concept drifts can be achieved using constant step sizes rather than the state-of-the-art decreasing step sizes. Based on these observations, we then propose a novel sparse approximated linear classifier, called sparse approximated linear classification (SALC), which uses a constant step size. In essence, SALC simply rounds small weights to zero for achieving sparsity and controls the truncation error in a principled way for achieving a low tracking regret. The degree of sparsity obtained by SALC is continuous and can be controlled by a parameter which captures the tradeoff between the sparsity of the model and the regret performance of the algorithm. Experiments on nine stationary data sets show that SALC is superior to the state-of-the-art sparse online learning algorithms, especially when the solution is required to be sparse; on seven groups of nonstationary data sets with various total shifting amounts, SALC also presents a good ability to track drifts. When wrapped with a drift detector, SALC achieves a remarkable tracking performance regardless of the total shifting amount.
OMeta: an ontology-based, data-driven metadata tracking system

The development of high-throughput sequencing and analysis has accelerated multi-omics studies of thousands of microbial species, metagenomes, and infectious disease pathogens. Omics studies are enabling genotype-phenotype association studies which identify genetic determinants of pathogen virulence and drug resistance, as well as phylogenetic studies designed to track the origin and spread of disease outbreaks. These omics studies are complex and often employ multiple assay technologies including genomics, metagenomics, transcriptomics, proteomics, and metabolomics. To maximize the impact of omics studies, it is essential that data be accompanied by detailed contextual metadata (e.g., specimen, spatial-temporal, phenotypic characteristics) in clear, organized, and consistent formats. Over the years, many metadata standards developed by various metadata standards initiatives have arisen; the Genomic Standards Consortium's minimal information standards (MIxS), the GSCID/BRC Project and Sample Application Standard. Some tools exist for tracking metadata, but they do not provide event based capabilities to configure, collect, validate, and distribute metadata. To address this gap in the scientific community, an event based data-driven application, OMeta, was created that allows users to quickly configure, collect, validate, distribute, and integrate metadata.A data-driven web application, OMeta, has been developed for use by researchers consisting of a browser-based interface, a command-line interface (CLI), and server-side components that provide an intuitive platform for configuring, capturing, viewing, and sharing metadata. Project and sample metadata can be set based on existing standards or based on projects goals. Recorded information includes details on the biological samples, procedures, protocols, and experimental technologies, etc. This information can be organized based on events, including sample collection, sample quantification, sequencing assay, and analysis results. OMeta enables configuration in various presentation types: checkbox, file, drop-box, ontology, and fields can be configured to use the National Center for Biomedical Ontology (NCBO), a biomedical ontology server. Furthermore, OMeta maintains a complete audit trail of all changes made by users and allows metadata export in comma separated value (CSV) format for convenient deposition of data into public databases.We present, OMeta, a web-based software application that is built on data-driven principles for configuring and customizing data standards, capturing, curating, and sharing metadata.
Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.
KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale

Understanding the privacy relevant characteristics of data sets, such as reidentifiability and joinability, is crucial for data governance, yet can be difficult for large data sets. While computing the data characteristics by brute force is straightforward, the scale of systems and data collected by large organizations demands an efficient approach. We present KHyperLogLog (KHLL), an algorithm based on approximate counting techniques that can estimate the reidentifiability and joinability risks of very large databases using linear runtime and minimal memory. KHLL enables one to measure reidentifiability of data quantitatively, rather than based on expert judgement or manual reviews. Meanwhile, joinability analysis using KHLL helps ensure the separation of pseudonymous and identified data sets. We describe how organizations can use KHLL to improve protection of user privacy. The efficiency of KHLL allows one to schedule periodic analyses that detect any deviations from the expected risks over time as a regression test for privacy. We validate the performance and accuracy of KHLL through experiments using proprietary and publicly available data sets.
Motion Planning of Autonomous Mobile Robot Using Recurrent Fuzzy Neural Network Trained by Extended Kalman Filter

This paper proposes a novel motion planning method for an autonomous ground mobile robot to address dynamic surroundings, nonlinear program, and robust optimization problems. A planner based on the recurrent fuzzy neural network (RFNN) is designed to program trajectory and motion of mobile robots to reach target. And, obstacle avoidance is achieved. In RFNN, inference capability of fuzzy logic and learning capability of neural network are combined to improve nonlinear programming performance. A recurrent frame with self-feedback loops in RFNN enhances stability and robustness of the structure. The extended Kalman filter (EKF) is designed to train weights of RFNN considering the kinematic constraint of autonomous mobile robots as well as target and obstacle constraints. EKF's characteristics of fast convergence and little limit in training data make it suitable to train the weights in real time. Convergence of the training process is also analyzed in this paper. Optimization technique and update strategy are designed to improve the robust optimization of a system in dynamic surroundings. Simulation experiment and hardware experiment are implemented to prove the effectiveness of the proposed method. Hardware experiment is carried out on a tracked mobile robot. An omnidirectional vision is used to locate the robot in the surroundings. Forecast improvement of the proposed method is then discussed at the end.
Application of interval-valued aggregation to optimization problem of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si7.gif" overflow="scroll"><mml:mrow><mml:mi>k</mml:mi><mml:mspace width="0.16em" /><mml:mo>−</mml:mo><mml:mspace width="0.16em" /><mml:mi>N</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:math>classifiers for missing values case

In this contribution we consider methods for improving the quality of classification by the k nearest neighborhood classifiers in the case of large number of missing values. The classical version of such classifier is applied, moreover the classifier with the aggregation of certainty coefficients of the individual classifiers with the use of the arithmetic mean, as well as new versions of the classifier with the use of diverse interval-valued aggregation functions are considered. After adding missing values to the data, the quality of the classical method decreases. By contrast, the interval modeling of missing values and application of interval-valued aggregation functions in the classifiers entail a much slower decrease of their quality. The paper includes the results of experiments performed on the data sets from the machine learning repository UCI.
The public sector can teach us a lot about digitizing customer service

Governments aren’t typically early adopters, but public agencies in Australia are taking the lead in using bots to improve services.
$HS^2$: Active learning over hypergraphs with pointwise and pairwise queries

We propose a hypergraph-based active learning scheme which we term HS2; HS2 generalizes the previously reported algorithm S2 originally proposed for graph-based active learning with pointwise queries. Our HS2 method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS2 for the above described generalized settings. Both the theoretical and empirical results show that HS2 requires a significantly fewer number of queries than S2 when one uses S2 over a graph obtained from the corresponding hypergraph via clique expansion.
From Facebook to the Streets: Russian Troll Ads and Black Lives Matter Protests

Online trolling is typically studied in the IS literature as an uncoordinated, anarchic activity.Coordinated, strategic online trolling is not well understood despite its prevalence on social media.To shed light on this prevailing activity, the present study examines the proposition that coordinated online trolling is timed to leverage macro societal unrest.In testing this proposition, we analyzed the dynamics of the Russian State's coordinated trolling campaign against the United States beginning in 2015.Using the May 2018 release of all Russian Troll Facebook advertisements, this study constructs a topic model of the content of these ads.The relationship between ad topics and the frequency of Black Lives Matter protests is examined.We argue that the frequency of Black Lives Matter protests proxies for civil unrest and divisiveness in the United States.The study finds that Russian ads related to police brutality were issued to coincide with periods of higher unrest.This work also finds that during periods of relative calm (evidenced by lower frequency of protests) Russian ads were relatively innocuous.
Pengembangan Media Pembelajaran Kimia Berbasis Android Untuk Penumbuhan Literasi Sains Siswa Pada Materi Reaksi Redoks Dan Elektrokimia

The aims of this study were to produce android base chemistrylearning media prototype and evaluate its elibibility andeffectiveness on student science literacy growing on redox reactionand electrochemistry subject material. In form of research anddevelopment, this study was carried out by ADDIE (Analysis,Design, Development, Implementation, dan Evalution)model ofinstructional development. Data was collected by content anddesign validation questionnaire, practitioner and student respondquestionnaire, and science litracy test. Data was analyzed byelegiibility percentega and categories and N-gain test. Study resultshowed that media design and content validity was very eleigiblewith 80 % of eligibility percentage. Practitioner and studentsrespond was very eleigible with 82.22 % and 86.35 % of eligibilitypercentage average respectively. This media effectiveness onstudent literacy growing was proven by N-gain value 0.65 % withfair category. It can be concluded that android base chemistrylearning media that developed was very eligible for chemistylearning application and grow student science literacy on redoxreaction and electrochemistry subject material effectively.
NEWSTRADCOIN: A Blockchain Based Privacy Preserving Secure NEWS Trading Network

To stay up to date with world issues and cutting-edge technologies, the newspaper plays a crucial role. However, collecting news is not a very easy task. Currently, news publishers are collecting news from their correspondents through social networks, email, phone call, fax, etc. and sometimes they buy news from the agencies. However, the existing news sharing networks may not provide security for data integrity and any third party may obstruct the regular flow of news sharing. Moreover, the existing news schemes are very vulnerable in case of disclosing the identity. Therefore, a universal platform is needed in the era of globalization where anyone can share and trade news from anywhere in the world securely, without the interference of third-party, and without disclosing the identity of an individual. Recently, blockchain has gained popularity because of its security mechanism over data, identity, etc. Blockchain enables a distributed way of managing transactions where each participant of the network holds the same copy of the transactions. Therefore, with the help of pseudonymity, fault-tolerance, immutability, and the distributed structure of blockchain, a scheme (termed as NEWSTRADCOIN) is presented in this paper in which not only news can be shared securely but also anyone can earn money by selling news. The proposed NEWSTRADCOIN can provide a universal platform where publishers can directly collect news from news-gatherers in a secure way by maintaining data integrity, without experiencing the interference of a third-party, and without disclosing the identity of the news gatherer and publishers.