Reviewerly/exHarmony huggingface dataset:
Lithium Potential Mapping Using Artificial Neural Networks: A Case Study from Central Portugal

The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification. They can be cost-effectively applied in various geological settings. This article presents an integrated approach of Li exploration targeting using ANNs for data interpretation. Based on medium resolution geological maps (1:50,000) and stream sediment geochemical data (1 sample per 0.25 km2), the Li potential was calculated for an area of approximately 1200 km2 in the surroundings of Bajoca Mine (Northeast Portugal). Extensive knowledge about geological processes leading to Li mineralisation (such as weathering conditions and diverse Li minerals) proved to be a determining factor in the exploration model. Furthermore, Sentinel-2 satellite imagery was used in a separate ANN model to identify potential Li mine sites exposed on the ground surface by analysing the spectral signature of surface reflectance in well-known Li locations. Finally, the results were combined to design a final map of predicted Li mineralisation occurrences in the study area. The proposed approach reveals how remote sensing data in combination with geological and geochemical data can be used for delineating and ranking exploration targets of almost any deposit type.
Bayesian estimation for collisional thermometry

Quantum thermometry exploits the high level of control in coherent devices to offer enhanced precision for temperature estimation. This highlights the need for constructing concrete estimation strategies. Of particular importance is collisional thermometry, where a series of ancillas are sent sequentially to probe the system's temperature. In this paper we put forth a complete framework for analyzing collisional thermometry using Bayesian inference. The approach is easily implementable and experimentally friendly. Moreover, it is guaranteed to always saturate the Cramer-Rao bound in the long-time limit. Subtleties concerning the prior information about the system's temperature are also discussed, and analyzed in terms of a modified Cramer-Rao bound associated to Van Trees and Sch\"utzenberger.
DUMA: Reading Comprehension With Transposition Thinking

Multi-choice Machine Reading Comprehension (MRC) requires models to decide the correct answer from a set of answer options when given a passage and a question. Thus, in addition to a powerful Pre-trained Language Model (PrLM) as an encoder, multi-choice MRC especially relies on a matching network design that is supposed to effectively capture the relationships among the triplet of passage, question, and answers. While the newer and more powerful PrLMs have shown their strengths even without the support from a matching network, we propose a new <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DU</b> al <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti-head Co- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ttention (DUMA) model. It is inspired by the human transposition thinking process solving the multi-choice MRC problem by considering each other’s focus from the standpoint of passage and question. The proposed DUMA has been shown to be effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM, and RACE. Our results show that in terms of powerful PrLMs, DUMA can further boost the models to obtain higher performance.
Variational Principle for Optimal Quantum Controls in Quantum Metrology

We develop a variational principle to determine the quantum controls and initial state that optimizes the quantum Fisher information, the quantity characterizing the precision in quantum metrology. When the set of available controls is limited, the exact optimal initial state and the optimal controls are, in general, dependent on the probe time, a feature missing in the unrestricted case. Yet, for time-independent Hamiltonians with restricted controls, the problem can be approximately reduced to the unconstrained case via Floquet engineering. In particular, we find for magnetometry with a time-independent spin chain containing three-body interactions, even when the controls are restricted to one- and two-body interaction, that the Heisenberg scaling can still be approximately achieved. Our results open the door to investigate quantum metrology under a limited set of available controls, of relevance to many-body quantum metrology in realistic scenarios.
Static inference meets deep learning

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type inference based on both static inference and neural predictions has not been exploited and remains an open challenge. In particular, it is hard to integrate DL models into the framework of rule-based static approaches.
TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems

Nowadays, intrusion detection systems based on deep learning deliver state-of-the-art performance. However, recent research has shown that specially crafted perturbations, called adversarial examples, are capable of significantly reducing the performance of these intrusion detection systems. The objective of this paper is to design an efficient transfer learning-based adversarial detector and then to assess the effectiveness of using multiple strategically placed adversarial detectors compared to a single adversarial detector for intrusion detection systems. In our experiments, we implement existing state-of-the-art models for intrusion detection. We then attack those models with a set of chosen evasion attacks. In an attempt to detect those adversarial attacks, we design and implement multiple transfer learning-based adversarial detectors, each receiving a subset of the information passed through the IDS. By combining their respective decisions, we illustrate that combining multiple detectors can further improve the detectability of adversarial traffic compared to a single detector in the case of a parallel IDS design.
Native qudit entanglement in a trapped ion quantum processor

Quantum information carriers, just like most physical systems, naturally occupy high-dimensional Hilbert spaces. Instead of restricting them to a two-level subspace, these high-dimensional (qudit) quantum systems are emerging as a powerful resource for the next generation of quantum processors. Yet harnessing the potential of these systems requires efficient ways of generating the desired interaction between them. Here, we experimentally demonstrate an implementation of a native two-qudit entangling gate in a trapped-ion qudit system up to dimension $5$. This is achieved by generalizing a recently proposed light-shift gate mechanism to generate genuine qudit entanglement in a single application of the gate. The gate seamlessly adapts to the local dimension of the system with a calibration overhead that is independent of the dimension.
Feature elimination in kernel machines in moderately high dimensions

We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features. We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions. We present a few case studies to show that the assumptions are met in most practical situations and present simulation results to demonstrate performance of the proposed approach.
Game Theoretical Analysis on Acceptance of a Cloud Data Access Control System Based on Reputation

With the rapid development of the Internet, cloud storage has penetrated into every aspect of human society. However, cloud data disclosure happens more and more frequently, which makes cloud data security and privacy protection impact wide adoption of cloud storage. Control cloud data access based on reputation by introducing a Reputation Center (RC) was proposed and demonstrated to secure cloud data effectively in [9] . But the acceptance of such a system by cloud users and Cloud Service Providers (CSPs) is crucial for its practical deployment and final success. In this paper, we investigate the acceptance of a cloud data access control system based on reputation using Game Theory. Due to the existence of dishonest CSPs, there exists a social reputation dilemma among CSPs, which seriously impedes the popularity of cloud storage. To encourage users to use cloud storage and suppress collusion between CSPs and data requesters, a repeated public-goods game is built up by applying a compensation mechanism to improve the utilities of cloud users and a punishment mechanism based on reputation to incent honest behaviors. Theoretical analysis and simulation results show the effectiveness of the compensation and punishment mechanisms to increase cloud storage rate and restrain dishonest system entities.
Computational Experiment-Based Evaluation on Context-Aware O2O Service Recommendation

O2O (online to offline) service recommendation is a typical context-aware service application, which needs to provide the most suitable services to customers in time according to their user profile and current context. By means of composing various data sources, different O2O service recommendation strategies can be customized, which may lead to great performance difference. Incorrect or non-real-time service recommendation would not work well and even cause negatives consequences. As a result, how to evaluate the performance of different O2O service recommendation strategies and select the most suitable one has become a key problem in the field. Due to the diversity and the variability of context events, as well as the economic, legal, and ethical impact, it is difficult or even impossible for traditional methods to realize comprehensive evaluation of various service strategies. Based on the background, this paper proposes a computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O2O service strategies, modeling of experiment system, and execution of experiment evaluation. As a case study, the method was applied to Food O2O service. Three kinds of service strategies were compared respectively under two different market environments. Experiment results show that the proposed evaluation method is effective.
Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds

To meet the dynamic workload requirements in widespread task-batch based workflow applications, we developed the cloud YARN (C-YARN) architecture by integrating the YARN platform with cloud computing.The C-YARN could provision flexible resources.In terms of depths and functions, tasks of different task-batches were merged to task-units.Based on task-units, a unit-aware deadline division method was investigated for properly dividing workflow deadlines to task deadlines to minimize the utilization of rented intervals.Considering different factors affecting time slot allocation, several rules were introduced for scheduling tasks with the task deadlines.A rule-based task scheduling method was presented for allocating tasks to time slots of rented Virtual Machines (VMs) with a task right shifting operation and a weighted priority composite rule.A Unit-aware Rule-based Heuristic (URH) was proposed for elastically provisioning VMs to task-batch based workflows to minimize the rental cost in C-YARN.Effectiveness of the proposed URH methods was verified by comparing them against two adapted existing algorithms for similar problems on some realistic workflows.
The computational landscape of general physical theories

There is good evidence that quantum computers are more powerful than classical computers, and that various simple modifications of quantum theory yield computational power that is dramatically greater still. However, these modifications also violate fundamental physical principles. This raises the question of whether there exists a physical theory, allowing computation more powerful than quantum, but which still respects those fundamental physical principles. Prior work by two of us introduced this question within a suitable framework for theories that make good operational sense, and showed that in any theory satisfying tomographic locality, the class of problems that can be solved efficiently is contained in the complexity class AWPP. Here, we show that this bound is tight, in the sense that there exists a theory, satisfying tomographic locality, as well as a basic principle of causality, which can efficiently decide everything in AWPP. Hence this theory can efficiently simulate any computation in this framework, including quantum computation.
Predicting Social Emotions from Readers’ Perspective

Due to the rapid development of Web, large numbers of documents assigned by readers' emotions have been generated through new portals. Comparing to the previous studies which focused on author's perspective, our research focuses on readers' emotions invoked by news articles. Our research provides meaningful assistance in social media application such as sentiment retrieval, opinion summarization and election prediction. In this paper, we predict the readers' emotion of news based on the social opinion network. More specifically, we construct the opinion network based on the semantic distance. The communities in the news network indicate specific events which are related to the emotions. Therefore, the opinion network serves as the lexicon between events and corresponding emotions. We leverage neighbor relationship in network to predict readers' emotions. As a result, our methods obtain better result than the state-of-the-art methods. Moreover, we developed a growing strategy to prune the network for practical application. The experiment verifies the rationality of the reduction for application.
Fast Reduced Set-Based Exemplar Finding and Cluster Assignment

As a fundamental step in various data analysis, exemplar-based clustering aims at clustering data by identifying representative samples as exemplars of the obtained groups. In this paper, a new fast exemplar-based clustering approach is proposed for a dataset with an arbitrary shape and number of clusters. The proposed approach begins with the reduced set of a dataset, which is a condensation of the dataset obtained by the well-developed kernel density estimators reduced set density estimator or fast reduced set density estimator, and then enters into its two advantageous stages: 1) fast exemplar finding (FEF) and 2) fast cluster assignment. The idea of the proposed approach has its basis in three assumptions: 1) exemplars should come from high-density samples; 2) exemplars should be either the components of the reduced set or their neighbors with high similarities; and 3) clusters can be diffused by surrounding both exemplars and its labeled reduced set. We theoretically analyze the proposed FEF from the perspective of the generalization performance of clustering and demonstrate the power of the proposed approach on several benchmarking datasets.
Demonstration of the no-hiding theorem on the 5-Qubit IBM quantum computer in a category-theoretic framework

Quantum no-Hiding theorem, first proposed by Braunstein and Pati [Phys. Rev. Lett. 98, 080502 (2007)], was verified experimentally by Samal et al. [Phys. Rev. Lett. 186, 080401 (2011)] using NMR quantum processor. Till then, this fundamental test has not been explored in any of the experimental architecture. Here, we demonstrate the above no-hiding theorem using the IBM 5Q quantum processor. Categorical algebra developed by Coecke and Duncan [New J. Phys. 13, 043016 (2011)] has been used for better visualization of the no-hiding theorem by analyzing the quantum circuit using the ZX calculus. The experimental results confirm the recovery of missing information by the application of local unitary operations on the ancillary qubits.
TransGate: Knowledge Graph Embedding with Shared Gate Structure

Embedding knowledge graphs (KGs) into continuous vector space is an essential problem in knowledge extraction. Current models continue to improve embedding by focusing on discriminating relation-specific information from entities with increasingly complex feature engineering. We noted that they ignored the inherent relevance between relations and tried to learn unique discriminate parameter set for each relation. Thus, these models potentially suffer from high time complexity and large parameters, preventing them from efficiently applying on real-world KGs. In this paper, we follow the thought of parameter sharing to simultaneously learn more expressive features, reduce parameters and avoid complex feature engineering. Based on gate structure from LSTM, we propose a novel model TransGate and develop shared discriminate mechanism, resulting in almost same space complexity as indiscriminate models. Furthermore, to develop a more effective and scalable model, we reconstruct the gate with weight vectors making our method has comparative time complexity against indiscriminate model. We conduct extensive experiments on link prediction and triplets classification. Experiments show that TransGate not only outperforms state-of-art baselines, but also reduces parameters greatly. For example, TransGate outperforms ConvE and RGCN with 6x and 17x fewer parameters, respectively. These results indicate that parameter sharing is a superior way to further optimize embedding and TransGate finds a better trade-off between complexity and expressivity.
Healthcare cost prediction: Leveraging fine-grain temporal patterns

To design and assess a method to leverage individuals' temporal data for predicting their healthcare cost. To achieve this goal, we first used patients' temporal data in their fine-grain form as opposed to coarse-grain form. Second, we devised novel spike detection features to extract temporal patterns that improve the performance of cost prediction. Third, we evaluated the effectiveness of different types of temporal features based on cost information, visit information and medical information for the prediction task. We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where the first two years were used to build the model to predict the costs in the third year. To prepare the data for modeling and prediction, the time series data of cost, visit and medical information were extracted in the form of fine-grain features (i.e., segmenting each time series into a sequence of consecutive windows and representing each window by various statistics such as sum). Then, temporal patterns of the time series were extracted and added to fine-grain features using a novel set of spike detection features (i.e., the fluctuation of data points). Gradient Boosting was applied on the final set of extracted features. Moreover, the contribution of each type of data (i.e., cost, visit and medical) was assessed. We benchmarked the proposed predictors against extant methods including those that used coarse-grain features which represent each time series with various statistics such as sum and the most recent portion of the values in the entire series. All prediction performances were measured in terms of Mean Absolute Percentage Error (MAPE). Gradient Boosting applied on fine-grain predictors outperformed coarse-grain predictors with a MAPE of 3.02 versus 8.14 (p < 0.01). Enhancing the fine-grain features with the temporal pattern extraction features (i.e., spike detection features) further improved the MAPE to 2.04 (p < 0.01). Removing cost, visit and medical status data resulted in MAPEs of 10.24, 2.22 and 2.07 respectively (p < 0.01 for the first two comparisons and p = 0.63 for the third comparison). Leveraging fine-grain temporal patterns for healthcare cost prediction significantly improves prediction performance. Enhancing fine-grain features with extraction of temporal cost and visit patterns significantly improved the performance. However, medical features did not have a significant effect on prediction performance. Gradient Boosting outperformed all other prediction models.
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-point regression. We evaluate the transferability of the learned representations to the task of ultrasound standard plane detection in two contexts. Firstly, we perform transfer learning by fine-tuning the CNN with a limited number of labeled standard plane images. We find that fine-tuning the saliency predictor is superior to training from random initialization, with an average F1-score improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters. We find that the attention models derive strong representations, approaching the precision of a fully-supervised baseline model for all but the last layer.
New chaotical image encryption algorithm based on Fisher–Yatess scrambling and DNA coding

Based on the Fisher–Yatess scrambling and DNA coding technology, a chaotical image encryption method is proposed. First, the SHA-3 algorithm is used to calculate the hash value of the initial password, which is used as the initial value of the chaotic system. Second, the chaotic sequence and Fisher–Yatess scrambling are used to scramble the plaintext, and a sorting scrambling algorithm is used for secondary scrambling. Then, the chaotic sequence and DNA coding rules are used to change the plaintext pixel values, which makes the ciphertext more random and resistant to attacks, and thus ensures that the encrypted ciphertext is more secure. Finally, we add plaintext statistics for pixel-level diffusion to ensure plaintext sensitivity. The experimental results and security analysis show that the new algorithm has a good encryption effect and speed, and can also resist common attacks.