Reviewerly/exHarmony huggingface dataset:
Representations of hypergraph states with neural networks*

The quantum many-body problem (QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers. With the rapid development of machine learning, artificial neural networks provide a powerful tool that can represent or approximate quantum many-body states. In this paper, we aim to explicitly construct the neural network representations of hypergraph states. We construct the neural network representations for any k-uniform hypergraph state and any hypergraph state, respectively, without stochastic optimization of the network parameters. Our method constructively shows that all hypergraph states can be represented precisely by the appropriate neural networks introduced in [Science 355 (2017) 602] and formulated in [Sci. China-Phys. Mech. Astron. 63 (2020) 210312].
Non-malleable Commitments Against Quantum Attacks

We construct, under standard hardness assumptions, the first non-malleable commitments secure against quantum attacks. Our commitments are statistically binding and satisfy the standard notion of non-malleability with respect to commitment. We obtain a $$\log ^\star (\lambda )$$ -round classical protocol, assuming the existence of post-quantum one-way functions. Previously, non-malleable commitments with quantum security were only known against a restricted class of adversaries known as synchronizing adversaries. At the heart of our results is a new general technique that allows to modularly obtain non-malleable commitments from any extractable commitment protocol, obliviously of the underlying extraction strategy (black-box or non-black-box) or round complexity. The transformation may also be of interest in the classical setting.
Gottesman Types for Quantum Programs

The Heisenberg representation of quantum operators provides a powerful technique for reasoning about quantum circuits, albeit those restricted to the common (non-universal) Clifford set H, S and CNOT .The Gottesman-Knill theorem showed that we can use this representation to efficiently simulate Clifford circuits.We show that Gottesman's semantics for quantum programs can be treated as a type system, allowing us to efficiently characterize a common subset of quantum programs.We also show that it can be extended beyond the Clifford set to partially characterize a broad range of programs.We apply these types to reason about separable states and the superdense coding algorithm.
Putting a Spin on Language: A Quantum Interpretation of Unary Connectives for Linguistic Applications

Extended versions of the Lambek Calculus currently used in computational linguistics rely on unary modalities to allow for the controlled application of structural rules affecting word order and phrase structure. These controlled structural operations give rise to derivational ambiguities that are missed by the original Lambek Calculus or its pregroup simplification. Proposals for compositional interpretation of extended Lambek Calculus in the compact closed category of FVect and linear maps have been made, but in these proposals the syntax-semantics mapping ignores the control modalities, effectively restricting their role to the syntax. Our aim is to turn the modalities into first-class citizens of the vectorial interpretation. Building on the directional density matrix semantics, we extend the interpretation of the type system with an extra spin density matrix space. The interpretation of proofs then results in ambiguous derivations being tensored with orthogonal spin states. Our method introduces a way of simultaneously representing co-existing interpretations of ambiguous utterances, and provides a uniform framework for the integration of lexical and derivational ambiguity.
Inverse Kinematics of Robot Manipulator Integrated with Image Processing Algorithms

Computer vision has many applications in various fields, such as remote sensing, face detection, and fingerprint detection. In this paper, various algorithms for motion detection, hazmat detection, and QR code detection are presented. These algorithms are implemented in OpenCV which are ROS-integrated. A camera is set up on a robot with a 5 degrees of freedom (DOF) manipulator. This robot is sent to remote locations and can gather information about the environment. This information can be the type of hazmat detected, the coordinates of the hazmat signs, etc. An inverse kinematic model of the arm is presented. A simulation of the arm configurations based on the coordinates obtained from the camera has been done using MATLAB.
Skeleton-Based Interactive Fabrication for Large-Scale Newspaper Sculpture

In this work, we propose a large-scale fabrication technique that uses newspapers with accessible and tractable properties. The system utilizes an interactive guidance system allowing common users to achieve large-scale newspaper sculptures from target 3D models. To maintain the fabrication shape, the proposed system provides an automatic design approach to create skeleton structures, which are formed from multiple chopsticks using the specialized connectors with 3D printing. Our system can support users in building complex newspaper sculptures. The interactive projection guidance is provided to visualize the differences between the depth maps of the target model and the current work so that the users can overlap the newspapers where needed. We evaluate the usefulness of the guidance system for fabrication support and the fabrication process with the proposed skeleton structure in our user study.
Implementation of Knowledge Management in Different Industries

Customer knowledge is a valuable asset, and gathering, managing, and sharing customer knowledge can be a useful competitive activity for organizations. Successful knowledge partnerships with essential and valuable customers can strengthen business performance and create an absolute competitive advantage that is difficult for competitors to emulate. In consumer knowledge management, companies often experience significant obstacles or constraints so that sometimes many companies prevent them from managing consumer knowledge. The company or organization depends on consumers who do not need to understand the industrial field of the company or organization. This paper discusses the comparison of the application of customer knowledge management in various industries. Research findings that all organizations must apply the three essential components of customer-related knowledge within the underlying conceptual framework. Three basic components of customer-related knowledge within a basic conceptual framework: knowledge for customers, knowledge of customers, and customer knowledge, cannot be eliminated. This proves the compatibility with previous studies. In fact, to succeed, they must be applied so that it is of value to the company. Implementation of the framework can present a great opportunity for the organization or company to make new products so that this will improve the performance of the company or organization and maintain the continuity of the company or organization.
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition

We propose a cross-modal transformer-based neural correction models that refines the output of an automatic speech recognition (ASR) system so as to exclude ASR errors. Generally, neural correction models are composed of encoder-decoder networks, which can directly model sequence-to-sequence mapping problems. The most successful method is to use both input speech and its ASR output text as the input contexts for the encoder-decoder networks. However, the conventional method cannot take into account the relationships between these two different modal inputs because the input contexts are separately encoded for each modal. To effectively leverage the correlated information between the two different modal inputs, our proposed models encode two different contexts jointly on the basis of cross-modal self-attention using a transformer. We expect that cross-modal self-attention can effectively capture the relationships between two different modals for refining ASR hypotheses. We also introduce a shallow fusion technique to efficiently integrate the first-pass ASR model and our proposed neural correction model. Experiments on Japanese natural language ASR tasks demonstrated that our proposed models achieve better ASR performance than conventional neural correction models.
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa.The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence.However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit.As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection.In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately.Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict.Experiments on political debates (i.e., IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition.Our code is released to support research 1 .
Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network

Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for training deep neural networks for classiﬁcation of breast cancer in histopathological images was developed in this study. However, this images are aﬀected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low conﬁdence. A threshold that decays over iteration number is used to decide which high conﬁdence samples should be concatenated with manually labeled samples and then used inﬁne-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .&#x0D;
Research on the Design of Nutrition and Sports Health Management System Based on Artificial Intelligence

In recent years, in the wave of big data on the Internet, China’s artificial intelligence development has always been among the top in the world. With the popularization of big data, the Internet and artificial intelligence have been involved in various fields, and the combination of artificial intelligence and current health management has achieved remarkable results. In this case, this paper advocates to provide differentiated health management services with the support of relevant literature and discusses the necessity of developing artificial intelligence in professional health management. Explore the development and design concept of personalized sports management service based on the concept of artificial intelligence, system operation process, management stage development, etc. Researched the practical ways of national health management. Artificial intelligence has a profound impact on promoting the expansion and renewal of sports and health management services, improving the level of sports and health management and opening up national fitness.
Experimental Demonstration of Conjugate-Franson Interferometry

Franson interferometry is a well-known quantum measurement technique for probing photon-pair frequency correlations that is often used to certify time-energy entanglement. We demonstrate the complementary technique in the time basis, called conjugate-Franson interferometry, that measures photon-pair arrival-time correlations, thus providing a valuable addition to the quantum toolbox. We obtain a conjugate-Franson interference visibility of $96\pm 1$% without background subtraction for entangled photon pairs generated by spontaneous parametric down-conversion. Our measured result surpasses the quantum-classical threshold by 25 standard deviations and validates the conjugate-Franson interferometer (CFI) as an alternative method for certifying time-energy entanglement. Moreover, the CFI visibility is a function of the biphoton's joint temporal intensity and is therefore sensitive to that state's spectral phase variation, something which is not the case for Franson interferometry or Hong-Ou-Mandel interferometry. We highlight the CFI's utility by measuring its visibilities for two different biphoton states, one without and the other with spectral phase variation, and observing a 21% reduction in the CFI visibility for the latter. The CFI is potentially useful for applications in areas of photonic entanglement, quantum communications, and quantum networking.
Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.
Analysis of the feedback particle filter with diffusion map based approximation of the gain

Control-type particle filters have been receiving increasing attention over the last decade as a means of obtaining sample based approximations to the sequential Bayesian filtering problem in the nonlinear setting. Here we analyse one such type, namely the feedback particle filter and a recently proposed approximation of the associated gain function based on diffusion maps. The key purpose is to provide analytic insights on the form of the approximate gain, which are of interest in their own right. These are then used to establish a roadmap to obtaining well-posedness and convergence of the finite $N$ system to its mean field limit. A number of possible future research directions are also discussed.
Cold ion beam in a storage ring as a platform for large-scale quantum computers and simulators: Challenges and directions for research and development

The purpose of this paper is to evaluate the possibility of constructing a large-scale storage-ring-type ion-trap system capable of storing, cooling, and controlling a large number of ions as a platform for scalable quantum computing (QC) and quantum simulations (QS). In such a trap, the ions form a crystalline beam moving along a circular path with a constant velocity determined by the frequency and intensity of the cooling lasers. In this paper we consider a large leap forward in terms of the number of qubits, from fewer than 100 available in state-of-the-art linear ion-trap devices today to an order of 105 crystallized ions in the storage-ring setup. This new trap design unifies two different concepts: the storage rings of charged particles and the linear ion traps used for QC and mass spectrometry. In this paper we use the language of particle accelerators to discuss the ion state and dynamics. We outline the differences between the above concepts, analyze challenges of the large ring with a revolving beam of ions, and propose goals for the research and development required to enable future quantum computers with 1000 times more qubits than available today. The challenge of creating such a large-scale quantum system while maintaining the necessary coherence of the qubits and the high fidelity of quantum logic operations is significant. Performing analog quantum simulations may be an achievable initial goal for such a device. Quantum simulations of complex quantum systems will move forward both the fundamental science and the applied research. Nuclear and particle physics, many-body quantum systems, lattice gauge theories, and nuclear structure calculations are just a few examples in which a large-scale quantum simulation system would be a very powerful tool to move forward our understanding of nature.
Barriers for fast matrix multiplication from irreversibility

Determining the asymptotic algebraic complexity of matrix multiplication, succinctly represented by the matrix multiplication exponent $\omega$, is a central problem in algebraic complexity theory. The best upper bounds on $\omega$, leading to the state-of-the-art $\omega \leq 2.37..$, have been obtained via Strassen's laser method and its generalization by Coppersmith and Winograd. Recent barrier results show limitations for these and related approaches to improve the upper bound on $\omega$. We introduce a new and more general barrier, providing stronger limitations than in previous work. Concretely, we introduce the notion of irreversibility of a tensor, and we prove (in some precise sense) that any approach that uses an irreversible tensor in an intermediate step (e.g., as a starting tensor in the laser method) cannot give $\omega = 2$. In quantitative terms, we prove that the best upper bound achievable is at least twice the irreversibility of the intermediate tensor. The quantum functionals and Strassen support functionals give (so far, the best) lower bounds on irreversibility. We provide lower bounds on the irreversibility of key intermediate tensors, including the small and big Coppersmith--Winograd tensors, that improve limitations shown in previous work. Finally, we discuss barriers on the group-theoretic approach in terms of monomial irreversibility. -------------- A conference version of this paper appeared in the Proceedings of the 34th Computational Complexity Conference, 2019.
Teaching Multiple Inverse Reinforcement Learners

In this paper, we propose the first machine teaching algorithm for multiple inverse reinforcement learners. As our initial contribution, we formalize the problem of optimally teaching a sequential task to a heterogeneous class of learners. We then contribute a theoretical analysis of such problem, identifying conditions under which it is possible to conduct such teaching using the same demonstration for all learners. Our analysis shows that, contrary to other teaching problems, teaching a sequential task to a heterogeneous class of learners with a single demonstration may not be possible, as the differences between individual agents increase. We then contribute two algorithms that address the main difficulties identified by our theoretical analysis. The first algorithm, which we dub S plit T each , starts by teaching the class as a whole until all students have learned all that they can learn as a group; it then teaches each student individually, ensuring that all students are able to perfectly acquire the target task. The second approach, which we dub J oint T each , selects a single demonstration to be provided to the whole class so that all students learn the target task as well as a single demonstration allows. While S plit T each ensures optimal teaching at the cost of a bigger teaching effort, J oint T each ensures minimal effort, although the learners are not guaranteed to perfectly recover the target task. We conclude by illustrating our methods in several simulation domains. The simulation results agree with our theoretical findings, showcasing that indeed class teaching is not possible in the presence of heterogeneous students. At the same time, they also illustrate the main properties of our proposed algorithms: in all domains, S plit T each guarantees perfect teaching and, in terms of teaching effort, is always at least as good as individualized teaching (often better); on the other hand, J oint T each attains minimal teaching effort in all domains, even if sometimes it compromises the teaching performance.
Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.