Reviewerly/exHarmony huggingface dataset:
Entanglement breaking channels, stochastic matrices, and primitivity

We consider the important class of quantum operations (completely positive trace-preserving maps) called entanglement breaking channels. We show how every such channel induces stochastic matrix representations that have the same non-zero spectrum as the channel. We then use this to investigate when entanglement breaking channels are primitive, and prove this depends on primitivity of the matrix representations. This in turn leads to tight bounds on the primitivity index of entanglement breaking channels in terms of the primitivity index of the associated stochastic matrices. We also present examples and discuss open problems generated by the work.
AI in the Workplace: Exploring Chatbot Use and Users’ Emotions

The adoption of Artificial Intelligence (AI) applications in organisations is growing rapidly. In this study, we focus on Chatbots as one type of AI applications in the workplace. Chatbots differ from traditional organisational ICTs in many aspects including machine learning and exhibiting social presence. These characteristics motivate us to explore the role of emotions on chatbot use in the workplace. Following a case study approach and collecting rich qualitative data, the research identifies the different emotions involved in chatbot use in the workplace and their effect on employees’ use behaviour. The findings surprisingly highlight that excitement, hope and playfulness in addition to empathy towards the chatbot offset the negative emotions of frustration experienced when getting wrong results and propel users to continue their use. The social presence of the chatbot and its potential to learn infuses a more tolerant forgiving user behaviour towards the chatbot. The study theoretically contributes to the understanding of chatbot adoption and use in organisations and informs research into the adoption, use and design of this new class of technology. Further research is encouraged to take the findings of this study and test them on a large sample of employees.
Coherent states for a system of an electron moving in a plane

In this paper, we construct the coherent states for a system of an electron moving on plane in uniform external magnetic and electric fields. These coherent states are built in the context of both discrete and continuous spectra and satisfy the Gazeau-Klauder coherent states properties [1].
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation

Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby improving the model performance. Moreover, we design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency. Extensive experiments on the LiTS dataset have demonstrated the superiority of the proposed method.
Hardware-efficient random circuits to classify noise in a multiqubit system

In this work we extend a multi-qubit benchmarking technique known as the Binned Output Generation (BOG) in order to discriminate between coherent and incoherent noise sources in the multi-qubit regime. While methods exist to discriminate coherent from incoherent noise at the single and few-qubit level, these methods scale poorly beyond a few qubits or must make assumptions about the form of the noise. On the other end of the spectrum, system-level benchmarking techniques exist, but fail to discriminate between coherent and incoherent noise sources. We experimentally verify the BOG against Randomized Benchmarking (RB) (the industry standard benchmarking technique) in the two-qubit regime, then apply this technique to a six qubit linear chain, a regime currently inaccessible to RB. In this experiment we inject an instantaneous coherent Z-type noise on each qubit and demonstrate that the measured coherent noise scales correctly with the magnitude of the injected noise, while the measured incoherent noise remains unchanged as expected. This demonstrates a robust technique to measure coherent errors in a variety of hardware.
Scale-invariant representation of machine learning

The success of machine learning has resulted from its structured representation of data. Similar data have close internal representations as compressed codes for classification or emerged labels for clustering. We observe that the frequency of internal codes or labels follows power laws in both supervised and unsupervised learning models. This scale-invariant distribution implies that machine learning largely compresses frequent typical data, and simultaneously, differentiates many atypical data as outliers. In this study, we derive the process by which these power laws can naturally arise in machine learning. In terms of information theory, the scale-invariant representation corresponds to a maximally uncertain data grouping among possible representations that guarantee a given learning accuracy.
Exploiting anticommutation in Hamiltonian simulation

Quantum computing can efficiently simulate Hamiltonian dynamics of many-body quantum physics, a task that is generally intractable with classical computers. The hardness lies at the ubiquitous anti-commutative relations of quantum operators, in corresponding with the notorious negative sign problem in classical simulation. Intuitively, Hamiltonians with more commutative terms are also easier to simulate on a quantum computer, and anti-commutative relations generally cause more errors, such as in the product formula method. Here, we theoretically explore the role of anti-commutative relation in Hamiltonian simulation. We find that, contrary to our intuition, anti-commutative relations could also reduce the hardness of Hamiltonian simulation. Specifically, Hamiltonians with mutually anti-commutative terms are easy to simulate, as what happens with ones consisting of mutually commutative terms. Such a property is further utilized to reduce the algorithmic error or the gate complexity in the truncated Taylor series quantum algorithm for general problems. Moreover, we propose two modified linear combinations of unitaries methods tailored for Hamiltonians with different degrees of anti-commutation. We numerically verify that the proposed methods exploiting anti-commutative relations could significantly improve the simulation accuracy of electronic Hamiltonians. Our work sheds light on the roles of commutative and anti-commutative relations in simulating quantum systems.
An Analysis of Advance Level Students to Perform Their Speaking English

This research aimed to identify and to describe about the students’ problem when they speak English. Because the advanced level Student of University couldn’t speak English well when they perform their Proposal Seminar and also final examination at Institut Agama Islam Negeri Metro Lampung. The data were taken by interview with 20 students, and questionnaire. The participants were students of English study program. The result showed that most students from advanced level student couldn’t speak English well when they presented seminar proposal and final examination or munaqosyah. Because they were too worry about making mistakes on their performance and because they don’t have enough vocabulary and grammar mstery so it would give impact to their pronunciation and confidence.&#x0D; Keywords: Advance Students Speaking, Speaking Skill, Speaking Perform
THE EFFECTIVENESS OF NON-VERBAL COMMUNICATION IN TEACHING AND LEARNING ENGLISH: A SYSTEMATIC REVIEW

In the reviewed studies non-verbal communication used by teachers in teaching English. The purpose of this research is to study the type of Non-verbal communication by English teachers in teaching and to finding out how English teachers use non-verbal communication in teaching class, much further, whether to seek out the strength and weaknesses of the non-verbal communication, and to find out the effective way to use non-verbal communication efficiently and effectively. This research used a Quantitative approach which the effortsis to collect all of the studies that shared the common field of the study. The result of this study showed that the benefits,the advantages, the disadvantages and its effects. Moreover the non-verbal communicationgive some contributions in education which can be applied by teacher today. Recommendation and suggestion are offered in the current study. Hence,non-verbal communication is recommended to be used by teacher in this classroom.
The ZX&amp;-calculus: A complete graphical calculus for classical circuits using spiders

We give a complete presentation for the fragment, ZX&, of the ZX-calculus generated by the Z and X spiders (corresponding to copying and addition) along with the not gate and the and gate. To prove completeness, we freely add a unit and counit to the category TOF generated by the Toffoli gate and ancillary bits, showing that this yields the full subcategory of finite ordinals and functions with objects powers of two; and then perform a two way translation between this category and ZX&. A translation to some extension of TOF, as opposed to some fragment of the ZX-calculus, is a natural choice because of the multiplicative nature of the Toffoli gate. To this end, we show that freely adding counits to the semi-Frobenius algebras of a discrete inverse category is the same as constructing the Cartesian completion. In particular, for a discrete inverse category, the category of classical channels, the Cartesian completion and adding counits all produce the same category. Therefore, applying these constructions to TOF produces the full subcategory of finite ordinals and partial maps with objects powers of two. By glueing together the free counit completion and the free unit completion, this yields "qubit multirelations."
Generating adversarial examples without specifying a target model

Adversarial examples are regarded as a security threat to deep learning models, and there are many ways to generate them. However, most existing methods require the query authority of the target during their work. In a more practical situation, the attacker will be easily detected because of too many queries, and this problem is especially obvious under the black-box setting. To solve the problem, we propose the Attack Without a Target Model (AWTM). Our algorithm does not specify any target model in generating adversarial examples, so it does not need to query the target. Experimental results show that it achieved a maximum attack success rate of 81.78% in the MNIST data set and 87.99% in the CIFAR-10 data set. In addition, it has a low time cost because it is a GAN-based method.
A Logic-based Multi-agent System for Ethical Monitoring and Evaluation of Dialogues

Dialogue Systems are tools designed for various practical purposes concerning human-machine interaction. These systems should be built on ethical foundations because their behavior may heavily influence a user (think especially about children). The primary objective of this paper is to present the architecture and prototype implementation of a Multi Agent System (MAS) designed for ethical monitoring and evaluation of a dialogue system. A prototype application, for monitoring and evaluation of chatting agents' (human/artificial) ethical behavior in an online customer service chat point w.r.t their institution/company's codes of ethics and conduct, is developed and presented. Future work and open issues with this research are discussed.
Adding the Relation Meets to the Temporal Logic of Prefixes and Infixes makes it EXPSPACE-Complete

The choice of the right trade-off between expressiveness and complexity is the main issue in interval temporal logic. In their seminal paper, Halpern and Shoham showed that the satisfiability problem for HS (the temporal logic of Allen's relations) is highly undecidable over any reasonable class of linear orders. In order to recover decidability, one can restrict the set of temporal modalities and/or the class of models. In the following, we focus on the satisfiability problem for HS fragments under the homogeneity assumption, according to which any proposition letter holds over an interval if only if it holds at all its points. The problem for full HS with homogeneity has been shown to be non-elementarily decidable, but its only known lower bound is EXPSPACE (in fact, EXPSPACE-hardness has been shown for the logic of prefixes and suffixes BE, which is a very small fragment of it. The logic of prefixes and infixes BD has been recently shown to be PSPACE-complete. In this paper, we prove that the addition of the Allen relation Meets to BD makes it EXPSPACE-complete.
Human-Centered Software Engineering for Changing Contexts of Use

The context of use plays an important role in Human-Centered Software Engineering (HCSE). Typically, user, environment, and platform are considered to make up the core aspects of the context of use. Changing the context of use has significant impact on how to design and develop with a user-centered perspective and how usage of these systems changed for example in the current pandemic situation with a focus on remote work. New kinds of work environments in industry providing different types of user assistance evolve, new settings for remote and distributed work styles are required to account for social distancing, or the trends towards autonomous and AI-based systems demand for new ways of thinking about socio-technical interaction and systems. This workshop promotes sharing of knowledge and experiences that address how to deal with evolving contexts of use in today’s and future application domains and its influence on human-centered socio-technical system design and development practices. Beyond the traditional themes of IFIP Working Group 13.2 workshops, the main focus lies on theories, methods, and approaches for dealing with the context of use, its influence on HCSE, and factors for its change over time.
Generalized Poisson Hurdle Model for Count Data and Its Application in Ear Disease

For count data, though a zero-inflated model can work perfectly well with an excess of zeroes and the generalized Poisson model can tackle over- or under-dispersion, most models cannot simultaneously deal with both zero-inflated or zero-deflated data and over- or under-dispersion. Ear diseases are important in healthcare, and falls into this kind of count data. This paper introduces a generalized Poisson Hurdle model that work with count data of both too many/few zeroes and a sample variance not equal to the mean. To estimate parameters, we use the generalized method of moments. In addition, the asymptotic normality and efficiency of these estimators are established. Moreover, this model is applied to ear disease using data gained from the New South Wales Health Research Council in 1990. This model performs better than both the generalized Poisson model and the Hurdle model.
Harnessing Unrecognizable Faces for Improving Face Recognition

The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector. When the detector succeeds, it can detect faces that cannot be recognized, no matter how capable the recognition system is. Recognizability, a latent variable, should therefore be factored into the design and implementation of face recognition systems. We propose a measure of recognizability of a face image that leverages a key empirical observation: An embedding of face images, implemented by a deep neural network trained using mostly recognizable identities, induces a partition of the hypersphere whereby unrecognizable identities cluster together. This occurs regardless of the phenomenon that causes a face to be unrecognizable, be it optical or motion blur, partial occlusion, spatial quantization, or poor illumination. Therefore, we use the distance from such an "unrecognizable identity" as a measure of recognizability, and incorporate it into the design of the overall system. We show that accounting for recognizability reduces the error rate of single-image face recognition by 58% at FAR=1e-5 on the IJB-C Covariate Verification benchmark, and reduces the verification error rate by 24% at FAR=1e-5 in set-based recognition on the IJB-C benchmark.
Robust nonadiabatic holonomic quantum gates on decoherence-protected qubits

Obtaining high-fidelity and robust quantum gates is the key for scalable quantum computation, and one of the promising ways is to implement quantum gates using geometric phases, where the influence of local noises can be greatly reduced. To obtain robust quantum gates, we here propose a scheme for quantum manipulation by combining the geometric phase approach with the dynamical correction technique, where the imperfection control induced X-error can be greatly suppressed. Moreover, to be robust against the decoherence effect and the randomized qubit-frequency shift Z-error, our scheme is also proposed based on the polariton qubit, the eigenstates of the light-matter interaction, which is immune to both errors up to the second order, due to its near symmetric energy spectrum. Finally, our scheme is implemented on the superconducting circuits, which also simplifies previous implementations. Since the main errors can be greatly reduced in our proposal, it provides a promising strategy for scalable solid-state fault-tolerant quantum computation.
Experiences of Primary School with Students with Reading and Writing Difficulties

This study aims to examine in-depth the practices and the problems of primary school teachers in identifying and eliminating reading and writing difficulties. The current study employed phenomenology method, one of the qualitative research designs and the data of the study were collected through observation and interview techniques. Criterion sampling, one of the purposeful sampling methods was utilized in determining study group. Six primary school teachers were selected in accordance with the determined criteria to collect the interview data. To collect observation data, 3 primary schools were selected and a total of 16 hours of observation was made. According to the findings, most of the teachers did not receive education about identifying students with reading and writing difficulties, conducting studies with these students, and preparing materials, activities, and etc. suitable for them. Moreover, primary school teachers expressed that although there were some disadvantages of inclusive education, students with reading and writing difficulties should continue their education in inclusive classes. However, the necessity of providing supplementary support to these students in addition to inclusive education was frequently emphasized. When the practices of primary school teachers with students having reading and writing difficulty were examined, it was observed that they generally used letter/syllable trinkets, gamification, letter cards, individual syllable work, memorization assignments, dictation work, story reading, drama and colored syllables. On the other hand, it was determined that methods, techniques or strategies that stated to be effective in the literature were either used very little or not at all in classrooms.
A FACIAL EXPRESSION RECOGNITION METHOD USING LOCAL NONLINEAR FEATURES

The purposes are to recognize the facial expression information and establish a fast and accurate facial expression recognition algorithm. First, the noise caused by face image shooting is eliminated by analyzing the primary methods of face recognition and preprocessing the images. Then, the Nonlinear Support Vector Machine (NSVM) is combined with the decision tree to obtain the Nonlinear Support Vector Machine Decision Tree (NSMMD) algorithm. By introducing the binary tree, NSVM has the ability of multi-classification and can extract the feature information of the face image to obtain the characteristic face. Then, the algorithm flow is introduced. Second, the characteristic face data are recognized and classified based on Generalized Regression Neural Network (GRNN), and the expression data contained in the face images are obtained to recognize the expressions of the face image. Finally, the designed face recognition algorithm and facial expression recognition algorithm are tested. The results show that the designed NSMMD algorithm has a weak dependence on feature dimension, and the face recognition rate for different datasets is above 80.1%. The GRNN algorithm will be affected by smooth parameters, its optimal parameter value is 0.1, and its recognition rate of expressions on various databases is above 92%. Therefore, the designed face recognition algorithm and facial expression recognition algorithm can provide sound recognition effects, which can reference the study of facial image feature recognition.
Games for Hybrid Logic

Game semantics and winning strategies offer a potential conceptual bridge between semantics and proof systems of logics. We illustrate this link for hybrid logic – an extension of modal logic that allows for explicit reference to worlds within the language. The main result is that the systematic search of winning strategies over all models can be finitized and thus reformulated as a proof system.
Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity

Abstract Objective We develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared information between cancers and auxiliary class features, respectively, to boost performance using enriched annotations which give both location-based information and document level labels for each pathology report. Materials and Methods Our data consists of 250 pathology reports each for kidney, colon, and lung cancer from 2002 to 2019 from a single institution (UCSF). For each report, we classified 5 attributes: procedure, tumor location, histology, grade, and presence of lymphovascular invasion. We develop novel NLP techniques involving transfer learning and string similarity trained on enriched annotations. We compare HCTC and ZSS methods to the state-of-the-art including conventional machine learning methods as well as deep learning methods. Results For our HCTC method, we see an improvement of up to 0.1 micro-F1 score and 0.04 macro-F1 averaged across cancer and applicable attributes. For our ZSS method, we see an improvement of up to 0.26 micro-F1 and 0.23 macro-F1 averaged across cancer and applicable attributes. These comparisons are made after adjusting training data sizes to correct for the 20% increase in annotation time for enriched annotations compared to ordinary annotations. Conclusions Methods based on transfer learning across cancers and augmenting information methods with string similarity priors can significantly reduce the amount of labeled data needed for accurate information extraction from pathology reports.