Reviewerly/exHarmony huggingface dataset:
Adaptive complex-valued stepsize based fast learning of complex-valued neural networks

Complex-valued gradient descent algorithm is a popular tool to optimize functions of complex variables, especially for the training of complex-valued neural networks. However, the choice of suitable learning stepsize is a challenging task during the training process. In this paper, an adaptive complex-valued stepsize design method is proposed for complex-valued neural networks by generalizing the adaptable learning rate tree technique to the complex domain. The scaling and rotation factors are introduced to simultaneously adjust the amplitude and phase of complex-valued stepsize. The search range is thus expanded from half line to half plane such that better search direction is obtained at each iteration. We analyze the dynamics of the algorithm near a saddle point and find that it is very easy to escape from the saddle point to guarantee fast convergence and high accuracy. Some experimental results on function approximation and pattern classification tasks are presented to illustrate the advantages of the proposed algorithm over some previous ones.
Approximate Maximum Matching in Random Streams

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Approximate Maximum Matching in Random StreamsAlireza Farhadi, Mohammad Taghi Hajiaghayi, Tung Mah, Anup Rao, and Ryan A. RossiAlireza Farhadi, Mohammad Taghi Hajiaghayi, Tung Mah, Anup Rao, and Ryan A. Rossipp.1773 - 1785Chapter DOI:https://doi.org/10.1137/1.9781611975994.108PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this paper, we study the problem of finding a maximum matching in the semi-streaming model when edges arrive in a random order. In the semi-streaming model, an algorithm receives a stream of edges and it is allowed to have a memory of Õ(n)1 where n is the number of vertices in the graph. A recent inspiring work by Assadi et al. [1] shows that there exists a streaming algorithm with the approximation ratio of ⅔ that uses Õ(n1.5) memory. However, the memory of their algorithm is much larger than the memory constraint of the semi-streaming algorithms. In this work, we further investigate this problem in the semi-streaming model, and we present simple and clean algorithms for approximating maximum matching in the semi-streaming model. Our main results are as follows. We show that there exists a single-pass deterministic semi-streaming algorithm that finds a approximation of the maximum matching in bipartite graphs using Õ(n) memory. This result significantly outperforms the state-of-the-art result of Konrad [12] that finds a 0.539 approximation of the maximum matching using Õ(n) memory. By giving a black-box reduction from finding a matching in general graphs to finding a matching in bipartite graphs, we show there exists a single-pass deterministic semi-streaming algorithm that finds a (≈ 0.545) approximation of the maximum matching in general graphs, improving upon the state-of-art result 0.506 approximation by Gamlath et al. [8]. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Monomial-Cartesian codes and their duals, with applications to LCD codes, quantum codes, and locally recoverable codes

A monomial-Cartesian code is an evaluation code defined by evaluating a set of monomials over a Cartesian product. It is a generalization of some families of codes in the literature, for instance toric codes, affine Cartesian codes and $J$-affine variety codes. In this work we use the vanishing ideal of the Cartesian product to give a description of the dual of a monomial-Cartesian code. Then we use such description of the dual to prove the existence of quantum error correcting codes and MDS quantum error correcting codes. Finally we show that the direct product of monomial-Cartesian codes is a locally recoverable code with $t$-availability if at least $t$ of the components are locally recoverable codes.
Theory of Noise-Scaled Stability Bounds and Entanglement Rate Maximization in the Quantum Internet

Crucial problems of the quantum Internet are the derivation of stability properties of quantum repeaters and theory of entanglement rate maximization in an entangled network structure. The stability property of a quantum repeater entails that all incoming density matrices can be swapped with a target density matrix. The strong stability of a quantum repeater implies stable entanglement swapping with the boundness of stored density matrices in the quantum memory and the boundness of delays. Here, a theoretical framework of noise-scaled stability analysis and entanglement rate maximization is conceived for the quantum Internet. We define the term of entanglement swapping set that models the status of quantum memory of a quantum repeater with the stored density matrices. We determine the optimal entanglement swapping method that maximizes the entanglement rate of the quantum repeaters at the different entanglement swapping sets as function of the noise of the local memory and local operations. We prove the stability properties for non-complete entanglement swapping sets, complete entanglement swapping sets and perfect entanglement swapping sets. We prove the entanglement rates for the different entanglement swapping sets and noise levels. The results can be applied to the experimental quantum Internet.
Building blocks for commodity augmented reality-based molecular visualization and modeling in web browsers

For years, immersive interfaces using virtual and augmented reality (AR) for molecular visualization and modeling have promised a revolution in the way how we teach, learn, communicate and work in chemistry, structural biology and related areas. However, most tools available today for immersive modeling require specialized hardware and software, and are costly and cumbersome to set up. These limitations prevent wide use of immersive technologies in education and research centers in a standardized form, which in turn prevents large-scale testing of the actual effects of such technologies on learning and thinking processes. Here, I discuss building blocks for creating marker-based AR applications that run as web pages on regular computers, and explore how they can be exploited to develop web content for handling virtual molecular systems in commodity AR with no more than a webcam- and internet-enabled computer. Examples span from displaying molecules, electron microscopy maps and molecular orbitals with minimal amounts of HTML code, to incorporation of molecular mechanics, real-time estimation of experimental observables and other interactive resources using JavaScript. These web apps provide virtual alternatives to physical, plastic-made molecular modeling kits, where the computer augments the experience with information about spatial interactions, reactivity, energetics, etc. The ideas and prototypes introduced here should serve as starting points for building active content that everybody can utilize online at minimal cost, providing novel interactive pedagogic material in such an open way that it could enable mass-testing of the effect of immersive technologies on chemistry education.
Quantifying the Re-identification Risk of Event Logs for Process Mining

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.
Sharing Matters for Generalization in Deep Metric Learning

Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples. It should also transfer to different object classes. So what complementary information is missed by the discriminative paradigm? Besides finding characteristics that separate between classes, we also need them to likely occur in novel categories, which is indicated if they are shared across training classes. This work investigates how to learn such characteristics without the need for extra annotations or training data. By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks. Experiments show that, independent of the underlying network architecture and the specific ranking loss, our approach significantly improves performance in deep metric learning, leading to new the state-of-the-art results on various standard benchmark datasets.
Heralded Distribution of Single-Photon Path Entanglement

We report the experimental realization of heralded distribution of single-photon path entanglement at telecommunication wavelengths in a repeater-like architecture. The entanglement is established upon detection of a single photon, originating from one of two spontaneous parametric down conversion photon pair sources, after erasing the photon's which-path information. In order to certify the entanglement, we use an entanglement witness which does not rely on post-selection. We herald entanglement between two locations, separated by a total distance of 2 km of optical fiber, at a rate of 1.6 kHz. This work paves the way towards high-rate and practical quantum repeater architectures.
The Effect of Planning, Strategy Learning, and Working Memory Capacity on Mental Workload

Abstract In our modern society, planning and problem solving are crucial for handling a wide range of situations. Investigation of the experienced mental workload connected to planning, strategy learning, and working memory capacity is of particular interest for adjusting conditions according to the mental state of the individual. In our study, we examined 21 subjects during a planning and a working memory task. We applied the method of Dual Frequency Head Maps (DFHM) from the electroencephalogram for capturing mental workload objectively. We evaluated the DFHM-workload index and performance data during the learning and main phase of the planning task and linked the results to subjects’ working memory capacity. The DFHM-workload index indicated that subjects with higher working memory capacity experienced a gradual decrease in mental workload during strategy learning of the planning task. However, the effect of learning on mental workload disappeared during the main phase.
Working in contexts for which transparency is important

Purpose This paper aims to introduce the topic of explainable artificial intelligence (XAI) and reports on the outcomes of an interdisciplinary workshop exploring it. It reflects on XAI through the frame and concerns of the recordkeeping profession. Design/methodology/approach This paper takes a reflective approach. The origins of XAI are outlined as a way of exploring how it can be viewed and how it is currently taking shape. The workshop and its outcomes are briefly described and reflections on the process of investigating and taking part in conversations about XAI are offered. Findings The article reinforces the value of undertaking interdisciplinary and exploratory conversations with others. It offers new perspectives on XAI and suggests ways in which recordkeeping can productively engage with it, as both a disruptive force on its thinking and a set of newly emerging record forms to be created and managed. Originality/value The value of this paper comes from the way in which the introduction it provides will allow recordkeepers to gain a sense of what XAI is and the different ways in which they are both already engaging and can continue to engage with it.
Reinterpreting CTC training as iterative fitting

The connectionist temporal classification (CTC) enables end-to-end sequence learning by maximizing the probability of correctly recognizing sequences during training. The outputs of a CTC-trained model tend to form a series of spikes separated by strongly predicted blanks, know as the spiky problem. To figure out the reason for it, we reinterpret the CTC training process as an iterative fitting task that is based on frame-wise cross-entropy loss. It offers us an intuitive way to compare target probabilities with model outputs for each iteration, and explain how the model outputs gradually turns spiky. Inspired by it, we put forward two ways to modify the CTC training. The experiments demonstrate that our method can well solve the spiky problem and moreover, lead to faster convergence over various training settings. Beside this, the reinterpretation of CTC, as a brand new perspective, may be potentially useful in other situations. The code is publicly available at https://github.com/hzli-ucas/caffe/tree/ctc.
A Question Answering and Quiz Generation Chatbot for Education

In recent years, there have been a number of chatbots developed in the field of education. While many of them are designed to answer queries based on publicly available information such as in community answering platforms, or from a predefined knowledge base, there is no possibility of customizing the information to be queried. Moreover, there are no existing chatbots capable of generating self assessment quizzes based on any given document. This paper proposes a Question Answering and Quiz Generation Chatbot that allows a user to upload relevant documents and perform two main functions on them, namely answer extraction and question generation. The uploaded document is converted into a knowledge base through a number of data cleaning and preprocessing steps. The Question Answering module uses ranking functions and neural networks to extract the most appropriate answer from the knowledge base and the Quiz Generation module identifies key sentences and generates question-answer pairs, which can be used to generate a quiz for the user.
Automatic Verification of Differential Characteristics: Application to Reduced Gimli

Since Keccak was selected as the SHA-3 standard, more and more permutation-based primitives have been proposed. Different from block ciphers, there is no round key in the underlying permutation for permutation-based primitives. Therefore, there is a higher risk for a differential characteristic of the underlying permutation to become incompatible when considering the dependency of difference transitions over different rounds. However, in most of the MILP or SAT based models to search for differential characteristics, only the difference transitions are involved and are treated as independent in different rounds, which may cause that an invalid one is found for the underlying permutation. To overcome this obstacle, we are motivated to design a model which automatically avoids the inconsistency in the search for differential characteristics. Our technique is to involve both the difference transitions and value transitions in the constructed model. Such an idea is inspired by the algorithm to find SHA-2 characteristics as proposed by Mendel et al. in ASIACRYPT 2011, where the differential characteristic and the conforming message pair are simultaneously searched. As a first attempt, our new technique will be applied to the Gimli permutation, which was proposed in CHES 2017. As a result, we reveal that some existing differential characteristics of reduced Gimli are indeed incompatible, one of which is found in the Gimli document. In addition, since only the permutation is analyzed in the Gimli document, we are lead to carry out a comprehensive study, covering the proposed hash scheme and the authenticated encryption (AE) scheme specified for Gimli, which has become a second round candidate of the NIST lightweight cryptography standardization process. For the hash scheme, a semi-free-start (SFS) collision attack can reach up to 8 rounds starting from an intermediate round. For the AE scheme, a state recovery attack is demonstrated to achieve up to 9 rounds. It should be emphasized that our analysis does not threaten the security of Gimli.
Lyapunov spectra of chaotic recurrent neural networks

The Lyapunov spectrum of recurrent neural networks is calculated and analytical approximations through random matrix theory are provided. The dependency of attractor dimensions and entropy rates on coupling strength and input fluctuations is identified and a point symmetry of the Lyapunov spectrum is revealed. A link is shown between Lyapunov exponents to error propagation and stability in trained recurrent networks for machine-learning applications.
Towards Privacy-Preserving Dummy Generation in Location-Based Services

The popularity of Location-Based Services applications have drastically increased in GPS enabled smartphones. However, location privacy remains a major concern for users of such applications. In this paper, we have identified key issues observed in Dummy-based privacy preservation techniques. We have envisioned several research directions for enhanced dummy-generation in Location-Based Services.
The Panopticon device: An integrated Paul-trap–hemispherical mirror system for quantum optics

We present the design and construction of a new experimental apparatus for the trapping of single Ba+ ions in the center of curvature of an optical-quality hemispherical mirror. We describe the layout, fabrication, and integration of the full setup, consisting of a high-optical access monolithic “3D-printed” Paul trap, the hemispherical mirror, a diffraction-limited in-vacuum lens (NA = 0.7) for collection of atomic fluorescence, and a state-of-the art ultra-high vacuum vessel. This new apparatus enables the study of quantum electrodynamics effects such as strong inhibition and enhancement of spontaneous emission and achieves a collection efficiency of the emitted light in a single optical mode of 31%.
Video-Grounded Dialogues with Pretrained Generation Language Models

Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses.In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns.We propose a framework by extending GPT-2 models to tackle these challenges by formulating videogrounded dialogue tasks as a sequence-tosequence task, combining both visual and textual representation into a structured sequence, and fine-tuning a large pre-trained GPT-2 network.Our framework allows fine-tuning language models to capture dependencies across multiple modalities over different levels of information: spatio-temporal level in video and token-sentence level in dialogue context.We achieve promising improvement on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark from DSTC7, which supports a potential direction in this line of research.
Emulating a sensor using soft material dynamics: A reservoir computing approach to pneumatic artificial muscle

The McKibben pneumatic artificial muscle (PAM) is a promising soft actuator featuring flexibility, light weight, and high power. However, to realize high-precision control with such soft actuators and achieve human-like, autonomous, and natural behavioral control, both actuation techniques and sensing technologies require development to make the most of their features. In this regard, McKibben PAMs contain a drawback rooted in the nonlinear mechanical properties of their soft materials, as frictional anisotropy triggered upon PAM actuation between its fibers and the tubes causes hysteresis. Therefore, because the length of PAM is not uniquely determined by the applied pressure, it is generally necessary to install a sensor device (such as a laser displacement meter) for measuring the length in addition to the platform. In this study, we propose a scheme that can estimate a PAM's length using applied pressure and its internal dynamics in real time, making use of the machine learning technique called reservoir computing. We show that estimation can be performed with significantly high accuracy. We subsequently demonstrate that the proposed approach is robust against drastic and immediate changes in platform conditions, and can be adapted dynamically to real-time change. We expect this approach can replace the functionality of the laser displacement meter, freeing the platform from mechanical constraints and leading to the realization of a simpler soft actuator capable of sensing using soft material dynamics.
The Use of Linked Data and Artificial Intelligence as Key Elements in the Transformation of Technical Services

Library Technical Services have benefited from numerous stimuli. Although initially looked at with suspicion, transitions such as the move from catalog cards to the MARC formats have proven enormously helpful to libraries and their patrons. Linked data and Artificial Intelligence (AI) hold the same promise. Through the conversion of metadata surrogates (cataloging) to linked open data, libraries can represent their resources on the Semantic Web. But in order to provide some form of controlled access to unstructured data, libraries must reach beyond traditional cataloging to new tools such as AI to provide consistent access to a growing world of full-text resources.
The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion

In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of inflectional morphology. Participants were asked to submit systems which take raw text and a list of lemmas as input, and output all inflected forms, i.e., the entire morphological paradigm, of each lemma. In order to simulate a realistic use case, we first released data for 5 development languages. However, systems were officially evaluated on 9 surprise languages, which were only revealed a few days before the submission deadline. We provided a modular baseline system, which is a pipeline of 4 components. 3 teams submitted a total of 7 systems, but, surprisingly, none of the submitted systems was able to improve over the baseline on average over all 9 test languages. Only on 3 languages did a submitted system obtain the best results. This shows that unsupervised morphological paradigm completion is still largely unsolved. We present an analysis here, so that this shared task will ground further research on the topic.