Reviewerly/exHarmony huggingface dataset:
Incremental Neural Coreference Resolution in Constant Memory

We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components.Given a new sentence, our endto-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any).These spans are then used to update the entity's representations before being forgotten; we only retain a fixed set of salient entities throughout the document.In this work, we successfully convert a highperforming model (Joshi et al., 2020), asymptotically reducing its memory usage to constant space with only a 0.3% relative loss in F1 on OntoNotes 5.0.
Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles

Many datasets have been shown to contain incidental correlations created by idiosyncrasies in the data collection process.For example, sentence entailment datasets can have spurious word-class correlations if nearly all contradiction sentences contain the word "not", and image recognition datasets can have telltale object-background correlations if dogs are always indoors.In this paper, we propose a method that can automatically detect and ignore these kinds of dataset-specific patterns, which we call dataset biases.Our method trains a lower capacity model in an ensemble with a higher capacity model.During training, the lower capacity model learns to capture relatively shallow correlations, which we hypothesize are likely to reflect dataset bias.This frees the higher capacity model to focus on patterns that should generalize better.We ensure the models learn non-overlapping approaches by introducing a novel method to make them conditionally independent.Importantly, our approach does not require the bias to be known in advance.We evaluate performance on synthetic datasets, and four datasets built to penalize models that exploit known biases on textual entailment, visual question answering, and image recognition tasks.We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
Gradient-guided Unsupervised Lexically Constrained Text Generation

Lexically constrained generation requires the target sentence to satisfy some lexical constraints, such as containing some specific words or being the paraphrase to a given sentence, which is very important in many real-world natural language generation applications. Previous works usually apply beam-search-based methods or stochastic searching methods to lexically-constrained generation. However, when the search space is too large, beam-search-based methods always fail to find the constrained optimal solution. At the same time, stochastic search methods always cost too many steps to find the correct optimization direction. In this paper, we propose a novel method G2LC to solve the lexically-constrained generation as an unsupervised gradient-guided optimization problem. We propose a differentiable objective function and use the gradient to help determine which position in the sequence should be changed (deleted or inserted/replaced by another word). The word updating process of the inserted/replaced word also benefits from the guidance of gradient. Besides, our method is free of parallel data training, which is flexible to be used in the inference stage of any pre-trained generation model. We apply G2LC to two generation tasks: keyword-to-sentence generation and unsupervised paraphrase generation. The experiment results show that our method achieves state-of-the-art compared to previous lexically-constrained methods.
PySBD: Pragmatic Sentence Boundary Disambiguation

In this paper, we present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages.We aim to provide a realistic segmenter which can provide logical sentences even when the format and domain of the input text is unknown.In our work, we adapt the Golden Rules Set (a language specific set of sentence boundary exemplars) originally implemented as a ruby gem pragmatic segmenter 1 which we ported to Python with additional improvements and functionality.PySBD passes 97.92% of the Golden Rule Set examplars for English, an improvement of 25% over the next best open source Python tool.
Optimal distributed sensing in noisy environments

This paper shows that signals with a specific spatial dependence can be directly measured with a quadratic quantum advantage by preparing appropriate quantum states that utilize entanglement between sensors, hence the measurement process is not affected by other signals, including noise, with a different spatial dependence.
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks

Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models.Recent work doing incremental coreference resolution using just the global representation of entities shows practical benefits but requires keeping all entities in memory, which can be impractical for long documents.We argue that keeping all entities in memory is unnecessary, and we propose a memoryaugmented neural network that tracks only a small bounded number of entities at a time, thus guaranteeing a linear runtime in length of document.We show that (a) the model remains competitive with models with high memory and computational requirements on OntoNotes and LitBank, and (b) the model learns an efficient memory management strategy easily outperforming a rule-based strategy.
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark

In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -RussianGLUE.Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills -detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon.For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology (Wang et al., 2019), was developed from scratch for the Russian language.We provide baselines, human level evaluation, an opensource framework for evaluating models and an overall leaderboard of transformer models for the Russian language.Besides, we present the first results of comparing multilingual models in the adapted diagnostic test set and offer the first steps to further expanding or assessing state-of-the-art models independently of language.
Simple Artificial Neuron Using an Ovonic Threshold Switch Featuring Spike-Frequency Adaptation and Chaotic Activity

As an essential building block for developing a large-scale brain-inspired computing system, we propose a highly scalable and energy-efficient artificial neuron device composed of an ovonic threshold switch (OTS) and a few passive electrical components. It is found that the proposed neuron device shows not only the basic integrate-and-fire function and the rate-coding property, but also the spike-frequency-adaptation (SFA) property and the chaotic activity of biological neurons, the most common features found in mammalian cortex, but they have been hard to achieve up to now. In addition, it is shown that the energy consumption of the OTS-based neuron device scales with the size of the OTS device, extrapolating both the size and the energy efficiency to the level of a biological neuron in a human brain with state-of-the-art technology. Finally, using the OTS-based neuron device combined with the reservoir computing technique, the spoken-digit recognition task has been performed with a considerable degree of recognition accuracy (94%). These results demonstrate that our OTS-based artificial neuron device is promising for the application in the development of a large-scale brain-inspired computing system.
Multipartite state generation in quantum networks with optimal scaling

We introduce a repeater scheme to efficiently distribute multipartite entangled states in a quantum network with optimal scaling. The scheme allows to generate graph states such as 2D and 3D cluster states of growing size or GHZ states over arbitrary distances, with a constant overhead per node/channel that is independent of the distance. The approach is genuine multipartite, and is based on the measurement-based implementation of multipartite hashing, an entanglement purification protocol that operates on a large ensemble together with local merging/connection of elementary building blocks. We analyze the performance of the scheme in a setting where local or global storage is limited, and compare it to bipartite and hybrid approaches that are based on the distribution of entangled pairs. We find that the multipartite approach offers a storage advantage, which results in higher efficiency and better performance in certain parameter regimes. We generalize our approach to arbitrary network topologies and different target graph states.
An Overview of Mobile Learning for Refugee Students: Juxtaposing Refugee Needs with Mobile Applications’ Characteristics

The mass influx of refugees into Europe since 2013 and their educational challenges have increased the need for high-quality refugee education. One proposal for addressing these challenges was to leverage mobile devices for educational purposes (mobile learning). Although significant research has been done in this field, mobile learning’s effectiveness on different social groups has yet to be explored. The present review paper aims to outline: (a) the factors that challenge refugee education, (b) the use of smart mobile devices by the refugee population, (c) the conflicting views about the effect of mobile learning in refugee education, and (d) the proposed characteristics for mobile refugee applications as found in the literature. A juxtaposition of refugee needs with the characteristics of mobile learning apps is attempted. By surveying the literature, the present paper concludes that mobile learning seems beneficial for refugees in two ways: providing refugees access to education and improving the quality of the provided refugee education. However, it is not a one-solution-fits-all regarding their education. At the end, future research proposals are included.
An Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification

Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.
X-Reality Museums: Unifying the Virtual and Real World Towards Realistic Virtual Museums

Culture is a field that is currently entering a revolutionary phase, no longer being a privilege for the few, but expanding to new audiences who are urged to not only passively consume cultural heritage content, but actually participate and assimilate it on their own. In this context, museums have already embraced new technologies as part of their exhibitions, many of them featuring augmented or virtual reality artifacts. The presented work proposes the synthesis of augmented, virtual and mixed reality technologies to provide unified X-Reality experiences in realistic virtual museums, engaging visitors in an interactive and seamless fusion of physical and virtual worlds that will feature virtual agents exhibiting naturalistic behavior. Visitors will be able to interact with the virtual agents, as they would with real world counterparts. The envisioned approach is expected to not only provide refined experiences for museum visitors, but also achieve high quality entertainment combined with more effective knowledge acquisition.
3D Bird Reconstruction: A Dataset, Model, and Shape Recovery from a Single View

Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh .
Linguistic Profiling of a Neural Language Model

In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems.We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation.We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks.We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.
Application of Artificial Intelligence for Medical Research

The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [...]
A Deep Learning based Method for Image Splicing Detection

Abstract Image manipulation has become an easy task due to the availability of user-friendly multimedia tools. The images can be manipulated in several ways. Image splicing is one of such image manipulation methods in which two or more images are merged to obtain a single composite image. These manipulated images can be misused to cheat others. This paper proposes a deep learning-based method to detect image splicing in the images. First, the input image is preprocessed using a technique called ‘Noiseprint’ to get the noise residual by suppressing the image content. Second, the popular ResNet-50 network is used as a feature extractor. Finally, the obtained features are classified as spliced or authentic using the SVM classifier. The experiments performed on the CUISDE dataset show that the proposed method outperforms other existing methods. The proposed method achieves an average classification accuracy of 97.24%.
Recycling the resource: Sequential usage of shared state in quantum teleportation with weak measurements

Complete measurements, while providing maximal information gain, result in destruction of the shared entanglement. In the standard teleportation scheme, the sender's measurement on the shared entangled state between the sender and the receiver has that consequence. We propose here a teleportation scheme involving weak measurements which can sustain entanglement up to a certain level so that the reusability of the shared resource state for reattempting teleportation is possible. The measurements are chosen in such a way that it is weak enough to retain entanglement and hence can be reused for quantum tasks, yet adequately strong to ensure quantum advantage in the protocol. In this scenario, we report that at most six sender-receiver duos can reuse the state for reattempting teleportation, when the initial shared state is entangled in a finite neighborhood of the maximally entangled state and for a suitable choice of weak measurements. However, we observe that the reattempt number decreases with the decrease in the entanglement of the initial shared state. Among the weakening strategies studied, Bell measurement admixed with white noise performs better than any other low-rank weak measurements in this situation.
Bayesian synthesis of probabilistic programs for automatic data modeling

We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data. We provide a precise formulation of Bayesian synthesis for automatic data modeling that identifies sufficient conditions for the resulting synthesis procedure to be sound. We also derive a general class of synthesis algorithms for domain-specific languages specified by probabilistic context-free grammars and establish the soundness of our approach for these languages. We apply the techniques to automatically synthesize probabilistic programs for time series data and multivariate tabular data. We show how to analyze the structure of the synthesized programs to compute, for key qualitative properties of interest, the probability that the underlying data generating process exhibits each of these properties. Second, we translate probabilistic programs in the domain-specific language into probabilistic programs in Venture, a general-purpose probabilistic programming system. The translated Venture programs are then executed to obtain predictions of new time series data and new multivariate data records. Experimental results show that our techniques can accurately infer qualitative structure in multiple real-world data sets and outperform standard data analysis methods in forecasting and predicting new data.
Long-term effectiveness of immersive VR simulations in undergraduate science learning: lessons from a media-comparison study

Our main goal was to investigate if and how using multiple immersive virtual reality (iVR) simulations and their video playback, in a science course, affects student learning over time. We conducted a longitudinal study, in ecological settings, at an undergraduate field-course on three topics in environmental biology. Twenty-eight undergraduates were randomly assigned to either an iVR-interaction group or a video-viewing group. During the field-course, the iVR group interacted with a head-mounted device-based iVR simulation related to each topic (i.e. total three interventions), while the video group watched a pre-recorded video of the respective simulation on a laptop. Cognitive and affective data were collected through the following checkpoints: a pre-test before the first intervention, one topic-specific post-test immediately after each intervention, a final post-test towards the end of the course, and a longitudinal post-test deployed approximately 2 months after the course. Through a descriptive analysis, it was found that student performance on the knowledge tests increased considerably over time for the iVR group but remained unchanged for the video group. While no within- or between-group differences were noted for intrinsic motivation and self-efficacy measures, students in the iVR group enjoyed all the simulations, and perceived themselves to benefit from those simulations.
Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach

Diabetes mellitus, commonly known as diabetes, is a chronic disease that often results in multiple complications. Risk prediction of diabetes complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, focusing on Type 2 diabetes mellitus (T2DM), we study the risk of developing complications after the initial T2DM diagnosis from longitudinal patient records. We propose a novel multi-task learning approach to simultaneously model multiple complications where each task corresponds to the risk modeling of one complication. Specifically, the proposed method strategically captures the relationships (1) between the risks of multiple T2DM complications, (2) between different risk factors, and (3) between the risk factor selection patterns, which assumes similar complications have similar contributing risk factors. The method uses coefficient shrinkage to identify an informative subset of risk factors from high-dimensional data, and uses a hierarchical Bayesian framework to allow domain knowledge to be incorporated as priors. The proposed method is favorable for healthcare applications because in addition to improved prediction performance, relationships among the different risks and among risk factors are also identified. Extensive experimental results on a large electronic medical claims database show that the proposed method outperforms state-of-the-art models by a significant margin. Furthermore, we show that the risk associations learned and the risk factors identified lead to meaningful clinical insights.
Dynamic Key-Value Memory Networks With Rich Features for Knowledge Tracing

Knowledge tracing is an important research topic in student modeling. The aim is to model a student's knowledge state by mining a large number of exercise records. The dynamic key-value memory network (DKVMN) proposed for processing knowledge tracing tasks is considered to be superior to other methods. However, through our research, we have noticed that the DKVMN model ignores both the students' behavior features collected by the intelligent tutoring system (ITS) and their learning abilities, which, together, can be used to help model a student's knowledge state. We believe that a student's learning ability always changes over time. Therefore, this article proposes a new exercise record representation method, which integrates the features of students' behavior with those of the learning ability, thereby improving the performance of knowledge tracing. Our experiments show that the proposed method can improve the prediction results of DKVMN.