Reviewerly/exHarmony huggingface dataset:
Generating Positive Psychosis Symptom Keywords from Electronic Health Records

The development of Natural Language Processing (NLP) solutions for information extraction from electronic health records (EHRs) has grown in recent years, as most clinically relevant information in EHRs is documented only in free text. One of the core tasks for any NLP system is to extract clinically relevant concepts such as symptoms. This information can then be used for more complex problems such as determining symptom onset, which requires temporal information. In the mental health domain, comprehensive vocabularies for specific disorders are scarce, and rarely contain keywords that reflect real-world terminology use. We explore the use of embedding techniques to automatically generate lexical variants of psychosis symptoms into vocabularies, that can be used in complex downstream NLP tasks. We study the impact of the underlying text material on generating useful lexical entries, experimenting with different corpora and with unigram/bigram models. We also propose a method to automatically compute thresholds for choosing the most relevant terms. Our main contribution is a systematic study of unsupervised vocabulary generation using different corpora for an understudied clinical use-case. Resulting lexicons are publicly available.
Survey on Event Extraction Technology in Information Extraction Research Area

Event extraction is one of the most challenging tasks in the field of information extraction. The research of event extraction technology has important theoretical significance and wide application value. This paper makes a survey of event extraction technology, describes the tasks and related concepts of event extraction, analyzes, compares and generalizes the relevant descriptions in different fields. Then analyzes, compares and summarizes the three main methods of event extraction. These methods have their own advantages and disadvantages. The methods based on rules and templates are more mature, the method based on statistical machine learning is dominant, and the method based on deep learning is the future development trend. At the same time, this paper also reviews the research status and key technologies of event extraction, and finally summarizes the current challenges and future research trends of event extraction technology.
Online Supervised Sketching Hashing for Large-Scale Image Retrieval

Online hashing methods have achieved a good tradeoff between the accuracy and the efficiency for learning the hash functions in the online settings. Compared to the stochastic gradient descent-based online hashing methods, the data sketching-based online hashing methods can preserve more information from the streaming data to learn the hash functions. However, the existing data sketching-based methods are unsupervised hashing methods which cannot utilize the supervised information to alleviate the gap between the binary codes and the semantic similarity. In this paper, we propose an online supervised hashing method in which a semantic similarity graph is designed to describe the data similarity according to the supervised information, and a data sketch is developed to preserve the characteristics of the graph with significantly smaller size in the online settings. Hence, the hash functions can be learned online from the data sketch with low computational time cost and small space storage. Furthermore, we develop a parallel computing version of the proposed method, aiming at accelerating the process of learning the hash functions. The experiments on three large-scale datasets show that our method can achieve comparable or better search accuracy compared to other online hashing methods.
Effective Feature Representation for Clinical Text Concept Extraction

Crucial information about the practice of healthcare is recorded only in free-form text, which creates an enormous opportunity for high-impact NLP. However, annotated healthcare datasets tend to be small and expensive to obtain, which raises the question of how to make maximally efficient uses of the available data. To this end, we develop an LSTM-CRF model for combining unsupervised word representations and hand-built feature representations derived from publicly available healthcare ontologies. We show that this combined model yields superior performance on five datasets of diverse kinds of healthcare text (clinical, social, scientific, commercial). Each involves the labeling of complex, multi-word spans that pick out different healthcare concepts. We also introduce a new labeled dataset for identifying the treatment relations between drugs and diseases.
Names Are Not Just Sound and Smoke: Word Embeddings for Axiom Selection

First-order theorem proving with large knowledge bases makes it necessary to select those parts of the knowledge base, that are necessary to prove the theorem at hand. We extend syntactic axiom selection procedures like SInE to use semantics of symbol names. For this, not only occurrences of symbol names but also semantically similar names are taken into account. We use a similarity measure based on word embeddings. An evaluation of this similarity based SInE is given using problems from TPTP’s CSR problem class and Adimen-SUMO. This evaluation is done with two very different systems, namely the Hyper tableau prover and the saturation based system E.
Deep Energy: Task Driven Training of Deep Neural Networks

The success of deep learning has been due, in no small part, to the availability of large annotated datasets.Thus, a major bottleneck in current learning pipelines is the timeconsuming human annotation of data.In scenarios where such input-output pairs cannot be collected, simulation is often used instead, leading to a domain-shift between synthesized and realworld data.This work offers an unsupervised alternative that relies on the availability of task-specific energy functions, replacing the generic supervised loss.Such energy functions are assumed to lead to the desired label as their minimizer given the input.The proposed approach, termed Deep-Energy, trains a Deep Neural Network (DNN) to approximate this minimization for any chosen input.Once trained, a simple and fast feed-forward computation provides the inferred label.This approach allows us to perform unsupervised training of DNNs with real-world inputs only, and without the need for manually-annotated labels, nor synthetically created data.Deep-Energy is demonstrated in this paper on three different tasks -seeded segmentation, image matting and single image dehazing -exposing its generality and wide applicability.Our experiments show that the solution provided by the network is often much better in quality than the one obtained by a direct minimization of the energy function, suggesting an added regularization property in our scheme.
Permissioned Distributed Ledgers for Land Transactions; A Case Study

Considering the inefficiency and ineffectiveness of the current manual land registration systems being practiced in Sri Lanka and the emergence of the concept of blockchain based land registries as a successful replacement for badly kept, mismanaged and/or corrupt land registries from around the world, this research proposes and evaluates the applicability of a permissioned distributed land ledger solution for Sri Lanka. The final solution presents optimal content for the ledger (extracted from the current folio), preserves current land transactions, has reassigned duties to state validators and has got away with the folio system while ensuring derivation of the pedigree/folio tree for a land at a given time. A regional distributed land ledger representing the present regional ledger system and an island wide unified land ledger which addresses unequal regional land transaction density conditions across the island were proposed as solution models. The proposed models of the solution were implemented using Hyperledger Fabric v1.2. They were evaluated for performance on an AWS t2.large instance with 2 vCPUs, 8GiB memory, under different land transaction density conditions and node failure conditions. A community consisting of twenty-one peers belonging to nine organizations, was subject to evaluation. The island wide solution records higher throughput, lower latency and tolerance for fail-stop conditions than the regional distributed land ledger. Further, the island wide solution does not show a significant drop in throughput up to two crash failures out of seven Kafka brokers in production scale deployment.
Reducing harm by designing discourse and digital tools for opioid users' contexts

The United States is struggling with an opioid overdose (OD) crisis. The opioid OD epidemic includes legally prescribed and illicitly acquired opioids. Regardless of if an opioid is legal, understanding users' contexts of use is essential to design effective methods for individuals to reverse opioid OD. In other words, if health information is not designed to be contextually relevant, the opioid OD health information will be unusable. To demonstrate these distinct healthcare design contexts, I extend Patient Experience Design (PXD) to include community-based and technology-based contexts of use by analyzing two case examples of the Chicago Recovery Alliance's and PwrdBy's attempts to decrease deaths by opioid OD. Next, I discuss implications of community-based and technology-based PXD within communities of opioid users, critiquing each method and suggesting four contexts of use-heuristic categories to consider when designing health communication information for users in these contexts.
Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach

While achieving great fluency, current machine translation (MT) techniques are bottlenecked by adequacy issues.To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation.To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs.We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task.We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models

A method (500) includes receiving audio data encoding an utterance (106) spoken by a native speaker (110) of a first language, and receiving a biasing term list (105) including one or more terms in a second language different than the first language. The method also includes processing, using a speech recognition model (200), acoustic features (105) derived from the audio data to generate speech recognition scores for both wordpieces and corresponding phoneme sequences in the first language. The method also includes rescoring the speech recognition scores for the phoneme sequences based on the one or more terms in the biasing term list, and executing, using the speech recognition scores for the wordpieces and the rescored speech recognition scores for the phoneme sequences, a decoding graph (400) to generate a transcription (116) for the utterance.
Quantum digital cooling

We introduce a method for digital preparation of ground states of simulated Hamiltonians, inspired by cooling in nature and adapted to leverage the capabilities of digital quantum hardware. The cold bath is simulated by a single ancillary qubit, which is reset periodically and coupled to the system non-perturbatively. Studying this cooling method on a 1-qubit system toy model, we optimize two cooling protocols based on weak-coupling and strong-coupling approaches. Extending the insight from the 1-qubit system model, we develop two scalable protocols for larger systems. The LogSweep protocol extends the weak-coupling approach by sweeping energies to resonantly match any targeted transition. We test LogSweep on the 1D tranverse-field Ising model, demonstrating approximate ground state preparation with an error that can be made polynomially small in the computation time for all three phases of the system. The BangBang protocol extends the strong-coupling approach, and exploits a heuristics for local Hamiltonians to maximise the probability of de-exciting system transitions in the shortest possible time. Although this protocol does not promise long-time convergence, it allows for a rapid cooling to an approximation of the ground state, making this protocol appealing for near-term demonstrations.
Detection of Genuine Multipartite Entanglement in Multipartite Systems

We investigate genuine multipartite entanglement in general multipartite systems. Based on the norms of the correlation tensors of a multipartite state under various partitions, we present an analytical sufficient criterion for detecting the genuine four-partite entanglement. The results are generalized to arbitrary multipartite systems.
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection

It is known that a deep neural networkmodel pre-trained with large-scale data greatly improves the accuracy of various tasks, especially when there are resource constraints. However, the information needed to solve a given task can vary, and simplyusing the output of the final layer is not necessarily sufficient. Moreover, to our knowledge, exploiting large language representation models to detect grammatical errors has not yet been studied. In this work, we investigate the effect of utilizing information not only from the final layer but also from intermediate layers ofa pre-trained language representation model to detect grammatical errors. We propose a multi-head multi-layer attention model that determines the appropriate layers in Bidirectional Encoder Representation from Transformers (BERT). The proposed method achieved the best scoreson three datasets for grammatical error detection tasks, outperforming the current state of the art method by 6.0 points on FCE, 8.2 points on CoNLL14, and 12.2 pointson JFLEG in terms of F0.5. We also demonstrate that by using multi-head multi-layer attention, our model can exploit a broader range of information for each token in a sentence than a model that uses only the final layer’s information.
Transmission spectra of an ultrastrongly coupled qubit-dissipative resonator system

We calculate the transmission spectra of a flux qubit coupled to a dissipative resonator in the ultrastrong coupling regime. Such a qubit-oscillator system constitutes the building block of superconducting circuit QED platforms. The calculated transmission of a weak probe field quantifies the response of the qubit, in frequency domain, under the sole influence of the oscillator and of its dissipative environment, an Ohmic heat bath. We find the distinctive features of the qubit-resonator system, namely two-dip structures in the calculated transmission, modified by the presence of the dissipative environment. The relative magnitude, positions, and broadening of the dips are determined by the interplay among qubit-oscillator detuning, the strength of their coupling, and the interaction with the heat bath.
Experimental control of the degree of non-classicality via quantum coherence

The origin of non-classicality in physical systems and its connection to distinctly quantum features such as entanglement and coherence is a central question in quantum physics. This work analyses this question theoretically and experimentally, linking quantitatively non-classicality with quantum coherence. On the theoretical front, we show when the coherence of an observable is linearly related to the degree of violation of the Kolmogorov condition, which quantifies the deviation from any classical (non-invasive) explanation of the multi-time statistics. Experimentally, we probe this connection between coherence and non-classicality in a time-multiplexed optical quantum walk. We demonstrate exquisite control of quantum coherence of the walker by varying the degree of coherent superposition effected by the coin, and we show a concomitant variation in the degree of non-classicality of the walker statistics, which can be accessed directly by virtue of the unprecedented control on the measurement-induced effects obtained via fast programmable electro-optic modulators.
Beating direct transmission bounds for quantum key distribution with a multiple quantum memory station

Overcoming repeaterless bounds for the secret key rate capacity of quantum key distribution protocols is still a challenge with current technology. Luong et al. [Appl. Phys. B 122, 96 (2016).] proposed a protocol to beat a repeaterless bound using one pair of quantum memories. However, the required experimental parameters for the memories are quite demanding. We extend the protocol with multiple pairs of memories, operated in a parallel manner to relax these conditions. We quantify the amount of relaxation in terms of the most crucial memory parameters, given the number of applied memory pairs. In the case of high-loss channels, we found that adding only a few pairs of memories can make the crossover possible.
Stability analysis of reservoir computers dynamics via Lyapunov functions

A Lyapunov design method is used to analyze the nonlinear stability of a generic reservoir computer for both the cases of continuous-time and discrete-time dynamics. Using this method, for a given nonlinear reservoir computer, a radial region of stability around a fixed point is analytically determined. We see that the training error of the reservoir computer is lower in the region where the analysis predicts global stability but is also affected by the particular choice of the individual dynamics for the reservoir systems. For the case that the dynamics is polynomial, it appears to be important for the polynomial to have nonzero coefficients corresponding to at least one odd power (e.g., linear term) and one even power (e.g., quadratic term).
Heavy metals and hydrocarbons content in soils of settlements of the Yamal-Nenets autonomous region

In August 2015 there were executed investigations on the study of the soils diversity of the Yamal-Nenets Autonomous Okrug. One of the directions of this work got be the study of urban soils of settlements of the Yamal-Nenents Autonomous Okrug. The sectors for the observation were settlement of Harsaim, village Aksarka, city of Salekhard, settlement Harp and city of Labytnangi. About 20 soil samples were collected during the field work. Samples were collected from a depth of 0-5 cm and 5-20 cm. Heavy metals (HM) were detected with the use of X-ray fluorescent analyzer “Spectroscan-MAX”. The HM content values were compared with the corresponding Approxible Permissible Concentrations and Maximum Allowable Concentrations (MAC) adopted in Russia. Hydrocarbons content was determined by gravimetric method. Values of the hydrocarbons content in studied soils were compared with the existing regulations of the Russian Federation. The levels of soil contamination by hydrocarbons were determined. The study of soil samples from different settlements allowed to reveal characteristic features of soil contamination of separate settlements by HM and hydrocarbons and to compare them against each other. The vast majority of samples are characterized by arsenic exceedance of MAC, which should indicate to a high regional background of this element. For a more adequate assessment of the Zc meaning as the value of the total pollution index of soils there were used not only arithmetical average values of the coefficients of the chemical composition concentration (Kc), but also their average geometric values. According to levels of total soil contamination most of soil samples are characterized as non-hazardous (Zc<16). Calculation of soil pollution index showed that the most of soil samples have values less than 1. It characterizes soils as unpolluted. Statistical processing of obtained data in the media of the analytical software interface STATISTICA 10 showed a statistically significant difference in the content ofHM and hydrocarbons for the layer of 0-20 cm of the soils for three elements (Cu, Zn, Ni).
Hot or Not

This paper introduces a novel technique to measure indoor ambient air temperature using the battery temperature sensor found on typical smartphones. We develop physics-based models to predict ambient air temperature that consider the many warming and cooling scenarios faced by phones and account for the excess heat generated by smartphone components such as the CPU, screen, network, and charging hardware. To accommodate never-before-seen devices, we also develop a domain adaptation technique that leverages previously derived models, substantially reducing the overhead of learning accurate models for a new phone. We evaluate our models for a range of devices, operating scenarios, ambient temperatures, and phone cases, with mean errors generally less than 1.5% of ambient temperature. We also present a case study to demonstrate the utility of our approach for spatial and temporal monitoring of ambient temperature variations in an office building; while indoor conditions vary by as much as 13°F, mean error in measurement by our models is 1.4%.
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients

Deformable registration continues to be one of the key challenges in medical image analysis. While iconic registration methods have started to benefit from the recent advances in medical deep learning, the same does not yet apply for the registration of point sets, e.g. registration based on surfaces, keypoints or landmarks. This is mainly due to the restriction of the convolution operator in modern CNNs to densely gridded input. However, with the newly developed methods from the field of geometric deep learning suitable tools are now emerging, which enable powerful analysis of medical data on irregular domains. In this work, we present a new method that enables the learning of regularized feature descriptors with dynamic graph CNNs. By incorporating the learned geometric features as prior probabilities into the well-established coherent point drift (CPD) algorithm, formulated as differentiable network layer, we establish an end-to-end framework for robust registration of two point sets. Our approach is evaluated on the challenging task of aligning keypoints extracted from lung CT scans in inhale and exhale states with large deformations and without any additional intensity information. Our results indicate that the inherent geometric structure of the extracted keypoints is sufficient to establish descriptive point features, which yield a significantly improved performance and robustness of our registration framework.
A Holistic Natural Language Generation Framework for the Semantic Web

With the ever-growing generation of data for the Semantic Web comes an increasing demand for this data to be made available to non-semantic Web experts.One way of achieving this goal is to translate the languages of the Semantic Web into natural language.We present LD2NL, a framework for verbalizing the three key languages of the Semantic Web, i.e., RDF, OWL, and SPARQL.Our framework is based on a bottom-up approach to verbalization.We evaluated LD2NL in an open survey with 86 persons.Our results suggest that our framework can generate verbalizations that are close to natural languages and that can be easily understood by nonexperts.Therewith, it enables non-domain experts to interpret Semantic Web data with more than 91% of the accuracy of domain experts.