Reviewerly/exHarmony huggingface dataset:
Unsupervised tumor detection in Dynamic PET/CT imaging of the prostate

Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached. This work aims to detect prostate cancer foci in Dynamic PET images using an unsupervised learning approach. The proposed method extracts three feature classes from 4D imaging data that include statistical, kinetic biological and deep features that are learned by a deep stacked convolutional autoencoder. Anomalies, which are classified as tumors, are detected in feature space using density estimation. The proposed algorithm generates promising results for sufficiently large cancer foci in real PET scans imaging where the foci is not viewed by the tomographic devices used for detection.
Interfacing Superconducting Qubits With Cryogenic Logic: Readout

As superconducting quantum processors increase in size and complexity, the scalability of standard techniques for qubit control and readout becomes a limiting factor. Replacing room temperature analog components with cryogenic digital components could allow for the realization of systems well beyond the current state-of-the-art qubit arrays with tens of qubits. The standard technique for performing a qubit measurement with heterodyne readout uses a quantum-limited cryogenic amplifier chain and requires bulky microwave components inside the refrigerator with multiple control lines and pump signals. Additionally, the result is only accessible in software at room temperature. An alternative method for measuring qubits involves mapping the qubit state onto the photon occupation in a microwave cavity, followed by subsequent photon detection using a Josephson photomultiplier (JPM). The JPM measures the qubit and stores the result in a classical circulating current. To make use of this result, we can leverage existing single flux quantum (SFQ) circuitry. An underdamped Josephson transmission line (JTL) can be coupled to the JPM and fluxons traveling along the JTL are accelerated or delayed, depending on the circulating current state of the JPM. This fluxon delay can then be converted to an SFQ logic signal resulting in a digital qubit readout with a proximal microfabricated device, paving the way for cryogenic digital feedback necessary for error-correcting codes.
Dynamic Evaluation of Transformer Language Models

This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation. Transformers use stacked layers of self-attention that allow them to capture long range dependencies in sequential data. Dynamic evaluation fits models to the recent sequence history, allowing them to assign higher probabilities to re-occurring sequential patterns. By applying dynamic evaluation to Transformer-XL models, we improve the state of the art on enwik8 from 0.99 to 0.94 bits/char, text8 from 1.08 to 1.04 bits/char, and WikiText-103 from 18.3 to 16.4 perplexity points.
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradient confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through theoretical and experimental results, we demonstrate how the neural network architecture affects gradient confusion, and thus the efficiency of training. Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training. On the other hand, increasing the depth of neural networks has the opposite effect. Our results indicate that alternate initialization techniques or networks using both batch normalization and skip connections help reduce the training burden of very deep networks.
Fast detection of maximal exact matches via fixed sampling of query<i>K</i>-mers and Bloom filtering of index<i>K</i>-mers

Abstract Motivation Detection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches. Results Under this framework, we proposed a new method to detect all MEMs from a pair of genomes. The method first performs a fixed sampling of k-mers on the query sequence, and adds these selected k-mers to a Bloom filter. Then all the k-mers of the reference sequence are tested by the Bloom filter. If a k-mer passes the test, it is inserted into a hash table for indexing. Compared with the existing methods, much less number of query k-mers are generated and much less k-mers are inserted into the index to avoid unnecessary matches, leading to an efficient matching process and memory usage savings. Experiments on large genomes demonstrate that our method is at least 1.8 times faster than the best of the existing algorithms. This performance is mainly attributed to the key novelty of our method that the fixed k-mer sampling must be conducted on the query sequence and the index k-mers are filtered from the reference sequence via a Bloom filter. Availability and implementation https://github.com/yuansliu/bfMEM Supplementary information Supplementary data are available at Bioinformatics online.
Mind your wallet's privacy

With the surge in popularity of cryptocurrencies, Bitcoin has emerged as one of the most promising means for remittance, payments, and trading. Supplemented by the convenience offered by the smartphones, an increasing number of users are adopting Bitcoin wallet apps for different purposes.
Risk frames and multiple ways of knowing: Coping with ambiguity in oil spill risk governance in the Norwegian Barents Sea

The opening of new areas for offshore drilling in the Arctic is highly controversial. As ice cover in the region is melting at an alarming rate, new areas have been opened for petroleum industry in the Norwegian Barents Sea. Our qualitative analysis examines risks related to the petroleum operations in the newly opened areas and provides insight into the complex and socially constructed nature of the risks. With the use of visual influence diagram- based mental modelling approach, we demonstrate the multiple ways in which the risks are understood and defined. We also analyse the type of knowledge that the risk frames are based on. The influence diagrams present the risk frames in a clear, visual, form. The study indicates that the existing governance framework fails to treat the ambiguity around oil spill risks: the current risk assessments and risk management do not reflect on the multiple ways in which the participants in this study 1) frame the problem situation, 2) how they identify different measures to manage risks, and 3) what are considered as key knowledge needs and knowledge producers by the participants. We suggest that social learning and collaborative knowledge production are needed to move towards developing shared understanding of the problem situation. Finally, we suggest that the rigorous examination and the unveiling of ambiguity may help developing deliberative risk governance measures and moving towards sustainability transformations.
On the Inductive Bias of Neural Tangent Kernels

State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certain kernel obtained at initialization, called the neural tangent kernel. We study the inductive bias of learning in such a regime by analyzing this kernel and the corresponding function space (RKHS). In particular, we study smoothness, approximation, and stability properties of functions with finite norm, including stability to image deformations in the case of convolutional networks, and compare to other known kernels for similar architectures.
Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone

Abstract Detecting anomalies in wind turbine blades from aerial images taken by drones can reduce the costs of periodic inspections. Deep learning is useful for image recognition, but it requires large amounts of data to be collected on rare abnormalities. In this paper, we propose a method to distinguish normal and abnormal parts of a blade by combining one-class support vector machine, an unsupervised learning method, with deep features learned from a generic image dataset. The images taken by a drone are subsampled, projected to the feature space, and compressed by using principle component analysis (PCA) to make them learnable. Experiments show that features in the lower layers of deep nets are useful for detecting anomalies in blade images.
ReQWIRE: Reasoning about Reversible Quantum Circuits

Common quantum algorithms make heavy use of ancillae: scratch qubits that are initialized at some state and later returned to that state and discarded.Existing quantum circuit languages let programmers assert that a qubit has been returned to the 0⟩ state before it is discarded, allowing for a range of optimizations.However, existing languages do not provide the tools to verify these assertions, introducing a potential source of errors.In this paper we present methods for verifying that ancillae are discarded in the desired state, and use these methods to implement a verified compiler from classical functions to quantum oracles.
A spatially explicit reinforcement learning model for geographic knowledge graph summarization

Abstract Web‐scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non‐spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.
Activating remote entanglement in a quantum network by local counting of identical particles

Quantum information and communication processing within quantum networks usually employs identical particles. Despite this, the physical role of quantum statistical nature of particles in large-scale networks remains elusive. Here, we show that just the indistinguishability of fermions makes it possible a new mechanism of entanglement transfer in many-node quantum networks. This process activates remote entanglement among distant sites, which do not share a common past, by only locally counting identical particles and classical communication. These results constitute the key achievement of the present technique and open the way to a more stable multistage transfer of nonlocal quantum correlations based on fermions.
Riker

This work studies product question answering (PQA) which aims to answer product-related questions based on customer reviews. Most recent PQA approaches adopt end2end semantic matching methodologies, which map questions and answers to a latent vector space to measure their relevance. Such methods often achieve superior performance but it tends to be difficult to interpret why. On the other hand, simple keyword-based search methods exhibit natural interpretability through matched keywords, but often suffer from the lexical gap problem. In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. Riker mines rich keyword representations of a question with two major components, internal word re-weighting and external word association, which predict the importance of each question word and associate the question with outside relevant keywords respectively, and can be jointly trained under weak supervision with large-scale QA pairs. The keyword representations from Riker can be directly used as input to a keyword-based search module, enabling the whole process to be effective while preserving good interpretability. We conduct extensive experiments using Amazon QA and review datasets from 5 different departments, and our results show that Riker substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations. In addition, we compare keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.
FEther: An Extensible Definitional Interpreter for Smart-Contract Verifications in Coq

Blockchain technology adds records to a list using cryptographic links. Therefore, the security of blockchain smart contracts is among the most popular contemporary research topics. To improve the theorem-proving technology in this field, we are developing an extensible hybrid verification tool chain, denoted as FSPVM-E, for Ethereum smart contract verification. This hybrid system extends the proof assistants in Coq, a formal proof-management system. Combining symbolic execution with higher-order theorem-proving, it solves consistency, automation, and reusability problems by standard theorem-proving approaches. This article completes the FSPVM-E by developing its proof engine. FSPVM-E is an extensible definitional interpreter based on our previous work FEther, which is totally developed in the Coq proof assistant. It supports almost all semantics of the Solidity programing language, and simultaneously executes multiple types of symbols. FEther also contains a set of automatic strategies that execute and verify the smart contracts in Coq with a high level of automation. The functional correctness of FEther was verified in Coq. In standard tutorials, the execution efficiency of FEther far exceeded that of the interpreters developed in Coq.
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

Chinese word segmentation (CWS) is a fundamental step of Chinese natural language processing. In this paper, we build a new toolkit, named PKUSEG, for multi-domain word segmentation. Unlike existing single-model toolkits, PKUSEG targets multi-domain word segmentation and provides separate models for different domains, such as web, medicine, and tourism. Besides, due to the lack of labeled data in many domains, we propose a domain adaptation paradigm to introduce cross-domain semantic knowledge via a translation system. Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain. We also further refine the performance of the default model with the help of synthetic data. Experiments show that PKUSEG achieves high performance on multiple domains. The new toolkit also supports POS tagging and model training to adapt to various application scenarios. The toolkit is now freely and publicly available for the usage of research and industry.
An Overview of Neural Network

Neural networks represent a brain metaphor for information processing.These models are biologically inspired rather than an exact replica of how the brain actually functions.Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data.This article aims to provide a brief overview of artificial neural network.The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task.It can learn either from available training patterns or automatically learn from examples or input-output relations.Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult.Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself.Artificial neural networks can learn by example like the way humans do.An artificial neural net is configured for a specific application like pattern recognition through a learning process.Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons.This is true of artificial neural networks as well.Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity.They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.
When resampling to cope with noise, use median, not mean

Due to their randomized nature, many nature-inspired heuristics are robust to some level of noise in the fitness evaluations. A common strategy to increase the tolerance to noise is to re-evaluate the fitness of a solution candidate several times and to then work with the average of the sampled fitness values. In this work, we propose to use the median instead of the mean. Besides being invariant to rescalings of the fitness, the median in many situations turns out to be much more robust than the mean. We show that when the noisy fitness is ϵ-concentrated, then a logarithmic number of samples suffice to discover the undisturbed fitness (via the median of the samples) with high probability. This gives a simple metaheuristic approach to transform a randomized optimization heuristics into one that is robust to this type of noise and that has a runtime higher than the original one only by a logarithmic factor. We show further that ϵ-concentrated noise occurs frequently in standard situations. We also provide lower bounds showing that in two such situations, even with larger numbers of samples, the average-resample strategy cannot efficiently optimize the problem in polynomial time.
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network

The goal of multi-modal learning is to use complementary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for fusing high level features obtained at intermediate layers of the deep neural network. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of the modalities degraded in quality. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature maps by combining the intermediate features obtained by the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The combining weights are determined by applying the convolutional layers followed by the sigmoid function to the concatenated intermediate feature maps. The whole network including the CNN backbone and GIF is trained in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve the robust performance in handling some degraded modalities.
Quantifying coherence with respect to general quantum measurements

Coherence is a cornerstone of quantum theory and a prerequisite for the advantage of quantum technologies. In recent work, the notion of coherence with respect to a general quantum measurement, i.e., positive operator-valued measure (POVM), was introduced and embedded into a resource-theoretic framework that generalizes the standard resource theory of coherence. In particular, POVM-incoherent (free) states and operations were established. In this work, we explore features of this framework which arise due to the rich structure of POVMs compared to projective measurements. Moreover, we introduce a rigorous, probabilisitic framework for POVM-based coherence measures and free operations. This leads to the introduction of strongly monotonic resource measures that neatly generalize well-known standard coherence measures. Finally, we show that the relative entropy of POVM coherence is equal to the cryptographic randomness gain, providing an important operational meaning to the concept of coherence with respect to a general measurement.
Unsupervised Post-Processing of Word Vectors via Conceptor Negation

Word vectors are at the core of many natural language processing tasks. Recently, there has been interest in post-processing word vectors to enrich their semantic information. In this paper, we introduce a novel word vector post-processing technique based on matrix conceptors (Jaeger 2014), a family of regularized identity maps. More concretely, we propose to use conceptors to suppress those latent features of word vectors having high variances. The proposed method is purely unsupervised: it does not rely on any corpus or external linguistic database. We evaluate the post-processed word vectors on a battery of intrinsic lexical evaluation tasks, showing that the proposed method consistently outperforms existing state-of-the-art alternatives. We also show that post-processed word vectors can be used for the downstream natural language processing task of dialogue state tracking, yielding improved results in different dialogue domains.
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.

Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations---which can be intractable in SCFMs, as branching on relaxations requires evaluating all (exponentially many) branching paths. Tractable alternatives mainly combine REINFORCE with complex control-variate schemes to improve the variance of naive estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs. Further, in contrast to the importance weighted autoencoder, we observe that RWS learns better models and inference networks with increasing numbers of particles. Our results suggest that RWS is a competitive, often preferable, alternative for learning SCFMs.