Reviewerly/exHarmony huggingface dataset:
Stochastic Deep Networks

Machine learning is increasingly targeting areas where input data cannot be accurately described by a single vector, but can be modeled instead using the more flexible concept of random vectors, namely probability measures or more simply point clouds of varying cardinality. Using deep architectures on measures poses, however, many challenging issues. Indeed, deep architectures are originally designed to handle fixedlength vectors, or, using recursive mechanisms, ordered sequences thereof. In sharp contrast, measures describe a varying number of weighted observations with no particular order. We propose in this work a deep framework designed to handle crucial aspects of measures, namely permutation invariances, variations in weights and cardinality. Architectures derived from this pipeline can (i) map measures to measures - using the concept of push-forward operators; (ii) bridge the gap between measures and Euclidean spaces - through integration steps. This allows to design discriminative networks (to classify or reduce the dimensionality of input measures), generative architectures (to synthesize measures) and recurrent pipelines (to predict measure dynamics). We provide a theoretical analysis of these building blocks, review our architectures' approximation abilities and robustness w.r.t. perturbation, and try them on various discriminative and generative tasks.
A reasonably exceptional type theory

Traditional approaches to compensate for the lack of exceptions in type theories for proof assistants have severe drawbacks from both a programming and a reasoning perspective. Pédrot and Tabareau recently extended the Calculus of Inductive Constructions (CIC) with exceptions. The new exceptional type theory is interpreted by a translation into CIC, covering full dependent elimination, decidable type-checking and canonicity. However, the exceptional theory is inconsistent as a logical system. To recover consistency, Pédrot and Tabareau propose an additional translation that uses parametricity to enforce that all exceptions are caught locally. While this enforcement brings logical expressivity gains over CIC, it completely prevents reasoning about exceptional programs such as partial functions. This work addresses the dilemma between exceptions and consistency in a more flexible manner, with the Reasonably Exceptional Type Theory (RETT). RETT is structured in three layers: (a) the exceptional layer, in which all terms can raise exceptions; (b) the mediation layer, in which exceptional terms must be provably parametric; (c) the pure layer, in which terms are non-exceptional, but can refer to exceptional terms. We present the general theory of RETT, where each layer is realized by a predicative hierarchy of universes, and develop an instance of RETT in Coq: the impure layer corresponds to the predicative universe hierarchy, the pure layer is realized by the impredicative universe of propositions, and the mediation layer is reified via a parametricity type class. RETT is the first full dependent type theory to support consistent reasoning about exceptional terms, and the CoqRETT plugin readily brings this ability to Coq programmers.
Differentially Private Online Submodular Minimization

In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. Our first result is in the full information setting, where the algorithm can observe the entire function after making its decision at each time step. We give an algorithm in this setting that is $\eps$-differentially private and achieves expected regret $\tilde{O}\left(\frac{n\sqrt{T}}{\eps}\right)$ over $T$ rounds for a collection of $n$ elements. Our second result is in the bandit setting, where the algorithm can only observe the cost incurred by its chosen set, and does not have access to the entire function. This setting is significantly more challenging due to the limited information. Our algorithm using bandit feedback is $\eps$-differentially private and achieves expected regret $\tilde{O}\left(\frac{n^{3/2}T^{2/3}}{\eps}\right)$.
Capture4VR

course Capture4VR: from VR photography to VR video Share on Authors: Christian Richardt University of Bath University of BathView Profile , Peter Hedman UCL UCLView Profile , Ryan S. Overbeck Google GoogleView Profile , Brian Cabral Facebook FacebookView Profile , Robert Konrad Stanford University Stanford UniversityView Profile , Steve Sullivan Microsoft MicrosoftView Profile Authors Info & Affiliations SIGGRAPH '19: ACM SIGGRAPH 2019 CoursesJuly 2019 Article No.: 4Pages 1–319https://doi.org/10.1145/3305366.3328028Published:28 July 2019 6citation313DownloadsMetricsTotal Citations6Total Downloads313Last 12 Months33Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Network distance education platform control system based on big data

In this paper, an overall framework of the control system of the network distance education platform is designed. The RTCP control structure Word is used to handle the context-aware control system of the network distance education platform, and the centralised control method is used to design the user interaction experience, the function of each module of the control system is analysed. The big data integration scheduling model of the network distance education platform is constructed. The experimental results show that the average computational cost of resource scheduling consumed by the proposed method was 32.34 ms, which is 12.6% and 23.7% faster than the other two traditional methods. It shows that the designed network distance education platform control system consumes shorter execution time and can provide better output stability, higher accuracy of big data resource scheduling, and good control performance.
The Role of Memory in Stochastic Optimization

The choice of how to retain information about past gradients dramatically affects the convergence properties of state-of-the-art stochastic optimization methods, such as Heavy-ball, Nesterov's momentum, RMSprop and Adam. Building on this observation, we use stochastic differential equations (SDEs) to explicitly study the role of memory in gradient-based algorithms. We first derive a general continuous-time model that can incorporate arbitrary types of memory, for both deterministic and stochastic settings. We provide convergence guarantees for this SDE for weakly-quasi-convex and quadratically growing functions. We then demonstrate how to discretize this SDE to get a flexible discrete-time algorithm that can implement a board spectrum of memories ranging from short- to long-term. Not only does this algorithm increase the degrees of freedom in algorithmic choice for practitioners but it also comes with better stability properties than classical momentum in the convex stochastic setting. In particular, no iterate averaging is needed for convergence. Interestingly, our analysis also provides a novel interpretation of Nesterov's momentum as stable gradient amplification and highlights a possible reason for its unstable behavior in the (convex) stochastic setting. Furthermore, we discuss the use of long term memory for second-moment estimation in adaptive methods, such as Adam and RMSprop. Finally, we provide an extensive experimental study of the effect of different types of memory in both convex and nonconvex settings.
Semantic Categorization of Software Bug Repositories for Severity Assignment Automation

Bug triage is one of the crucial activities undertaken during the maintenance phase of large-scale software projects, to fix the bugs that appear. In this paper we propose an approach to automate one of the important activities of bug triage which is the bug severity assignment. The proposed approach is based on mining the historical bug repositories of software projects. It utilizes the Hierarchical Dirichlet Process (HDP) topic modeller to extract the topics shared by the historical bug reports, then categorizing them according to their proportions in the extracted topics using the K-means clustering algorithm. For each new submitted report, the top K similar reports are retrieved from their cluster using a novel weighted K-nearest neighbour algorithm that utilizes a similarity measure called Improved-Sqrt-Cosine similarity. The severity level of the new bug is assigned using a Dual-weighted voting scheme. The experimental results demonstrated that our proposed model improved the performance of the bug severity assignment task when compared against three baseline models in the context of two popular bug repositories, Eclipse and Mozilla.
Explanations for Query Answers under Existential Rules

Ontology-mediated query answering is an extensively studied paradigm, which aims at improving query answers with the use of a logical theory. As a form of logical entailment, ontology-mediated query answering is fully interpretable, which makes it possible to derive explanations for query answers. Surprisingly, however, explaining answers for ontology-mediated queries has received little attention for ontology languages based on existential rules. In this paper, we close this gap, and study the problem of explaining query answers in terms of minimal subsets of database facts. We provide a thorough complexity analysis for several decision problems associated with minimal explanations under existential rules.
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data. Many dimensionality reduction methods have been widely adopted to extract low-dimensional, smooth and time-evolving latent trajectories. However, simple state transition structures, linear embedding assumptions, or inflexible inference networks impede the accurate recovery of dynamic portraits. In this paper, we propose a novel latent dynamic model that is capable of capturing nonlinear, non-Markovian, long short-term time-dependent dynamics via recurrent neural networks and tackling complex nonlinear embedding via non-parametric Gaussian process. Due to the complexity and intractability of the model and its inference, we also provide a powerful inference network with bi-directional long short-term memory networks that encode both past and future information into posterior distributions. In the experiment, we show that our model outperforms other state-of-the-art methods in reconstructing insightful latent dynamics from both simulated and experimental neural datasets with either Gaussian or Poisson observations, especially in the low-sample scenario. Our codes and additional materials are available at this https URL.
Estimating Shortwave Clear‐Sky Fluxes From Hourly Global Radiation Records by Quantile Regression

Abstract Estimates of radiative fluxes under cloud‐free conditions (“clear‐sky”) are required in many fields, from climatic analyses of solar transmission to estimates of solar energy potential for electricity generation. Ideally, these fluxes can be obtained directly from measurements of solar fluxes at the surface. However, common standard methods to identify clear‐sky conditions require observations of both the total and the diffuse radiative fluxes at very high temporal resolution of minutes, which restricts these methods to a few, well‐equipped sites. Here we propose a simple method to estimate clear‐sky fluxes only from typically available global radiation measurements (R sd ) at (half‐)hourly resolution. Plotting a monthly sample of observed R sd against the corresponding incoming solar radiation at the top of atmosphere (potential solar radiation) reveals a typical triangle shape with clear‐sky conditions forming a distinct, linear slope in the upper range of observations. This upper slope can be understood as the fractional transmission of solar radiation representative for cloud‐free conditions of the sample period. We estimate this upper slope through quantile regression. We employ data of 42 stations of the worldwide Baseline Surface Radiation Network to compare our monthly estimates with the standard clear‐sky identification method developed by Long and Ackerman (2000, https://doi.org/10.1029/2000JD900077 ). We find very good agreement of the derived fractional solar transmission ( R 2 = 0.73) across sites. These results thus provide confidence in applying the proposed method to the larger set of global radiation measurements to obtain further observational constraints on clear‐sky fluxes and cloud radiative effects.
Optimized dithering technique in frequency domain for high-quality three-dimensional depth data acquisition*

On the basis of the objective functions, dithering optimization techniques can be divided into the intensity-based optimization technique and the phase-based optimization technique. However, both types of techniques are spatial-domain optimization techniques, while their measurement performances are essentially determined by the harmonic components in the frequency domain. In this paper, a novel genetic optimization technique in the frequency domain is proposed for high-quality fringe generation. In addition, to handle the time-consuming difficulty of genetic algorithm (GA), we first optimize a binary patch, then join the optimal binary patches together according to periodicity and symmetry so as to generate a full-size pattern. It is verified that the proposed technique can significantly enhance the measured performance and ensure the robustness to various amounts of defocusing.
The design of electronic medical records for patients of continuous care

In order to meet the ever-increasing needs of health care, as well as helping patients who need continuous care after being discharged from the hospital and making modern medical technology better serve humans, the design of electronic medical records for continuous care patients, especially those with malignant tumors, is investigated. In the research process, the idea of Browser/Server (B/S) framework is adopted, and the corresponding electronic medical record system is designed based on the targets and the overall structure of the system; afterward, the black-box testing and white-box testing are carried out to test the functions, reliability, and stability of the designed electronic medical record system; in addition, combined with other research results, the feasibility of the design is proved. It can be seen that the electronic medical record system designed for patients who need continuous care in the study is absolutely feasible, which can be further researched and improved with the development of electronic medical records; therefore, it would make greater contributions to both patients and hospitals in the future.
Einstein-Podolsky-Rosen steering in Gaussian weighted graph states

Einstein-Podolsky-Rosen (EPR) steering, as one of the most intriguing phenomenon of quantum mechanics, is a useful quantum resource for quantum communication. Understanding the type of EPR steering in a graph state is the basis for application of it in a quantum network. In this paper, we present EPR steering in a Gaussian weighted graph state, including a linear tripartite and a four-mode square weighted graph state. The dependence of EPR steering on weight factor in the weighted graph state is analyzed. Gaussian EPR steering between two modes of a weighted graph state is presented, which does not exist in the Gaussian cluster state (where the weight factor is unit). For the four-mode square Gaussian weighted graph state, EPR steering between one and its two nearest modes is also presented, which is absent in the four-mode square Gaussian cluster state. We also show that Gaussian EPR steering in a weighted graph state is also bounded by the Coffman-Kundu-Wootters monogamy relation. The presented results are useful for exploiting EPR steering in a Gaussian weighted graph state as a valuable resource in multiparty quantum communication tasks.
The LAIX Systems in the BEA-2019 GEC Shared Task

In this paper, we describe two systems we developed for the three tracks we have participated in the BEA-2019 GEC Shared Task.We investigate competitive classification models with bi-directional recurrent neural networks (Bi-RNN) and neural machine translation (NMT) models.For different tracks, we use ensemble systems to selectively combine the NMT models, the classification models, and some rules, and demonstrate that an ensemble solution can effectively improve GEC performance over single systems.Our GEC systems ranked the first in the Unrestricted Track, and the third in both the Restricted Track and the Low Resource Track.
Combinatorial Inference against Label Noise

Label noise is one of the critical sources that degrade generalization performance of deep neural networks significantly. To handle the label noise issue in a principled way, we propose a unique classification framework of constructing multiple models in heterogeneous coarse-grained meta-class spaces and making joint inference of the trained models for the final predictions in the original (base) class space. Our approach reduces noise level by simply constructing meta-classes and improves accuracy via combinatorial inferences over multiple constituent classifiers. Since the proposed framework has distinct and complementary properties for the given problem, we can even incorporate additional off-the-shelf learning algorithms to improve accuracy further. We also introduce techniques to organize multiple heterogeneous meta-class sets using $k$-means clustering and identify a desirable subset leading to learn compact models. Our extensive experiments demonstrate outstanding performance in terms of accuracy and efficiency compared to the state-of-the-art methods under various synthetic noise configurations and in a real-world noisy dataset.
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

In this work, we consider one challenging training time attack by modifying training data with bounded perturbation, hoping to manipulate the behavior (both targeted or non-targeted) of any corresponding trained classifier during test time when facing clean samples. To achieve this, we proposed to use an auto-encoder-like network to generate such adversarial perturbations on the training data together with one imaginary victim differentiable classifier. The perturbation generator will learn to update its weights so as to produce the most harmful noise, aiming to cause the lowest performance for the victim classifier during test time. This can be formulated into a non-linear equality constrained optimization problem. Unlike GANs, solving such problem is computationally challenging, we then proposed a simple yet effective procedure to decouple the alternating updates for the two networks for stability. By teaching the perturbation generator to hijacking the training trajectory of the victim classifier, the generator can thus learn to move against the victim classifier step by step. The method proposed in this paper can be easily extended to the label specific setting where the attacker can manipulate the predictions of the victim classifier according to some predefined rules rather than only making wrong predictions. Experiments on various datasets including CIFAR-10 and a reduced version of ImageNet confirmed the effectiveness of the proposed method and empirical results showed that, such bounded perturbations have good transferability across different types of victim classifiers.
Journal Editors and Altmetrics

![Figure][1] Not everything that can be counted counts, and not everything that counts can be counted.—William Bruce Cameron [(1)][2]![Figure][1] The recently concluded impact factor season reignites, as it does each year, interest in the vexing question of how we should measure
Cross-Linguistic Semantic Annotation: Reconciling the Language-Specific and the Universal

Developers of cross-lingual semantic annotation schemes face a number of issues not encountered in monolingual annotation.This paper discusses four such issues, related to the establishment of annotation labels, and the treatment of languages with more fine-grained, more coarse-grained, and cross-cutting categories.We propose that a lattice-like architecture of the annotation categories can adequately handle all four issues, and at the same time remain both intuitive for annotators and faithful to typological insights.This position is supported by a brief annotation experiment.
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant

We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, obtaining, selecting, and labeling relevant data is expensive. This work presents a simple technique that automatically identifies new helpful examples suitable for human annotation. Our experimental results show that the proposed method, compared with random-selection and entropy-based methods, leads to higher accuracy improvements given a fixed annotation budget. Although developed and tested in the setting of a commercial intelligent assistant, the technique is of wider applicability.
Aggressive Social Media Post Detection System Containing Symbolic Images

Social media platforms are an inexpensive communication medium help to reach other users very quickly. The same benefit is also utilized by some mischievous users to post objectionable images and symbols to certain groups of people. This types of posts include cyber-aggression, cyberbullying, offensive content, and hate speech. In this work, we analyze images posted on online social media sites to hurt online users. In this research, we designed a deep learning based system to classify aggressive post from a non-aggressive post containing symbolic images. To show the effectiveness of our model, we created a dataset crawling images from Google search to query aggressive images. The validation shows promising results.
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme (G2P) models are a key component in Automatic Speech Recognition (ASR) systems, such as the ASR system in Alexa, as they are used to generate pronunciations for out-of-vocabulary words that do not exist in the pronunciation lexicons (mappings like e c h o to E k oU). Most G2P systems are monolingual and based on traditional joint-sequence based n-gram models [1,2]. As an alternative, we present a single end-to-end trained neural G2P model that shares same encoder and decoder across multiple languages. This allows the model to utilize a combination of universal symbol inventories of Latin-like alphabets and cross-linguistically shared feature representations. Such model is especially useful in the scenarios of low resource languages and code switching/foreign words, where the pronunciations in one language need to be adapted to other locales or accents. We further experiment with word language distribution vector as an additional training target in order to improve system performance by helping the model decouple pronunciations across a variety of languages in the parameter space. We show 7.2% average improvement in phoneme error rate over low resource languages and no degradation over high resource ones compared to monolingual baselines.
Predictability limits in session-based next item recommendation

Session-based recommendations are based on the user's recent actions, for example, the items they have viewed during the current browsing session or the sightseeing places they have just visited. Closely related is sequence-aware recommendation, where the choice of the next item should follow from the sequence of previous actions.