Reviewerly/exHarmony huggingface dataset:
A Sampling Approach to Generating Closely Interacting 3D Pose-Pairs from 2D Annotations

We introduce a data-driven method to generate a large number of plausible, closely interacting 3D human pose-pairs, for a given motion category, e.g., wrestling or salsa dance. With much difficulty in acquiring close interactions using 3D sensors, our approach utilizes abundant existing video data which cover many human activities. Instead of treating the data generation problem as one of reconstruction, either through 3D acquisition or direct 2D-to-3D data lifting from video annotations, we present a solution based on Markov Chain Monte Carlo (MCMC) sampling. Given a motion category and a set of video frames depicting the motion with the 2D pose-pair in each frame annotated, we start the sampling with one or few seed 3D pose-pairs which are manually created based on the target motion category. The initial set is then augmented by MCMC sampling around the seeds, via the Metropolis-Hastings algorithm and guided by a probability density function (PDF) that is defined by two terms to bias the sampling towards 3D pose-pairs that are physically valid and plausible for the motion category. With a focus on efficient sampling over the space of close interactions, rather than pose spaces, we develop a novel representation called interaction coordinates (IC) to encode both poses and their interactions in an integrated manner. Plausibility of a 3D pose-pair is then defined based on the IC and with respect to the annotated 2D pose-pairs from video. We show that our sampling-based approach is able to efficiently synthesize a large volume of plausible, closely interacting 3D pose-pairs which provide a good coverage of the input 2D pose-pairs.
Formation of Teachers’ Digital Competence: Domestic Challenges and Foreign Experience

Education system in Ukraine is undergoing large-scale reforms including modernization on different school levels and higher educational institutions. Students’ world view is laid out at school, that is why teachers must be ready to promote new content of education. According to the Concept “New School of Ukraine” among the key competencies of a student are the ability of intercultural communication in social and cultural contexts, digital competence, awareness of national identity as the basis of open-mindedness and respect for the diversity of cultural expression of others, and more. With this paper the author intends to initiate modifying demanded competencies and skills for contemporary primary school teachers based on cross-cultural experience. The article explains the necessity of forming a digital competence as a separate component of the professional skills of the modern teacher and shows its influence on the development of the personality of the modern student. Much attention is drawn to the domestic challenges in the implementation of digital devices in classrooms. Some foreign experience is analyzed in this research
Zero-Shot Learning Based on Deep Weighted Attribute Prediction

In zero-shot learning, attributes play as a bridge from original images to class labels. Therefore, to achieve accurate zero-shot image classification, we mainly focus on improving attribute prediction accuracy by taking full advantage of prior information about attribute from two aspects. First, we present a new attribute classifier called deep attribute prediction (DeepAP) model by using supervised deep convolutional neural networks (DCNNs), where the attribute label information participates in the training of DCNNs. Unlike common DCNNs that are usually used to extract image features, the constructed DCNNs are used to directly predict attribute values from the original input images. Thus, the designed DeepAP model can serve as the mapping from low-level image features to high-level semantic attributes in the traditional direct attribute prediction (DAP) model. Second, another prior information about attribute, i.e., class-attribute matrix is used to mine the attribute-class correlation with sparse representation coefficients. Since the attribute-class correlation can reflect different contributions of attributes to classification, we use it to define attribute weights and incorporate the idea of weighted attributes into DeepAP to form the deep weighted attribute prediction (DWAP) model. Experiments on three real datasets show that DWAP outperforms the deep attribute network and DAP on attribute prediction and zero-shot image classification.
Forest Packing: Fast Parallel, Decision Forests

Decision Forests are popular machine learning techniques that assist scientists to extract knowledge from massive data sets. This class of tool remains popular because of their interpretability and ease of use, unlike other modern machine learning methods, such as kernel machines and deep learning. Decision forests also scale well for use with large data because training and run time operations are trivially parallelizable allowing for high inference throughputs. A negative aspect of these forests, and an untenable property for many real time applications, is their high inference latency caused by the combination of large model sizes with random memory access patterns. We present memory packing techniques and a novel tree traversal method to overcome this deficiency. The result of our system is a grouping of trees into a hierarchical structure. At low levels, we pack the nodes of multiple trees into contiguous memory blocks so that each memory access fetches data for multiple trees. At higher levels, we use leaf cardinality to identify the most popular paths through a tree and collocate those paths in contiguous cache lines. We extend this layout with a re-ordering of the tree traversal algorithm to take advantage of the increased memory throughput provided by out-of-order execution and cache-line prefetching. Together, these optimizations increase the performance and parallel scalability of classification in ensembles by a factor of ten over an optimized C++ implementation and a popular R-language implementation.
A Kronecker Product Model for Repeated Pattern Detection on 2D Urban Images

Repeated patterns (such as windows, balconies, and doors) are prominent and significant features in urban scenes. Therefore, detection of these repeated patterns becomes very important for city scene analysis. This paper attacks the problem of repeated pattern detection in a precise, efficient and automatic way, by combining traditional feature extraction with a Kronecker product based low-rank model. We introduced novel algorithms that extract repeated patterns from rectified images with solid theoretical support. Our method is tailored for 2D images of building façades and tested on a large set of façade images.
Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach

Abstract Weak lensing shear estimation typically results in per galaxy statistical errors significantly larger than the sought after gravitational signal of only a few percent. These statistical errors are mostly a result of shape-noise — an estimation error due to the diverse (and a-priori unknown) morphology of individual background galaxies. These errors are inversely proportional to the limiting angular resolution at which localized objects, such as galaxy clusters, can be probed with weak lensing shear. In this work we report on our initial attempt to reduce statistical errors in weak lensing shear estimation using a machine learning approach — training a multi-layered convolutional neural network to directly estimate the shear given an observed background galaxy image. We train, calibrate and evaluate the performance and stability of our estimator using simulated galaxy images designed to mimic the distribution of HST observations of lensed background sources in the CLASH galaxy cluster survey. Using the trained estimator, we produce weak lensing shear maps of the cores of 20 galaxy clusters in the CLASH survey, demonstrating an RMS scatter reduced by approximately 26% when compared to maps produced with a commonly used shape estimator. This is equivalent to a survey speed enhancement of approximately 60%. However, given the non-transparent nature of the machine learning approach, this result requires further testing and validation. We provide python code to train and test this estimator on both simulated and real galaxy cluster observations. We also provide updated weak lensing catalogues for the 20 CLASH galaxy clusters studied.
Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-Memory OLAP

OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional surrogate key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
Dynamical fidelity susceptibility of decoherence-free subspaces

In idealized models of a quantum register and its environment, quantum information can be stored indefinitely by encoding it into a decoherence-free subspace (DFS). Nevertheless, perturbations to the idealized register-environment coupling will cause decoherence in any realistic setting. Expanding a measure for state preservation, the dynamical fidelity, in powers of the strength of the perturbations, we prove stability to linear order is a generic property of quantum state evolution. The effect of noise perturbation is quantified by a concise expression for the strength of the quadratic leading order, which we define as the dynamical fidelity susceptibility of DFSs. Under the physical restriction that noise acts on the register $k$-locally, this susceptibility is bounded from above by a polynomial in the system size. These general results are illustrated by two physically relevant examples. Knowledge of the susceptibility can be used to increase coherence times of future quantum computers.
Quantum simulation and optimization in hot quantum networks

We propose and analyze a setup based on (solid-state) qubits coupled to a common multi-mode transmission line, which allows for coherent spin-spin interactions over macroscopic on-chip distances, without any ground-state cooling requirements for the data bus. Our approach allows for the realization of fast deterministic quantum gates between distant qubits, the simulation of quantum spin models with engineered (long-range) interactions, and provides a flexible architecture for the implementation of quantum approximate optimization algorithms.
Unusual scaling in a discrete quantum walk with random long range steps

A discrete time quantum walker is considered in one dimension, where at each step, the translation can be more than one unit length chosen randomly. In the simplest case, the probability that the distance travelled is ℓ is taken as P(ℓ)=αδ(ℓ−1)+(1−α)δ(ℓ−2n) with n>1. Even the n=1 case shows a drastic change in the scaling behaviour for any α≠0,1. Specifically, 〈x2〉∝t3∕2 for 0<α<1 implying the walk is slower compared to the usual quantum walk. This scaling behaviour, which is neither conventional quantum nor classical, can be justified using a simple form for the probability density. The decoherence effect is characterised by two parameters which vanish in a power law manner close to α=0 and 1 with an exponent ≈0.5. It is also shown that randomness is the essential ingredient for the decoherence effect.
Reasonable method to extract Fisher information from experimental data

Fisher information (FI) plays a crucial role in quantum precision measurement and entanglement detection. Recently two methods have been suggested to extract it from experimental data: Hellinger distance and Kullback–Leibler entropy. In this paper, an extensive investigation is considered with the help of a dichotomic measurement model. It is found that the general quadratic fitting for FI with both methods has two constraints: one is the critical visibility V0=2∕3, the other is the smallest δθ for the dichotomic measurement. To relax them, we propose the higher-order fitting (fourth order considered), by which a reasonable FI is obtained from recent optical experimental data.
Learning Latent Stable Patterns for Image Understanding With Weak and Noisy Labels

This paper focuses on weakly supervised image understanding, in which the semantic labels are available only at image-level, without the specific object or scene location in an image. Existing algorithms implicitly assume that image-level labels are error-free, which might be too restrictive. In practice, image labels obtained from the pretrained predictors are easily contaminated. To solve this problem, we propose a novel algorithm for weakly supervised segmentation when only noisy image labels are available during training. More specifically, a semantic space is constructed first by encoding image labels through a graphlet (i.e., superpixel cluster) embedding process. Then, we observe that in the semantic space, the distribution of graphlets from images with a same label remains stable, regardless of the noises in image labels. Therefore, we propose a generative model, called latent stability analysis, to discover the stable patterns from images with noisy labels. Inferring graphlet semantics by making use of these mid-level stable patterns is much more secure and accurate than directly transferring noisy image-level labels into different regions. Finally, we calculate the semantics of each superpixel using maximum majority voting of its correlated graphlets. Comprehensive experimental results show that our algorithm performs impressively when the image labels are predicted by either the hand-crafted or deeply learned image descriptors.
A unified framework and models for integrating translation memory into phrase-based statistical machine translation

Since statistical machine translation (SMT) and translation memory (TM) complement each other in TM matched and unmatched regions, a unified framework for integrating TM into phrase-based SMT is proposed in this paper. Unlike previous two-stage pipeline approaches, which directly merge TM results into the input sentences and subsequently let the SMT only translates those unmatched regions, the proposed framework refers to the corresponding TM information associated with each phrase at the SMT decoding. Under this unified framework, several integrated models are proposed to incorporate different types of information extracted from TM to guide the SMT decoding. We thus let SMT implicitly and indirectly utilize global context with a local dependency model. Furthermore, the SMT phrase table is dynamically enhanced with TM phrase pairs when the TM database and the SMT training set are different. On a Chinese–English TM database, our experiments show that the proposed Model-I significantly improves over both SMT and TM when the SMT training set is also adopted as the TM database and when the fuzzy match score is over 0.4 (overall 3.5 BLEU points improvement and 2.6 TER points reduction). In addition, the proposed Model-II is significantly better than the TM and the SMT systems when the SMT training set and the TM database are different. Furthermore, the proposed Model-III outperforms both the TM and the SMT systems even when the SMT training set and the TM database are from different domains. Additionally, the proposed Model-IV further achieves significant improvements with the help of Top-N TM sentence pairs. Lastly, all our models significantly outperform those state-of-the-art approaches under all test conditions.
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our system first splits the documents into topical segments, "visualizes" the matchings between the query and the segments, and then feeds an interaction matrix into a Neu-IR model, DeepTileBars, to obtain the final ranking scores. DeepTileBars models the relevance signals occurring at different granularities in a document's topic hierarchy. It better captures the discourse structure of a document and thus the matching patterns. Although its design and implementation are light-weight, DeepTileBars outperforms other state-of-the-art Neu-IR models on benchmark datasets including the Text REtrieval Conference (TREC) 2010-2012 Web Tracks and LETOR 4.0.
Symmetries among Multivariate Information Measures Explored Using Möbius Operators

Relations between common information measures include the duality relations based on Möbius inversion on lattices, which are the direct consequence of the symmetries of the lattices of the sets of variables (subsets ordered by inclusion). In this paper we use the lattice and functional symmetries to provide a unifying formalism that reveals some new relations and systematizes the symmetries of the information functions. To our knowledge, this is the first systematic examination of the full range of relationships of this class of functions. We define operators on functions on these lattices based on the Möbius inversions that map functions into one another, which we call Möbius operators, and show that they form a simple group isomorphic to the symmetric group S3. Relations among the set of functions on the lattice are transparently expressed in terms of the operator algebra, and, when applied to the information measures, can be used to derive a wide range of relationships among diverse information measures. The Möbius operator algebra is then naturally generalized which yields an even wider range of new relationships.
A new method estimating linear gaussian filter kernel by image PRNU noise

In the paper, we present a method estimating the linear Gaussian filter kernel by means of photo-response non-uniformity (PRNU) noise. In the past decade, a large number of works have proved that PRNU noise is a reliable fingerprint for digital image. Accordingly, PRNU noise has been widely used in such fields as camera identification and image manipulation detection. Specifically, in order to detect whether the query image is a Gaussian filtered image and estimate the filter kernel of the Gaussian filtered image, the proposed paper takes fully the camera reference PRNU noise as the identification fingerprint, and we correlate the identification fingerprint with the noise residual extracted from clean image and noise residual extracted from filtered image, respectively. Mathematically, the linear correlation between the two cross-correlation can be determined. In addition, due to the unknown of clean image, how to access to a clean image without additive noise is crucial in this application. Consequently, we propose to use the method of recapturing the query image instead of randomly shooting a large number of content-irrelevant images. It exhibits better estimation result compared to the existing techniques. Moreover, we observe that the proposed method is robust to JPEG compression.
Quantum algorithms and approximating polynomials for composed functions with shared inputs

We give new quantum algorithms for evaluating composed functions whose inputs may be shared between bottom-level gates. Let f be a Boolean function and consider a function F obtained by applying f to conjunctions of possibly overlapping subsets of n variables. If f has quantum query complexity Q(f), we give an algorithm for evaluating F using quantum queries. This improves on the bound of that follows by treating each conjunction independently, and is tight for worst-case choices of f. Using completely different techniques, we prove a similar tight composition theorem for the approximate degree of f.By recursively applying our composition theorems, we obtain a nearly optimal Õ(n1–2–d) upper bound on the quantum query complexity and approximate degree of linear-size depth-d AC0 circuits. As a consequence, such circuits can be PAC learned in subexponential time, even in the challenging agnostic setting. Prior to our work, a subexponential-time algorithm was not known even for linear-size depth-3 AC0 circuits. We also show that any substantially faster learning algorithm will require fundamentally new techniques.
Covariance control joint integrated probabilistic data association filter for multi‐target tracking

The joint integrated probabilistic data association (JIPDA) filter is effective for automatic multi-target tracking in cluttered environment. At each time step, the posterior probability density function (PDF) is approximated by a Gaussian PDF to estimate track states. However, JIPDA suffers from the track coalescence problem. When targets are closely spaced, their tracking gates may overlap, so that the track state estimates become biased and tend to coalesce. The authors note that the covariance can influence both the size of the tracking gate and the accuracy of the Gaussian approximation. Therefore, in this study, the posterior PDF is optimised by controlling the covariance to improve the accuracy of the track state estimates. A cost function measuring the track estimate error covariance is developed as the optimisation criterion, and an iterative strategy is adopted for minimising it. Finally, the posterior PDF with the minimal cost function is derived as the desired one. The theoretical analysis and example show that the proposed approach can reduce the extent of tracking gate overlap and improve the accuracy of the Gaussian approximation. Simulation results show that the new approach can handle track coalescence and performs better than the traditional methods.
Active Learning of Multi-Class Classification Models from Ordered Class Sets

In this paper, we study the problem of learning multi-class classification models from a limited set of labeled examples obtained from human annotator. We propose a new machine learning framework that learns multi-class classification models from ordered class sets the annotator may use to express not only her top class choice but also other competing classes still under consideration. Such ordered sets of competing classes are common, for example, in various diagnostic tasks. In this paper, we first develop strategies for learning multi-class classification models from examples associated with ordered class set information. After that we develop an active learning strategy that considers such a feedback. We evaluate the benefit of the framework on multiple datasets. We show that class-order feedback and active learning can reduce the annotation cost both individually and jointly.