Reviewerly/exHarmony huggingface dataset:
Learning Deformable Image Registration with Structure Guidance Constraints for Adaptive Radiotherapy

Accurate registration of CT and CBCT images is key for adaptive radiotherapy. A particular challenge is the alignment of flexible organs, such as bladder or rectum, that often yield extreme deformations. In this work we analyze the impact of so-called structure guidance for learning based registration when additional segmentation information is provided to a neural network. We present a novel weakly supervised deep learning based method for multi-modal 3D deformable CT-CBCT registration with structure guidance constraints. Our method is not supervised by ground-truth deformations and we use the energy functional of a variational registration approach as loss for training. Incorporating structure guidance constraints in our learning based approach results in an average Dice score of $$0.91\pm 0.08$$ compared to a score of $$0.76\pm 0.15$$ for the same method without constraints. An iterative registration approach with structure guidance results in a comparable average Dice score of $$0.91\pm 0.09$$. However, learning based registration requires only a single pass through the network, yielding computation of a deformation fields in less than 0.1 s which is more than 100 times faster than the runtime of iterative registration.
Influence Paths for Characterizing Subject-Verb Number Agreement in LSTM Language Models

LSTM-based recurrent neural networks are the state-of-the-art for many natural language processing (NLP) tasks.Despite their performance, it is unclear whether, or how, LSTMs learn structural features of natural languages such as subject-verb number agreement in English.Lacking this understanding, the generality of LSTMs on this task and their suitability for related tasks remains uncertain.Further, errors cannot be properly attributed to a lack of structural capability, training data omissions, or other exceptional faults.We introduce influence paths, a causal account of structural properties as carried by paths across gates and neurons of a recurrent neural network.The approach refines the notion of influence (the subject's grammatical number has influence on the grammatical number of the subsequent verb) into a set of gate-level or neuron-level paths.The set localizes and segments the concept (e.g., subject-verb agreement), its constituent elements (e.g., the subject), and related or interfering elements (e.g., attractors).We exemplify the methodology on a widely-studied multi-layer LSTM language model, demonstrating its accounting for subject-verb number agreement.The results offer both a finer and a more complete view of an LSTM's handling of this structural aspect of the English language than prior results based on diagnostic classifiers and ablation.
Introducing structure to expedite quantum searching

We present a novel quantum algorithm for solving the unstructured search problem with one marked element. Our algorithm allows generating quantum circuits that use asymptotically fewer additional quantum gates than the famous Grover's algorithm and may be successfully executed on NISQ devices. We prove that our algorithm is optimal in the total number of elementary gates up to a multiplicative constant. As many NP-hard problems are not in fact unstructured, we also describe the \emph{partial uncompute} technique which exploits the oracle structure and allows a significant reduction in the number of elementary gates required to find the solution. Combining these results allows us to use asymptotically smaller number of elementary gates than the Grover's algorithm in various applications, keeping the number of queries to the oracle essentially the same. We show how the results can be applied to solve hard combinatorial problems, for example Unique k-SAT. Additionally, we show how to asymptotically reduce the number of elementary gates required to solve the unstructured search problem with multiple marked elements.
Co-Saliency Spatio-Temporal Interaction Network for Person Re-Identification in Videos

Person re-identification aims at identifying a certain pedestrian across non-overlapping camera networks. Video-based person re-identification approaches have gained significant attention recently, expanding image-based approaches by learning features from multiple frames. In this work, we propose a novel Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person re-identification in videos. It captures the common salient foreground regions among video frames and explores the spatial-temporal long-range context interdependency from such regions, towards learning discriminative pedestrian representation. Specifically, multiple co-saliency learning modules within CSTNet are designed to utilize the correlated information across video frames to extract the salient features from the task-relevant regions and suppress background interference. Moreover, multiple spatial-temporal interaction modules within CSTNet are proposed, which exploit the spatial and temporal long-range context interdependencies on such features and spatial-temporal information correlation, to enhance feature representation. Extensive experiments on two benchmarks have demonstrated the effectiveness of the proposed method.
Framework of Confidence Values during Digital Forensic Investigation Processes

The advancement of Internet of Things (IoT) devices is continuously progressing and such development also enables a number of issues to arise which increases the complexity in the forensic investigation of the IoT. Globally, investigators are faced with challenges in ways of retrieving evidence from the different areas of the IoT environment, which includes Devices, Networks and the Cloud. One of the most crucial steps during forensic investigations is the writing up and creation of a case report which then needs to be presented in the court of law. In this paper, we propose models to estimate the confidence values of evidence, investigators and case reports to ensure case investigation accuracy and improve the evidential values of case presentation as well as evidence sharing of sensitive data worldwide.
Enhancement of Retinal Fundus Images via Pixel Color Amplification

We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distortion model underlying dehazing theory shows how three existing priors commonly used by the dehazing community and a novel fourth prior are related. We utilize the theory to develop a family of enhancement methods for retinal images, including novel methods for whole image brightening and darkening. We show a novel derivation of the Unsharp Masking algorithm. We evaluate the enhancement methods as a pre-processing step to a challenging multi-task segmentation problem and show large increases in performance on all tasks, with Dice score increases over a no-enhancement baseline by as much as 0.491. We provide evidence that our enhancement preprocessing is useful for unbalanced and difficult data. We show that the enhancements can perform class balancing by composing them together.
LGSLRR: Towards fusing discriminative ordinal local and global structured low-rank representation for image recognition

Structural information extraction has been a focal technique in many classification applications, such as image recognition and biometrics. However, it remains a challenge to simultaneously utilize local and global structural information in a classification model. In addition, in terms of the local information, the existing methods mainly seek to extract or preserve the first-order structure while ignoring the useful ordinal structural information for classification. To this end, this paper presents a discriminative ordinal local and global structured low-rank representation (LGSLRR) model that jointly preserves the local ordinal structure and global structure for image recognition. A discriminative block-diagonal low-rank representation is employed to obtain global information while the first-order and second-order local information is preserved by a joint graph based manifold embedding with two different Laplacian matrices. Some extensive comparison experiments on ten public image datasets are performed, and the results demonstrate the effectiveness and significant performance of the proposed method over some state-of-the-art methods.
When OT meets MoM: Robust estimation of Wasserstein Distance

Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of efficient approximations. In this work, we consider the problem of estimating the Wasserstein distance between two probability distributions when observations are polluted by outliers. To that end, we investigate how to leverage Medians of Means (MoM) estimators to robustify the estimation of Wasserstein distance. Exploiting the dual Kantorovitch formulation of Wasserstein distance, we introduce and discuss novel MoM-based robust estimators whose consistency is studied under a data contamination model and for which convergence rates are provided. These MoM estimators enable to make Wasserstein Generative Adversarial Network (WGAN) robust to outliers, as witnessed by an empirical study on two benchmarks CIFAR10 and Fashion MNIST. Eventually, we discuss how to combine MoM with the entropy-regularized approximation of the Wasserstein distance and propose a simple MoM-based re-weighting scheme that could be used in conjunction with the Sinkhorn algorithm.
Achieving Better Kinship Recognition Through Better Baseline

Recognizing blood relations using face images can be seen as an application of face recognition systems with additional restrictions. These restrictions proved to be difficult to deal with, however, recent advancements in face verification show that there is still much to gain using more data and novel ideas. As a result face recognition is a great source domain from which we can transfer the knowledge to get better performance in kinship recognition as a source domain. We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model. With the approach described above as the foundation, we constructed a pipeline that achieved state-of-the-art performance on two tracks in the recent Recognizing Families In the Wild Data Challenge.
A Simplified Framework for Zero-shot Cross-Modal Sketch Data Retrieval

We deal with the problem of zero-shot cross-modal image retrieval involving color and sketch images through a novel deep representation learning technique. The problem of a sketch to image retrieval and vice-versa is of practical importance, and a trained model in this respect is expected to generalize beyond the training classes, e.g., the zero-shot learning scenario. Nonetheless, considering the drastic distributions-gap between both the modalities, a feature alignment is necessary to learn a shared feature space where retrieval can efficiently be carried out. Additionally, it should also be guaranteed that the shared space is semantically meaningful to aid in the zero-shot retrieval task. The very few existing techniques for zero-shot sketch-RGB image retrieval extend the deep generative models for learning the embedding space; however, training a typical GAN like model for multi-modal image data may be non-trivial at times. To this end, we propose a multi-stream encoder-decoder model that simultaneously ensures improved mapping between the RGB and sketch image spaces and high discrimination in the shared semantics-driven encoded feature space. Further, it is guaranteed that the class topology of the original semantic space is preserved in the encoded feature space, which subsequently reduces the model bias towards the training classes. Experimental results obtained on the benchmark Sketchy and TU-Berlin datasets establish the efficacy of our model as we outperform the existing state-of-the-art techniques by a considerable margin.
Quaternary Census Transform Based on the Human Visual System for Stereo Matching

The census transform is a non-parametric local transform that is widely used in stereo matching. This transform encodes the structural information of a local patch into a binary code stream representing the relative intensity ordering of the pixels within the patch. Despite its high performance in stereo matching, the census transform often generates identical binary code streams for two different patches because it simply thresholds the pixels within the patch at the center pixel intensity. To overcome this problem, we introduce a quaternary census transform that encodes the local structural information into a quaternary code stream by employing both the relative intensity ordering and the minimum visibility threshold of the human eye known as the just-noticeable difference. Moreover, because the human eye activates different areas of the retina based on brightness, the patch size for the proposed quaternary census transform adaptively varies depending on the luminance of each pixel. Experimental results on well-known Middlebury stereo datasets prove that the proposed transform outperforms the other census transform-based methods in terms of the accuracy of stereo matching.
Learning to Prove from Synthetic Theorems

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.
A Reversible Data Hiding Based on Histogram Shifting of Prediction Errors for Two-Tier Medical Images

Clinics and hospitals have already adopted more technological resources to provide a faster and more precise diagnostic for patients, health care providers, and institutes of medicine.Security issues get more and more important in medical services via communication resources such as Wireless-Fidelity (Wi-Fi), third generation of mobile telecommunications technology (3G), and other mobile devices to connect medical systems from anywhere.Furthermore, cloud-based medical systems allow users to access archived medical images from anywhere.In order to protect medical images, lossless data hiding methods are efficient and easy techniques.In this paper, we present a data hiding of two-tier medical images based on histogram shifting of prediction errors.The median histogram shifting technique and prediction error schemes as the two-tier hiding have high capacity and PSNR in 16-bit medical images.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models

Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction. This is a critical problem for interpretability because it permits contradictory models to represent the same function. To solve this problem, we propose pure interaction effects: variance in the outcome which cannot be represented by any smaller subset of features. This definition has an equivalence with the Functional ANOVA decomposition. To compute this decomposition, we present a fast, exact algorithm that transforms any piecewise-constant function (such as a tree-based model) into a purified, canonical representation. We apply this algorithm to Generalized Additive Models with interactions trained on several datasets and show large disparity, including contradictions, between the effects before and after purification. These results underscore the need to specify data distributions and ensure identifiability before interpreting model parameters.
Incremental hashing with sample selection using dominant sets

In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from an image collection using a query, a hash table consisting of a set of hash functions is needed to transform images into binary hash codes which are used as the basis to find similar images to the query. If the image collection is dynamic, the hash table built at one time step may not work well at the next due to changes in the collection as a result of new images being added. Therefore, the hash table needs to be rebuilt or updated at successive time steps. Incremental hashing (ICH) is the first effective method to deal with the concept drift problem in image retrieval from dynamic collections. In ICH, a new hash table is learned based on newly emerging images only which represent data distribution of the current data environment. The new hash table is used to generate hash codes for all images including old and new ones. Due to the dynamic nature, new images of one class may not be similar to old images of the same class. In order to learn new hash table that preserves within-class similarity in both old and new images, incremental hashing with sample selection using dominant sets (ICHDS) is proposed in this paper, which selects representative samples from each class for training the new hash table. Experimental results show that ICHDS yields better retrieval performance than existing dynamic and static hashing methods.
The Implicit Regularization of Ordinary Least Squares Ensembles

Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the nature of the subsampling effect, particularly of the features, is not well understood. We study the case of an ensemble of linear predictors, where each individual predictor is fit using ordinary least squares on a random submatrix of the data matrix. We show that, under standard Gaussianity assumptions, when the number of features selected for each predictor is optimally tuned, the asymptotic risk of a large ensemble is equal to the asymptotic ridge regression risk, which is known to be optimal among linear predictors in this setting. In addition to eliciting this implicit regularization that results from subsampling, we also connect this ensemble to the dropout technique used in training deep (neural) networks, another strategy that has been shown to have a ridge-like regularizing effect.
Structural proof theory for first-order weak Kleene logics

This paper presents a sound and complete five-sided sequent calculus for first-order weak Kleene valuations which permits not only elegant representations of four logics definable on first-order weak Kleene valuations, but also admissibility of five cut rules by proof analysis.
Using structured pathology data to predict hospital-wide mortality at admission

Early prediction of in-hospital mortality can improve patient outcome. Current prediction models for in-hospital mortality focus mainly on specific pathologies. Structured pathology data is hospital-wide readily available and is primarily used for e.g. financing purposes. We aim to build a predictive model at admission using the International Classification of Diseases (ICD) codes as predictors and investigate the effect of the self-evident DNR ("Do Not Resuscitate") diagnosis codes and palliative care codes. We compare the models using ICD-10-CM codes with Risk of Mortality (RoM) and Charlson Comorbidity Index (CCI) as predictors using the Random Forests modeling approach. We use the Present on Admission flag to distinguish which diagnoses are present on admission. The study is performed in a single center (Ghent University Hospital) with the inclusion of 36 368 patients, all discharged in 2017. Our model at admission using ICD-10-CM codes (AUCROC = 0.9477) outperforms the model using RoM (AUCROC = 0.8797 and CCI (AUCROC = 0.7435). We confirmed that DNR and palliative care codes have a strong impact on the model resulting in a decrease of 7% for the ICD model (AUCROC = 0.8791) at admission. We therefore conclude that a model with a sufficient predictive performance can be derived from structured pathology data, and if real-time available, can serve as a prerequisite to develop a practical clinical decision support system for physicians.
Security Enhancement Against Insiders in Cyber-Manufacturing Systems

Insiders—who are within a security parameter of a system—can intentionally or unintentionally harm the system due to their granted access authorization and intimate knowledge of the system. Especially, Cyber-Manufacturing systems employing Programmable Logic Controller (PLC) to control manufacturing processes via various computer networks can be easily compromised and critically damaged by insiders. This paper presents a system architecture of an applied blockchain in Cyber-Manufacturing System, where the input data for PLC is stored and retrieved through the blockchain. The paper demonstrates how the blockchain technology can help preventing the data from unintended manipulation or data injection by insiders.
Prospects of Digital Footprints Use in the Higher Education

The objective increase in the role of digital technology comes into a real contradiction with the real practice of its usage in the Russian educational system. In the pedagogical science and practice, educational opportunities of digital tools and resources, their didactic and educational potential are still underestimated. This article investigates possible directions of digital footprints use in the system of higher education in the Russian Federation. The main research methods are analysis, systematization and modeling. Based on them, the author identifies three promising application areas for digital footprint technology in the educational context, which are interdependent: ensuring continuity and integration of educational levels; organization and management of the educational process; management of the educational system as a whole (educational management).
Intercloud Resource Discovery: A Future Perspective using Blockchain Technology

Intercloud is a single logical entity orchestrating resources from different individual clouds providing on-demand resource provisioning in a seamless manner. However, achieving efficient resource discovery in the intercloud environment remains a challenging task owing to the heterogeneity of resources and diversity of cloud platforms. The paper briefs about intercloud resource discovery, outlines the current work done using existing approaches and examines the challenges involved. Finally, the paper explains the concept of blockchain and presents an innovative conceptual model for efficient resource discovery in intercloud.