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Found 244 papers, 67 papers with code
ODIST: Open World Classification via Distributionally Shifted Instances
In this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances.
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
The nature of no word delimiter or inflection that can indicate segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks.
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond
More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space.
Optimization and scheduling for a large scale urban transportation system in a fast-changing world
This paper proposes a set of technological solutions to transform existing transport systems into more intelligent, interactive systems by utilizing optimization and control methods that can be implemented in the near future.
Backward Compatibility During Data Updates by Weight Interpolation
This method interpolates between the weights of the old and new model and we show in extensive experiments that it reduces negative flips without sacrificing the improved accuracy of the new model.
Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Sensor and Actuator Attacks
To this end, we consider the resilient containment control problem of general linear heterogeneous MAS in the face of correlated and unbounded sensor attacks, as well as general unbounded actuator attacks.
Dialog2API: Task-Oriented Dialogue with API Description and Example Programs
The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs.
Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data.
Learning threshold neurons via the "edge of stability"
For these models, we provably establish the edge of stability phenomenon and discover a sharp phase transition for the step size below which the neural network fails to learn "threshold-like" neurons (i. e., neurons with a non-zero first-layer bias).
Robust Split Federated Learning for U-shaped Medical Image Networks
To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL.
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints.
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial intelligence (AIoT) devices through centralized global model aggregation.
DFEE: Interactive DataFlow Execution and Evaluation Kit
DataFlow has been emerging as a new paradigm for building task-oriented chatbots due to its expressive semantic representations of the dialogue tasks.
Prototype matching: children's preference for forming scientific concepts
Children have an obvious preference for "prototype matching" in scientific concept learning, which is not only obviously deviated from the current general understanding of science education that emphasizes discovery/inquiry construction, but also points out that there may be a priority relationship among various ways of concept organization (such as definition theory, prototype theory, schema theory, etc.).
Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates
One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances.
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation
Existing methods suffer from the trade-off between the quality and diversity of generated images.
Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment
We further propose Anchor Augment to guide the generative model's attention to the fine-grained information in the representation of CLIP.
A quantum neural network with efficient optimization and interpretability
As the quantum counterparts to the classical artificial neural networks underlying widespread machine-learning applications, unitary-based quantum neural networks are active in various fields of quantum computation.
Fair Visual Recognition via Intervention with Proxy Features
To this end, we propose \emph{Proxy Debiasing}, to first transfer the target task's learning of bias information from bias features to artificial proxy features, and then employ causal intervention to eliminate proxy features in inference.
Boosting Point Clouds Rendering via Radiance Mapping
In this work, we focus on boosting the image quality of point clouds rendering with a compact model design.
FairCLIP: Social Bias Elimination based on Attribute Prototype Learning and Representation Neutralization
Although FairCLIP is used to eliminate bias in image retrieval, it achieves the neutralization of the representation which is common to all CLIP downstream tasks.
NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging
Single neural networks have achieved simultaneous phase retrieval with aberration compensation and phase unwrapping in off-axis Quantitative Phase Imaging (QPI).
What Makes Convolutional Models Great on Long Sequence Modeling?
We focus on the structure of the convolution kernel and identify two critical but intuitive principles enjoyed by S4 that are sufficient to make up an effective global convolutional model: 1) The parameterization of the convolutional kernel needs to be efficient in the sense that the number of parameters should scale sub-linearly with sequence length.
Ranked #4 on
Long-range modeling
on LRA
Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery
Accurate vehicle type classification serves a significant role in the intelligent transportation system.
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention
Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge.
Research on Multi-Objective Planning of Electric Vehicle Charging Stations Considering the Condition of Urban Traffic Network
How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning.
Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles
Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device.
A non-invasive fault location method for modular multilevel converters under light load conditions
The proposed approach of injecting the second-order circulating current will rebuild the bipolar arm current of the MMC and enlarge the capacitor voltage deviations between the healthy and faulty SMs.
Learning Degradation Representations for Image Deblurring
With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively.
Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation
Inspired by the remarkable ability of humans to infer occluded joints from visible cues, we develop a method to explicitly model this process that significantly improves bottom-up multi-person human pose estimation with or without occlusions.
Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment
Product-specific guidances (PSGs) recommended by the United States Food and Drug Administration (FDA) are instrumental to promote and guide generic drug product development.
Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data
Owing to this paucity of samples, learning on the tail classes is especially challenging for the fine-tuning when transferring a pretrained model to a downstream task.
Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios
To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.
Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation
In this work, we propose PolarBEV for vision-based uneven BEV representation learning.
Counterfactually Measuring and Eliminating Social Bias in Vision-Language Pre-training Models
Vision-Language Pre-training (VLP) models have achieved state-of-the-art performance in numerous cross-modal tasks.
Collaboration-Aware Graph Convolutional Network for Recommender Systems
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models
With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-based post-hoc XAI models and explanations are socially misaligned.
No Attention is Needed: Grouped Spatial-temporal Shift for Simple and Efficient Video Restorers
The key of our framework is the grouped spatial-temporal shift, which is simple and lightweight, but can implicitly establish inter-frame correspondences and achieve multi-frame aggregation.
Vicinity Vision Transformer
Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.
Hands-on Wireless Sensing with Wi-Fi: A Tutorial
With the rapid development of wireless communication technology, wireless access points (AP) and internet of things (IoT) devices have been widely deployed in our surroundings.
Unveiling Transformers with LEGO: a synthetic reasoning task
We propose a synthetic task, LEGO (Learning Equality and Group Operations), that encapsulates the problem of following a chain of reasoning, and we study how the transformer architectures learn this task.
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively.
Aerial Vision-and-Dialog Navigation
To this end, we introduce Aerial Vision-and-Dialog Navigation (AVDN), to navigate a drone via natural language conversation.
A microstructure estimation Transformer inspired by sparse representation for diffusion MRI
Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage.
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion.
Clinical Prompt Learning with Frozen Language Models
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups.
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network
As for each sub-network, we propose an efficient multi-frequency denoising network to remove noise of different frequencies.
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding.
Indoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue.
Label Semantic Aware Pre-training for Few-shot Text Classification
Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.
Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging.
Depth Completion using Geometry-Aware Embedding
The ablation study gives more insights into our method that could achieve significant gains with a simple design, while having better generalization capability and stability.
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias
Backdoor attack is a new AI security risk that has emerged in recent years.
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
First, we measure and analyze model update regression in different model update settings.
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks
As an example, we show that within the MAB framework, the optimal beam sweeping period, beamwidth, and beam directions could be dynamically learned with sample-computational-efficient bandit algorithms.
Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition
Previous methods mainly focus on projecting multiple modalities into a common latent space and learning an identical representation for all labels, which neglects the diversity of each modality and fails to capture richer semantic information for each label from different perspectives.
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery
Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet).
Design Challenges for a Multi-Perspective Search Engine
Naturally, identifying such responses within a document is a natural language understanding task.
IDR: Self-Supervised Image Denoising via Iterative Data Refinement
To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
On Predicting Generalization using GANs
Research on generalization bounds for deep networks seeks to give ways to predict test error using just the training dataset and the network parameters.
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes
In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions.
Nonlinear Multi-Objective Flux Balance Analysis of the Warburg Effect
To extend the applicability of the previous model and model the disparate cellular pathway preferences in different cell types, we built a Nonlinear Multi-Objective FBA (NLMOFBA) model by including three key objective terms (ATP production rate, lactate generation rate and ATP yield) into one objective function through linear scalarization.
Unsupervised PET Reconstruction from a Bayesian Perspective
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality.
Nuisance-Label Supervision: Robustness Improvement by Free Labels
Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them.
Rethinking Noise Synthesis and Modeling in Raw Denoising
However, the real raw image noise is contributed by many noise sources and varies greatly among different sensors.
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design
However, none of them provides an in-depth summary of many applications of the recent AI models in drug design.
Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings
Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Semantic Parsing in Task-Oriented Dialog with Recursive Insertion-based Encoder
At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination.
Dual camera snapshot hyperspectral imaging system via physics informed learning
Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains.
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition
In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model.
Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power
In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics.
What is Your Article Based On? Inferring Fine-grained Provenance
We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www. politifact. com}; our experimental results show that our solution leads to a significant improvement over baselines.
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving.
ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic Videos
With this goal in mind, we propose PV-SOD, a new task that aims to segment salient objects from panoramic videos.
Semi-Supervised Learning with Multi-Head Co-Training
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning.
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning
DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner.
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Salient Object Ranking with Position-Preserved Attention
In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.
Visual Question Rewriting for Increasing Response Rate
Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful.
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.
A Comparative Study on Schema-Guided Dialogue State Tracking
In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking.
Learning to Decompose and Organize Complex Tasks
Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.
Multi-turn Dialog System on Single-turn Data in Medical Domain
This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.
SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos
Salient human detection (SHD) in dynamic 360{\deg} immersive videos is of great importance for various applications such as robotics, inter-human and human-object interaction in augmented reality.
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates
Behavior of deep neural networks can be inconsistent between different versions.
CMA-Net: A Cascaded Mutual Attention Network for Light Field Salient Object Detection
In the past few years, numerous deep learning methods have been proposed to address the task of segmenting salient objects from RGB images.
Deformable TDNN with adaptive receptive fields for speech recognition
Time Delay Neural Networks (TDNNs) are widely used in both DNN-HMM based hybrid speech recognition systems and recent end-to-end systems.
Optimal Cooperative Driving at Signal-Free Intersections with Polynomial-Time Complexity
Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years.
Learning Synergistic Attention for Light Field Salient Object Detection
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction.
Distributed Cooperative Driving in Multi-Intersection Road Networks
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years.
Modeling Fuzzy Cluster Transitions for Topic Tracing
Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks.
Tracing Topic Transitions with Temporal Graph Clusters
Twitter serves as a data source for many Natural Language Processing (NLP) tasks.
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information
In this paper, we introduce the homotopy equivalence relation (HER) to make the neural networks learn the data distribution from a high-dimension manifold.
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Clustering is a fundamental task in the computer vision and machine learning community.
Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data
Traditional statistics forbids use of test data (a. k. a.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems
Finally, by integrating the SRL with ASR, an end-to-end multilingual ASR framework is formulated in a supervised manner, which is able to translate the raw wave into text in one model, i. e., wave-to-text.
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
Physiological computing uses human physiological data as system inputs in real time.
RGB-D Salient Object Detection via 3D Convolutional Neural Networks
The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis).
Natural Language Inference
Natural Language Understanding
+1
Localized Simple Multiple Kernel K-Means
After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Inflection Point: A New Perspective on Photonic Nanojets
When light propagates through the edge or middle part of microparticle's incoming interface, there is a basic rule that light converges and diverges rapidly or slowly at the output port.
Optics
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.