Miracle-dz/UniFAR-FTUs huggingface dataset:
Degenerative Adversarial NeuroImage Nets for 3D Simulations: Application in Longitudinal MRI

The recent success of deep learning together with the availability of large medical imaging datasets have enabled researchers to improve our understanding of complex chronic medical conditions such as neurodegenerative diseases. The possibility of predicting realistic and accurate images would be a breakthrough for many clinical healthcare applications. However, current image simulators designed to model neurodegenerative disease progression present limitations that preclude their utility in clinical practice. These limitations include personalization of disease progression and the ability to synthesize spatiotemporal images in high resolution. In particular, memory limitations prohibit full 3D image models, necessitating various techniques to discard spatiotemporal information, such as patch-based approaches. In this work, we introduce a novel technique to address this challenge, called Profile Weight Functions (PWF). We demonstrate its effectiveness integrated within our new deep learning framework, showing that it enables the extension to 3D of a recent state-of-the-art 2D approach. To our knowledge, we are the first to implement a personalized disease progression simulator able to predict accurate, personalised, high-resolution, 3D MRI. In particular, we trained a model of ageing and Alzheimer's disease progression using 9652 T1-weighted (longitudinal) MRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and validated on a separate test set of 1283 MRI (also from ADNI, random partition). We validated our model by analyzing its capability to synthesize MRI that produce accurate volumes of specific brain regions associated with neurodegeneration. Our experiments demonstrate the effectiveness of our solution to provide a 3D simulation that produces accurate and convincing synthetic MRI that emulate ageing and disease progression.
A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification

Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set.
TutorialVQA: Question Answering Dataset for Tutorial Videos

Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.
Automatic Video Object Segmentation via Motion-Appearance-Stream Fusion and Instance-aware Segmentation

This paper presents a method for automatic video object segmentation based on the fusion of motion stream, appearance stream, and instance-aware segmentation. The proposed scheme consists of a two-stream fusion network and an instance segmentation network. The two-stream fusion network again consists of motion and appearance stream networks, which extract long-term temporal and spatial information, respectively. Unlike the existing two-stream fusion methods, the proposed fusion network blends the two streams at the original resolution for obtaining accurate segmentation boundary. We develop a recurrent bidirectional multiscale structure with skip connection for the stream fusion network to extract long-term temporal information. Also, the multiscale structure enables to obtain the original resolution features at the end of the network. As a result of two-stream fusion, we have a pixel-level probabilistic segmentation map, which has higher values at the pixels belonging to the foreground object. By combining the probability of foreground map and objectness score of instance segmentation mask, we finally obtain foreground segmentation results for video sequences without any user intervention, i.e., we achieve successful automatic video segmentation. The proposed structure shows a state-of-the-art performance for automatic video object segmentation task, and also achieves near semi-supervised performance.
Assessing the Robustness of Visual Question Answering

Deep neural networks have been playing an essential role in the task of Visual Question Answering (VQA). Until recently, their accuracy has been the main focus of research. Now there is a trend toward assessing the robustness of these models against adversarial attacks by evaluating the accuracy of these models under increasing levels of noisiness in the inputs of VQA models. In VQA, the attack can target the image and/or the proposed query question, dubbed main question, and yet there is a lack of proper analysis of this aspect of VQA. In this work, we propose a new method that uses semantically related questions, dubbed basic questions, acting as noise to evaluate the robustness of VQA models. We hypothesize that as the similarity of a basic question to the main question decreases, the level of noise increases. To generate a reasonable noise level for a given main question, we rank a pool of basic questions based on their similarity with this main question. We cast this ranking problem as a LASSO optimization problem. We also propose a novel robustness measure Rscore and two large-scale basic question datasets in order to standardize robustness analysis of VQA models. The experimental results demonstrate that the proposed evaluation method is able to effectively analyze the robustness of VQA models. To foster the VQA research, we will publish our proposed datasets.
Asymptotic Normality and Variance Estimation For Supervised Ensembles

Ensemble methods based on bootstrapping have improved the predictive accuracy of base learners, but fail to provide a framework in which formal statistical inference can be conducted. Recent theoretical developments suggest taking subsamples without replacement and analyze the resulting estimator in the context of a U-statistic, thus demonstrating asymptotic normality properties. However, we observe that current methods for variance estimation exhibit severe bias when the number of base learners is not large enough, compromising the validity of the resulting confidence intervals or hypothesis tests. This paper shows that similar asymptotics can be achieved by means of V-statistics, corresponding to taking subsamples with replacement. Further, we develop a bias correction algorithm for estimating variance in the limiting distribution, which yields satisfactory results with moderate size of base learners.
GGNN: Graph-Based GPU Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations. Since PQT (Wieschollek et al., 2016), FAISS (Johnson et al., 2021), and SONG (Zhao et al., 2020) started to leverage the massive parallelism offered by GPUs, GPU-based implementations are a crucial resource for today’s state-of-the-art ANN methods. While most of these methods allow for faster queries, less emphasis is devoted to accelerating the construction of the underlying index structures. In this paper, we propose a novel GPU-friendly search structure based on nearest neighbor graphs and information propagation on graphs. Our method is designed to take advantage of GPU architectures to accelerate the hierarchical construction of the index structure and for performing the query. Empirical evaluation shows that GGNN significantly surpasses the state-of-the-art CPU- and GPU-based systems in terms of build-time, accuracy and search speed.
Bohmian-Based Approach to Gauss-Maxwell Beams

Usual Gaussian beams are particular scalar solutions to the paraxial Helmholtz equation, which neglect the vector nature of light. In order to overcome this inconvenience, Simon et al. (J. Opt. Soc. Am. A 1986, 3, 536–540) found a paraxial solution to Maxwell’s equation in vacuum, which includes polarization in a natural way, though still preserving the spatial Gaussianity of the beams. In this regard, it seems that these solutions, known as Gauss-Maxwell beams, are particularly appropriate and a natural tool in optical problems dealing with Gaussian beams acted or manipulated by polarizers. In this work, inspired in the Bohmian picture of quantum mechanics, a hydrodynamic-type extension of such a formulation is provided and discussed, complementing the notion of electromagnetic field with that of (electromagnetic) flow or streamline. In this regard, the method proposed has the advantage that the rays obtained from it render a bona fide description of the spatial distribution of electromagnetic energy, since they are in compliance with the local space changes undergone by the time-averaged Poynting vector. This feature confers the approach a potential interest in the analysis and description of single-photon experiments, because of the direct connection between these rays and the average flow exhibited by swarms of identical photons (regardless of the particular motion, if any, that these entities might have), at least in the case of Gaussian input beams. In order to illustrate the approach, here it is applied to two common scenarios, namely the diffraction undergone by a single Gauss-Maxwell beam and the interference produced by a coherent superposition of two of such beams.
Higher-order total variation approaches and generalisations

Over the last decades, the total variation (TV) has evolved to be one of the most broadly-used regularisation functionals for inverse problems, in particular for imaging applications. When first introduced as a regulariser, higher-order generalisations of TV were soon proposed and studied with increasing interest, which led to a variety of different approaches being available today. We review several of these approaches, discussing aspects ranging from functional-analytic foundations to regularisation theory for linear inverse problems in Banach space, and provide a unified framework concerning well-posedness and convergence for vanishing noise level for respective Tikhonov regularisation. This includes general higher orders of TV, additive and infimal-convolution multi-order total variation, total generalised variation, and beyond. Further, numerical optimisation algorithms are developed and discussed that are suitable for solving the Tikhonov minimisation problem for all presented models. Focus is laid in particular on covering the whole pipeline starting at the discretisation of the problem and ending at concrete, implementable iterative procedures. A major part of this review is finally concerned with presenting examples and applications where higher-order TV approaches turned out to be beneficial. These applications range from classical inverse problems in imaging such as denoising, deconvolution, compressed sensing, optical-flow estimation and decompression, to image reconstruction in medical imaging and beyond, including magnetic resonance imaging, computed tomography, magnetic-resonance positron emission tomography, and electron tomography.
Neural network wiretaps

The signalling between internal layers of fully connected neural networks is scrambled because backpropagation training does not require perceptrons to be arranged in any particular order. The input and the output inherit the order from the training database, but all internal signalling has the appearance of noise. The resulting lack of interpretability and trust is a much criticised feature of deep neural networks. We present here a group-theoretical procedure that brings the intermediate signalling into a human-readable form. The assumptions making this possible are (a) that fully connected deep neural nets are interpretable; (b) that the descrambled signals are in a broad sense "physical" - either smooth, or localised, or endowed with other quantifiable features. This appears to be the case for the network we have studied, thus retaining reductionism and critical rationalism as central philosophies in machine learning - both are currently under pressure from black box neural nets.
Comparison and Evaluation of 2D and 3D Range Sensors

For mobile robots range sensors are important to perceive the environment. Sensors that can measure in a 3D volume are especially significant for outdoor robotics, because this environment is often highly unstructured. The quality of the data gathered by those sensors influences all algorithms relying on it. In this paper thus the precision of several 2D and 2.5D sensors is measured at different ranges and different incidence angles. The results of all tests are presented and analyzed.
Longitudinal mediation analysis using natural effect models.

Mediation analysis is concerned with the decomposition of the total effect of an exposure on an outcome into the indirect effect through a given mediator, and the remaining direct effect. This is ideally done using longitudinal measurements of the mediator, as these capture the mediator process more finely. However, longitudinal measurements pose challenges for mediation analysis. This is because the mediators and outcomes measured at a given time-point can act as confounders for the association between mediators and outcomes at a later time-point; these confounders are themselves affected by the prior exposure and outcome. Such post-treatment confounding cannot be dealt with using standard methods (e.g. generalised estimating equations). Analysis is further complicated by the need for so-called cross-world counterfactuals to decompose the total effect. This article addresses these challenges. In particular, we introduce so-called natural effect models, which parameterise the direct and indirect effect of a baseline exposure with respect to a longitudinal mediator and outcome. These can be viewed as a generalisation of marginal structural mean models to enable effect decomposition. We introduce inverse probability weighting techniques for fitting these models, adjusting for (measured) time-varying confounding of the mediator-outcome association. Application of this methodology uses data from the Millennium Cohort Study, UK.
Scope of Magnetic Tunnel Junction Based Molecular Electronics and Spintronics Devices.

Dream of developing molecule-based logic and memory device is more than 70-year-old. Presently, molecule-based devices are also considered for quantum computation hardware. The recent studies have shown the molecule connected to metal leads can perform the qubit-based logic operation. This is an interesting question of why experimental progress is still very slow even when the scope of molecule-based devices may govern the advancement of next-generation logic and memory devices for the highest possible computer technologies. Molecules have the potential to be unmatched device elements as chemists can mass-produce an endless variety of molecules with novel optical, magnetic, and charge transport characteristics. However, the biggest challenge is to connect two metal leads to target molecules and develop a robust and versatile device fabrication technology that can be adopted for commercial-scale mass production. This paper discusses the distinct advantages of utilizing commercially successful magnetic tunnel junctions as a vehicle for developing molecular electronics and molecular spintronics devices. We describe the use of a tunnel junction with the exposed sides as a testbed for molecular devices. On the exposed sides of a tunnel, junction molecules are bridged across an insulator by chemically bonding with the two metal electrodes; sequential growth of metal-insulator-metal layers ensures that separation between two metal electrodes is controlled by the insulator thickness to the molecular device length scale. Here we also strongly emphasize a need for close collaboration between chemists and magnetic tunnel junction researchers. Such partnerships will have a strong potential to develop magnetic tunnel junction based molecular devices for the futuristic areas such as memory devices, magnetic metamaterials, and high sensitivity biosensors.
Epidemiology of Collegiate American Football Injuries -Longitudinal Injury Surveillance for 10 Years, 1999 Through 2008-

American football (AF) is one of most exciting collegiate sports in Japan, and many collegiate players here start playing football in their freshman year. However, many collegiate AF players sustain injuries, including brain and head injuries as well as spinal cord injuries (Dick, et al., 2007; Shankar, et al, 2007; Torg, et al., 1979; Matsumoto, et al., 1990). Many studies have been made on AF-related injuries, but it is diffi cult to compare them due to the different methodologies and injury definitions used (Dick, et al., 2007; Shankar, et al., 2007; Fujiya, et al., 2002; Matsumoto, et al., 1990). The United States, the country of origin of AF, established the National Collegiate Athletic Association (NCAA) in 1906 in order to prevent young athletes, particularly those in football, from suffering numerous injuries and even fatalities. Since then, the NCAA has consistently published reports on injuries sustained in collegiate football. This collection of data amassed over such a long period can help medical associates gain an understanding of the tendencies of football-related injuries. For example, Dick, et al. (2007) reported that the game injury rate (GIR) was 36 injuries per 1000 athlete-exposures (A-Es) from 1988 to 2004, based on a study of 16 years of data on injuries collected by the NCAA over this period, and that the GIR was more than nine times higher than the practice injury rate (PIR). Many changes and modifications to the NCAA rules on football have been made as a result of these longitudinal data (Torg, et al., 1976). However, no such longitudinal studies that include detailed data on injuries sustained in both games and practices Epidemiology of Collegiate American Football Injuries -Longitudinal Injury Surveillance for 10 Years, 1999 Through 2008-
Fast and Scalable Estimator for Sparse and Unit-Rank Higher-Order Regression Models

Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univariate outcome becomes an elementary task in many fields. To solve this task, we propose \underline{Fa}st \underline{S}parse \underline{T}ensor \underline{R}egression model (FasTR) based on so-called unit-rank CANDECOMP/PARAFAC decomposition. FasTR first decomposes the tensor coefficient into component vectors and then estimates each vector with $\ell_1$ regularized regression. Because of the independence of component vectors, FasTR is able to solve in a parallel way and the time complexity is proved to be superior to previous models. We evaluate the performance of FasTR on several simulated datasets and a real-world fMRI dataset. Experiment results show that, compared with four baseline models, in every case, FasTR can compute a better solution within less time.
On irreversible spread of influence in edge-weighted graphs

Various kinds of spread of influence occur in real world social and virtual networks. These phenomena are formulated by activation processes and irreversible dynamic monopolies in combinatorial graphs representing the topology of the networks. In most cases the nature of influence is weighted and the spread of influence depends on the weight of edges. The ordinary formulation and results for dynamic monopolies do not work for such models. In this paper we present a graph theoretical analysis for spread of weighted influence and mention a real world example realizing the activation model with weighted influence. Then we obtain some extremal bounds and algorithmic results for activation process and dynamic monopolies in directed and undirected graphs with weighted edges.
Make Thunderbolts Less Frightening - Predicting Extreme Weather Using Deep Learning

Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. In this work, we tackle one specific sub-problem of weather forecasting, namely the prediction of thunderstorms and lightning. We propose the use of a convolutional neural network architecture inspired by UNet++ and ResNet to predict thunderstorms as a binary classification problem based on satellite images and lightnings recorded in the past. We achieve a probability of detection of more than 94% for lightnings within the next 15 minutes while at the same time minimizing the false alarm ratio compared to previous approaches.
TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking

The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most instances we have to choose which property is most important to us. We used the opportunity of the TREC 2019 Deep Learning track to evaluate the effectiveness of a balanced neural re-ranking approach. We submitted results of the TK (Transformer-Kernel) model: a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of lightweight Transformer layers to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. Our best result for the passage ranking task is: 0.420 MAP, 0.671 nDCG, 0.598 P@10 (TUW19-p3 full). Our best result for the document ranking task is: 0.271 MAP, 0.465 nDCG, 0.730 P@10 (TUW19-d3 re-ranking).
Mathematical Analysis and Algorithms for Federated Byzantine Agreement Systems

We give an introduction to federated Byzantine agreement systems (FBAS) with many examples ranging from small "academic" cases to the current Stellar network. We then analyze the main concepts from a mathematical and an algorithmic point of view. Based on work of Lachowski we derive algorithms for quorum enumeration, checking quorum intersection, and computing the intact nodes with respect to a given set of ill-behaved (Byzantine) nodes. We also show that from the viewpoint of the intactness probability of nodes, which we introduce in this paper, a hierarchical setup of nodes is inferior to an arrangement that we call a symmetric simple FBAS. All algorithms described in this paper are implemented in the Python package Stellar Observatory, which is also used in some of the computed examples.