Miracle-dz/UniFAR-FTUs huggingface dataset:
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera

In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoRec is based on a multi-view stereo setting which encodes the information of multiple consecutive images in a cost volume. To deal with dynamic objects in the scene, we introduce a MaskModule that predicts moving object masks by leveraging the photometric inconsistencies encoded in the cost volumes. Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks. Furthermore, we present a novel multi-stage training scheme with a semi-supervised loss formulation that does not require LiDAR depth values. We carefully evaluate MonoRec on the KITTI dataset and show that it achieves state-of-theart performance compared to both multi-view and singleview methods. With the model trained on KITTI, we furthermore demonstrate that MonoRec is able to generalize well to both the Oxford RobotCar dataset and the more challenging TUM-Mono dataset recorded by a handheld camera. Code and related materials are available at https://vision.in. tum.de/research/monorec.
Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.
Automatic Artifact Removal of Resting-State fMRI with Deep Neural Networks

Functional Magnetic Resonance Imaging (fMRI) is a noninvasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a sequence of 3-D brain images is obtained for further analysis. In the course of fMRI, some sources of activation are caused by noise and artifacts. The removal of these sources is essential before the analysis of the brain activations. Deep Neural Network (DNN) architectures can be used for denoising and artifact removal. The main advantage of DNN models is the automatic learning of abstract and meaningful features, given the raw data. This work presents advanced DNN architectures for noise and artifact classification, using both spatial and temporal information in resting-state fMRI sessions. The highest performance is achieved by a voting schema using information from all the domains, with an average accuracy of over 98% and a very good balance between the metrics of sensitivity and specificity (98.5% and 97.5% respectively).
Blind deblurring for microscopic pathology images using deep learning networks

Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image and output a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and pathological stain. In this approach, a deep learning classifier is first trained to identify the image blur type. Then, two encoder-decoder networks are trained and used alone or in combination to deblur the input image. It is an end-to-end approach and introduces no corrugated artifacts as traditional blind deconvolution methods do. We test our approach on different types of pathology specimens and demonstrate great performance on image blur correction and the subsequent improvement on the diagnosis outcome of AI algorithms.
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs

Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution, leading to potentially different results if the model is uncertain. Alongside uncertainty quantification, our proposed method offers several advantages in different settings. The proposed method can (1) learn deterministic and probabilistic automata from data, (2) learn well-calibrated models on real-world classification tasks, (3) improve the performance of out-of-distribution detection, and (4) control the exploration-exploitation trade-off in reinforcement learning.
Paraxial fluid of light in hot atomic vapors.

Quantum fluids of light are the photonic counterpart of Bose gases. They currently attract increasing interest since they are versatile and highly tunable systems for probing many-body physics quantum phenomena, such as superfluidity. Superfluid flow of light has already been reported in microcavity exciton-polariton condensates but clear observation of this phenomenon in cavityless systems, that is, for propagating photon fluids, remains elusive. In this thesis, we study the hydrodynamical properties of light propagating close to resonance in hot rubidium vapors. Whereas photons in air are not interacting with each other, the situation is different in rubidium vapors, as an effective interaction between them, mediated by the atomic ensemble, appears. The light behaves therefore as a fluid flowing in the plane perpendicular to the optical axis. The primary purpose of this thesis was to show that light in those systems can behave as a superfluid. The first step toward the observation of superfluidity is to measure the dispersion relation of small amplitude density waves travelling onto the propagating photon fluid. I show that this dispersion exhibits a linear trend for small excitation wave-vectors, which is, according to the Landau criterion, a sufficient condition for guaranteeing superfluidity. I present then an all-optical defect experiment - where the photon fluid flows against an optically induced obstacle (namely, a local change of refractive index) - designed to measure the drag force cancellation at the fluid/superfluid threshold.
Atomistic Simulations of Dislocation — Crack Interaction

The interaction of dislocations with a static mode I crack is studied by large scale molecular dynamics simulations. The model consists of a blunted [001](110) crack in nickel, to which after relaxation at K < K Ic the displacement field of a dislocation is added. The response of the system is monitored during its evolution in the micro-canonical ensemble. The three dimensional nature of the problem requires the simulation of many millions of atoms. The great demands on the computational resources and data storage can only be met by high performance computing platforms and by the development of appropriate simulation methods. The simulations allowed to identify different characteristic processes during the interaction of the impinging dislocation with the crack. In particular, stimulated dislocation emission and cross slip processes are observed to be important for the development of a plastic zone.
Shuffling Gradient-Based Methods with Momentum

We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with momentum to approximate a stationary point of non-convex finite-sum minimization problems. While our method is inspired by momentum techniques, its update is significantly different from existing momentum-based methods. We establish that our algorithm achieves a state-of-the-art convergence rate for both constant and diminishing learning rates under standard assumptions (i.e., $L$-smoothness and bounded variance). When the shuffling strategy is fixed, we develop another new algorithm that is similar to existing momentum methods. This algorithm covers the single-shuffling and incremental gradient schemes as special cases. We prove the same convergence rate of this algorithm under the $L$-smoothness and bounded gradient assumptions. We demonstrate our algorithms via numerical simulations on standard datasets and compare them with existing shuffling methods. Our tests have shown encouraging performance of the new algorithms.
Towards Assistive Diagnoses in m-Health: A Gray-box Neural Model for Cerebral Autoregulation Index.

The cerebral autoregulation system (CAS), is a mechanism which aims to regulate pressure variations occurring in the cerebral circulatory system. At present, there only exist invasive methods and, in turn, they are not used to prevent cerebrovascular accidents. Nowadays, the emergent concept of m-Health allows to use mobile devices to assist the cerebral autoregulation index (ARI). For this, it is necessary to find novel models which allow to approximate the ARI by using the blood pressure value. This work proposes a gray-box neural model to find a relation between the arterial blood pressure (ABP) and the cerebral blood flow velocity (CBFV) in order to obtain the ARI. Preliminary results show good performance by using a phenomenological model in comparison to the Aaslid-Tiecks model.
RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique

Among many prevailing malware, crypto-ransomware poses a significant threat as it financially extorts affected users by creating denial of access via unauthorized encryption of their documents as well as holding their documents hostage and financially extorting them. This results in millions of dollars of annual losses worldwide. Multiple variants of ransomware are growing in number with capabilities of evasion from many anti-viruses and software-only malware detection schemes that rely on static execution signatures. In this paper, we propose a hardware-assisted scheme, called RanStop, for early detection of crypto-ransomware infection in commodity processors. RanStop leverages the information of hardware performance counters embedded in the performance monitoring unit in modern processors to observe micro-architectural event sets and detects known and unknown crypto-ransomware variants. In this paper, we train a recurrent neural network-based machine learning architecture using long short-term memory (LSTM) model for analyzing micro-architectural events in the hardware domain when executing multiple variants of ransomware as well as benign programs. We create timeseries to develop intrinsic statistical features using the information of related HPCs and improve the detection accuracy of RanStop and reduce noise by via LSTM and global average pooling. As an early detection scheme, RanStop can accurately and quickly identify ransomware within 2ms from the start of the program execution by analyzing HPC information collected for 20 timestamps each 100us apart. This detection time is too early for a ransomware to make any significant damage, if none. Moreover, validation against benign programs with behavioral (sub-routine-centric) similarity with that of a crypto-ransomware shows that RanStop can detect ransomware with an average of 97% accuracy for fifty random trials.
Leveraging Architectural Support of Three Page Sizes with Trident

Large pages are commonly deployed to reduce address translation overheads for big-memory workloads. Modern x86-64 processors from Intel and AMD support two large page sizes -- 1GB and 2MB. However, previous works on large pages have primarily focused on 2MB pages, partly due to lack of substantial evidence on the profitability of 1GB pages to real-world applications. We argue that in fact, inadequate system software support is responsible for a decade of underutilized hardware support for 1GB pages. Through extensive experimentation on a real system, we demonstrate that 1GB pages can improve performance over 2MB pages, and when used in tandem with 2MB pages for an important set of applications; the support for the latter is crucial but missing in current systems. Our design and implementation of \trident{} in Linux fully exploit hardware supported large pages by dynamically and transparently allocating 1GB, 2MB, and 4KB pages as deemed suitable. \trident{} speeds up eight memory-intensive applications by {$18\%$}, on average, over Linux's use of 2MB pages. We also propose \tridentpv{}, an extension to \trident{} that effectively virtualizes 1GB pages via copy-less promotion and compaction in the guest OS. Overall, this paper shows that even GB-sized pages have considerable practical significance with adequate software enablement, in turn motivating architects to continue investing/innovating in large pages.
Achieving Sample-Efficient and Online-Training-Safe Deep Reinforcement Learning with Base Controllers

Application of Deep Reinforcement Learning (DRL) algorithms in real-world robotic tasks faces many challenges. On the one hand, reward-shaping for complex tasks is difficult and may result in sub-optimal performances. On the other hand, a sparse-reward setting renders exploration inefficient, and exploration using physical robots is of high-cost and unsafe. In this paper we propose a method of learning challenging sparse-reward tasks utilizing existing controllers. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the controllers into stages of exploration, Q-value estimation as well as policy update. Through experiments ranging from stacking blocks to cups, we present a straightforward way of synthesizing these controllers, and show that the learned state-based or image-based policies steadily outperform them. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and can learn online in a safe manner. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation

Relation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models on either distantly supervised data or human-annotated data. To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision framework which effectively utilizes both types of data. However, simply combining the two types of data to train a RE model may decrease the prediction accuracy since distant supervision has labeling bias. We employ two separate prediction networks HA-Net and DS-Net to predict the labels by human annotation and distant supervision, respectively, to prevent the degradation of accuracy by the incorrect labeling of distant supervision. Furthermore, we propose an additional loss term called disagreement penalty to enable HA-Net to learn from distantly supervised labels. In addition, we exploit additional networks to adaptively assess the labeling bias by considering contextual information. Our performance study on sentence-level and document-level REs confirms the effectiveness of the dual supervision framework.
Generative Adversarial Stacked Autoencoders

Generative Adversarial Networks (GANs) have become predominant in image generation tasks. Their success is attributed to the training regime which employs two models: a generator G and discriminator D that compete in a minimax zero sum game. Nonetheless, GANs are difficult to train due to their sensitivity to hyperparameter and parameter initialisation, which often leads to vanishing gradients, non-convergence, or mode collapse, where the generator is unable to create samples with different variations. In this work, we propose a novel Generative Adversarial Stacked Convolutional Autoencoder(GASCA) model and a generative adversarial gradual greedy layer-wise learning algorithm de-signed to train Adversarial Autoencoders in an efficient and incremental manner. Our training approach produces images with significantly lower reconstruction error than vanilla joint training.
Quantum speedups for convex dynamic programming

We present a quantum algorithm to solve dynamic programming problems with convex value functions. For linear discrete-time systems with a $d$-dimensional state space of size $N$, the proposed algorithm outputs a quantum-mechanical representation of the value function in time $O(T \gamma^{dT}\mathrm{polylog}(N,(T/\varepsilon)^{d}))$, where $\varepsilon$ is the accuracy of the solution, $T$ is the time horizon, and $\gamma$ is a problem-specific parameter depending on the condition numbers of the cost functions. This allows us to evaluate the value function at any fixed state in time $O(T \gamma^{dT}\sqrt{N}\,\mathrm{polylog}(N,(T/\varepsilon)^{d}))$, and the corresponding optimal action can be recovered by solving a convex program. The class of optimization problems to which our algorithm can be applied includes provably hard stochastic dynamic programs. Finally, we show that the algorithm obtains a quadratic speedup (up to polylogarithmic factors) compared to the classical Bellman approach on some dynamic programs with continuous state space that have $\gamma=1$.
A More Biologically Plausible Local Learning Rule for ANNs

The backpropagation algorithm is often debated for its biological plausibility. However, various learning methods for neural architecture have been proposed in search of more biologically plausible learning. Most of them have tried to solve the "weight transport problem" and try to propagate errors backward in the architecture via some alternative methods. In this work, we investigated a slightly different approach that uses only the local information which captures spike timing information with no propagation of errors. The proposed learning rule is derived from the concepts of spike timing dependant plasticity and neuronal association. A preliminary evaluation done on the binary classification of MNIST and IRIS datasets with two hidden layers shows comparable performance with backpropagation. The model learned using this method also shows a possibility of better adversarial robustness against the FGSM attack compared to the model learned through backpropagation of cross-entropy loss. The local nature of learning gives a possibility of large scale distributed and parallel learning in the network. And finally, the proposed method is a more biologically sound method that can probably help in understanding how biological neurons learn different abstractions.
Astrophysical bounds on mirror dark matter derived from binary pulsars timing data

Mirror Dark Matter (MDM) has been considered as an elegant framework for a particle theory of Dark Matter (DM). It is supposed that there exists a dark sector which is mirror of the ordinary matter. Some MDM models allow particle interactions mirror and ordinary matter, in addition to the gravitational interaction. The possibility of neutron to mirror neutron transition has recently been discussed both from theoretical and experimental perspectives. This paper is based on a previous work in which we obtained stringent upper limits on the possibility of converting neutrons to mirror neutrons in the interiors of neutron stars, by using timing data of binary pulsars. Such a transition would imply mass loss in neutron stars leading to a significant change of orbital period of neutron star binary systems. The observational bounds on the period changes of such binaries, therefore put strong limits on the above transition rate and hence on the neutron -- mirror-neutron mixing parameter $\epsilon'$. Our limits are much stronger than the values required to explain the neutron decay anomaly via $n-n'$ mixing.
Scaling Wide Residual Networks for Panoptic Segmentation

The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the Residual Networks (ResNets) by stacking a small number of residual blocks with large channel sizes, have demonstrated outstanding performance on multiple dense prediction tasks. However, since proposed, the Wide-ResNet architecture has barely evolved over the years. In this work, we revisit its architecture design for the recent challenging panoptic segmentation task, which aims to unify semantic segmentation and instance segmentation. A baseline model is obtained by incorporating the simple and effective Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets. Its network capacity is further scaled up or down by adjusting the width (i.e., channel size) and depth (i.e., number of layers), resulting in a family of SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such a simple scaling scheme, coupled with grid search, identifies several SWideRNets that significantly advance state-of-the-art performance on panoptic segmentation datasets in both the fast model regime and strong model regime.