Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularization Matters in Policy Optimization

Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough. We hope our study provides guidance for future practices in regularizing policy optimization algorithms.
PR ] 2 1 O ct 2 01 9 FUNCTIONAL CENTRAL LIMIT THEOREM FOR RANDOM WALKS IN RANDOM ENVIRONMENT DEFINED ON REGULAR TREES

We study Random Walks in an i.i. d. Random Environment (RWRE) defined on b-regular trees. We prove a functional central limit theorem (FCLT) for transient processes, under a moment condition on the environment. We emphasize that we make no uniform ellipticity assumptions. Our approach relies on regenerative levels, i.e. levels that are visited exactly once. On the way, we prove that the distance between consecutive regenerative levels have a geometrically decaying tail. In the second part of this paper, we apply our results to Linearly Edge-Reinforced Random Walk (LERRW) to prove FCLT when the process is defined on b-regular trees, with b ≥ 4, substantially improving the results of the first author (see Theorem 3 of [4]).
Graph approximations to the Laplacian spectra

I prove that the spectrum of the Laplace–Beltrami operator with the Neumann boundary condition on a compact Riemannian manifold with boundary admits a fast approximation by the spectra of suitable graph Laplacians on proximity graphs on the manifold, and similar graph approximation works for metric-measure spaces glued out of compact Riemannian manifolds of the same dimension.
Shortest Paths, Minimum Weight Perfect Matchings and Travelling Salesperson Tours with Random weights Plus a Random Cost Constraint

Recall that Janson showed that if the edges of the complete graph $K_n$ are assigned exponentially distributed independent random weights, then the expected length of a shortest path between a fixed pair of vertices is asymptotically equal to $(\log n)/n$. We consider analogous problems where edges have not only a random length but also a random cost, and we are interested in the length of the minimum-length structure whose total cost is less than some cost budget. For several classes of structures, we determine the correct minimum length structure as a function of the cost-budget, up to constant factors. Moreover, we achieve this even in the more general setting where the distribution of weights and costs are arbitrary, so long as the density $f(x)$ as $x\to 0$ behaves like $cx^\gamma$ for some $\gamma\geq 0$; previously, this case was not understood even in the absence of cost constraints. We also handle the case where each edge has several independent costs associated to it, and we must simultaneously satisfy budgets on each cost. In this case, we show that the minimum-length structure obtainable is essentially controlled by the product of the cost thresholds.
Automatic Post-Editing for Machine Translation

Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for improvement, leaving APE as a viable option to improve translation quality as a downstream task - which is the focus of this thesis. This field has received less attention compared to MT due to several reasons, which include: the limited availability of data to perform a sound research, contrasting views reported by different researchers about the effectiveness of APE, and limited attention from the industry to use APE in current production pipelines. In this thesis, we perform a thorough investigation of APE as a downstream task in order to: i) understand its potential to improve translation quality; ii) advance the core technology - starting from classical methods to recent deep-learning based solutions; iii) cope with limited and sparse data; iv) better leverage multiple input sources; v) mitigate the task-specific problem of over-correction; vi) enhance neural decoding to leverage external knowledge; and vii) establish an online learning framework to handle data diversity in real-time. All the above contributions are discussed across several chapters, and most of them are evaluated in the APE shared task organized each year at the Conference on Machine Translation. Our efforts in improving the technology resulted in the best system at the 2017 APE shared task, and our work on online learning received a distinguished paper award at the Italian Conference on Computational Linguistics. Overall, outcomes and findings of our work have boost interest among researchers and attracted industries to examine this technology to solve real-word problems.
Resistive contribution in electrical-switching experiments with antiferromagnets

Recent research demonstrated the electrical switching of antiferromagnets via intrinsic spin-orbit torque or the spin Hall effect of an adjacent heavy metal layer. The electrical readout is typically realized by measuring the transverse anisotropic magnetoresistance at planar cross- or star-shaped devices with four or eight arms, respectively. Depending on the material, the current density necessary to switch the magnetic state can be large, often close to the destruction threshold of the device. We demonstrate that the resulting electrical stress changes the film resistivity locally and thereby breaks the fourfold rotational symmetry of the conductor. This symmetry breaking due to film inhomogeneity produces signals, that resemble the anisotropic magnetoresistance and is experimentally seen as a "saw-tooth"-shaped transverse resistivity. This artifact can persist over many repeats of the switching experiment and is not easily separable from the magnetic contribution. We discuss the origin of the artifact, elucidate the role of the film crystallinity, and propose approaches how to separate the resistive contribution from the magnetic contribution.
The focusing logarithmic Schrödinger equation: Analysis of breathers and nonlinear superposition

We consider the logarithmic Schrodinger equation in the focusing regime. For this equation, Gaussian initial data remains Gaussian. In particular, the Gausson-a time-independent Gaussian function-is an orbitally stable solution. In the general case in dimension $d = 1$, the solution with Gaussian initial data is periodic, and we compute some approximations of the period in the case of small and large oscillations, showing that the period can be as large as wanted for the latter. The main result of this article is a principle of nonlinear superposition: starting from an initial data made of the sum of several standing Gaussian functions far from each other, the solution remains close (in $L^2$) to the sum of the corresponding Gaussian solutions for a long time, in square of the distance between the Gaussian functions.
A Conformal Three-Field Formulation for Nonlinear Elasticity: From Differential Complexes to Mixed Finite Element Methods

We introduce a new class of mixed finite element methods for 2D and 3D compressible nonlinear elasticity. The independent unknowns of these conformal methods are displacement, displacement gradient, and the first Piola-Kirchhoff stress tensor. The so-called edge finite elements of the curl operator is employed to discretize the trial space of displacement gradients. Motivated by the differential complex of nonlinear elasticity, this choice guarantees that discrete displacement gradients satisfy the Hadamard jump condition for the strain compatibility. We study the stability of the proposed mixed finite element methods by deriving some inf-sup conditions. By considering 32 choices of simplicial conformal finite elements of degrees 1 and 2, we show that 10 choices are not stable as they do not satisfy the inf-sup conditions. We numerically study the stable choices and conclude that they can achieve optimal convergence rates. By solving several 2D and 3D numerical examples, we show that the proposed methods are capable of providing accurate approximations of strain and stress.
PERICARDIAL THICKENING IS A MAJOR CARDIAC COMPLICATION IN PATIENTS WITH CHRONIC KIDNEY DISEASE AT FIRST PRESENTATION

Background and Aims: A high prevalence of pericardial effusion and low prevalence of pericardial thickening have been documented in end stage renal disease (ESRD) especially in patients undergoing dialysis. The aim of the study was to investigate the presence of pericardial disease in predialysis chronic kidney disease (CKD) patients and to determine relationship of pericardial disease with the aetiology of CKD. Methods: This is a prospective cross-sectional conducted in Enugu, Nigeria. Eighty eight consecutive predialysis CKD patients, and forty four age and sex-matched control subjects were studied using twodimensional echocardiography. Results: Fifty six percent of the patients had pericardial disease while 44% did not. Pericardial disease was detected as early as stage 3 CKD. Of the 88 patients studied 15.9% had pericardial effusion only, 29.5% had pericardial thickening only and 10.2% had a combination of both. Majority (81.8%) were in ESRD. Systolic blood pressure, diastolic blood pressure, CKD stage, and serum phosphate correlated positively with pericardial disease. Haemoglobin concentration, glomerular filtration rate, and serum albumin correlated negatively with the presence of pericardial disease. The aetiology of CKD did not correlate with the presence of pericardial disease. Regression analysis showed that only serum haemoglobin predicted the presence of pericardial effusion. Conclusions: Pericardial disease is common in Nigerian patients with CKD at first evaluation and occurs as early as stage 3 CKD. Pericardial thickening is more prevalent than pericardial effusion. Pericardial effusion is predicted by low haemoglobin concentration. Echocardiography to detect pericardial disease should be part of routine investigation of patients with CKD.
Thermalization of Finite Many-Body Systems by a Collision Model

We construct a collision model description of the thermalization of a finite many-body system by using careful derivation of the corresponding Lindblad-type master equation in the weak coupling regime. Using the example of a two-level target system, we show that collision model thermalization is crucially dependent on the various relevant system and bath timescales and on ensuring that the environment is composed of ancillae which are resonant with the system transition frequencies. Using this, we extend our analysis to show that our collision model can lead to thermalization for certain classes of many-body systems. We establish that for classically correlated systems our approach is effective, while we also highlight its shortcomings, in particular with regards to reaching entangled thermal states.
Prevalence and Antimicrobial Susceptibility Pattern of Streptococcus Pneumoniae Isolated From Respiratory Samples in a South Indian Tertiary Care Hospital.

Objective: Streptococcus pneumniae is the most common cause of community acquired pneumonia and RTI affecting mostly the extremes of age groups. Globally, an estimated 1.6 million people including 1 million children less than 5 years old die of invasive pneumonia disease annually . The present study was conducted to find out the prevalence and antimicrobial susceptibility pattern of respiratory isolates of Streptococcus pneumoniae at our hospital. Materials And Methods: Patients having symptoms of respiratory tract infections , whose respiratory samples (sputum/ throat swab/ bronchial washing) were sent for culture and sensitivity prior to starting of any antibiotics, during the period from June 2012 to June 2013 were included in the study . Informed consent was taken from the patient and ethical clearance was obtained from the institute. Streptococcus pneumoniae was identified by standard laboratory procedure as per CLSI guideline. Antimicrobial susceptibility testing was done by Kirby-Bauer disk diffusion method in Blood Agar media. Results : Out of 300 respiratory samples ,108 Streptococcus pneumoniae were isolated among which 72 (66.67%) were males and 36 (33.33 %) were females . The IPD ( 90 / 83.33% ) contributed more than OPD patients ( 18 / 16.67% ) . Overall male to female ratio was 2 : 1 . The age incidence of (1-10 ) years , (11- 40) years , (41 – 60) years and > 60 years age groups were 30 (27.78 %) , 12 ( 11.11 % ) , 26 (24.07 % ) and 40 (37.04 % ) respectively. Positive samples were received mostly from the TBCD department ( 54 / 50.00 % ) followed by Paediatrics ( 30 / 27.78 % ) , General Medicine ( 15 / 13.88 % ) , Surgery ( 6 / 5.56 % ) and lowest from CTVS department ( 3 / 2.78 % ). Vancomycin and Imipenem were the most active antibiotics with 100 % susceptibility rates. The next best were Amoxyclav, Erythromycin, Levofloxacin, Ceftazidime and Cefepime. Conclusions: Pneumococcal infection easily spreads among community and indiscriminate use of antibiotics contributes to their resistance. Area-wise studies on antimicrobial susceptibility profiles are essential to guide policy on the appropriate use of antibiotics to reduce the morbidity and mortality and also to control the emergence of antimicrobial resistance.
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video

Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function. To this end, we propose a novel approach to learn a task-agnostic skill embedding space from unlabeled multi-view videos. Our method learns a general skill embedding independently from the task context by using an adversarial loss. We combine a metric learning loss, which utilizes temporal video coherence to learn a state representation, with an entropy-regularized adversarial skill-transfer loss. The metric learning loss learns a disentangled representation by attracting simultaneous viewpoints of the same observations and repelling visually similar frames from temporal neighbors. The adversarial skill-transfer loss enhances re-usability of learned skill embeddings over multiple task domains. We show that the learned embedding enables training of continuous control policies to solve novel tasks that require the interpolation of previously seen skills. Our extensive evaluation with both simulation and real world data demonstrates the effectiveness of our method in learning transferable skills from unlabeled interaction videos and composing them for new tasks. Code, pretrained models and dataset are available at http://robotskills.cs. uni-freiburg.de
Semantics for Global and Local Interpretation of Deep Neural Networks

Deep neural networks (DNNs) with high expressiveness have achieved state-of-the-art performance in many tasks. However, their distributed feature representations are difficult to interpret semantically. In this work, human-interpretable semantic concepts are associated with vectors in feature space. The association process is mathematically formulated as an optimization problem. The semantic vectors obtained from the optimal solution are applied to interpret deep neural networks globally and locally. The global interpretations are useful to understand the knowledge learned by DNNs. The interpretation of local behaviors can help to understand individual decisions made by DNNs better. The empirical experiments demonstrate how to use identified semantics to interpret the existing DNNs.
Estimation of phase in EEG rhythms for real-time applications

Objective. Estimating the ongoing phase of oscillations in electroencephalography (EEG) recordings is an important aspect of understanding brain function, as well as for the development of phase-dependent closed-loop real-time systems that deliver stimuli. Such stimuli may take the form of direct brain stimulation (for example transcranial magnetic stimulation), or sensory stimuli (for example presentation of an auditory stimulus). We identify two linked problems related to estimating the phase of EEG rhythms with a specific focus on the alpha-band: 1) when the signal after a specific stimulus is unknown (real-time case), or 2) when it is corrupted by the presence of the stimulus itself (offline analysis). We propose methods to estimate the phase at the presentation time of these stimuli. Approach. Machine learning methods are used to learn the causal mapping from an unprocessed EEG recording to a phase estimate generated with a non-causal signal processing chain. This mapping is then used to predict the phase causally where non-causal methods are inappropriate. Main results. We demonstrate the ability of these machine learning methods to estimate instantaneous phase from an EEG signal subjected to very minor pre-processing with higher accuracy than commonly used signal-processing methods. Significance. Neural oscillations have been implicated in a wide variety of sensory, cognitive and motor functions. The instantaneous phase of these rhythms may reflect specific processes of computation which can be acted upon if they can be estimated with sufficient accuracy. Such brain-state dependent paradigms are of increasing medical and scientific interest.
Chemoprevention and Novel Treatments of Non-Muscle Invasive Bladder Cancer

The Cancer Journal for Clinicians reports there will be 69,250 newly diagnosed cases of bladder cancer in 2011, with 52,020 being men and 17,230 being women with an increase by 50% of annual cases since 1985. Approximately 1 in 5 of those who develop bladder cancer will die due to the disease (relative mortality 20.8%, [Siegel et al., 2011, Golijanin et al., 2006]). Bladder cancer has become the second most prevalent cancer after cancer of the prostate in middle-aged to elderly male individuals. Many patients do not die from their disease, but typically have multiple recurrences (Pelucchi et al., 2006). This lends to a fiveyear cost to Medicare attributed to bladder cancer of over one billion dollars (Yabroff et al., 2008). Tobacco use and exposure to aromatic amines are well established etiologic contributors to bladder cancer and by eliminating or reducing contact with these substances has been shown to reduce such risk. BCG (bacillus Calmette-Guerin) has become the standard of care in the treatment of carcinoma in situ as well as high grade T1 (invasion into the lamina propria) and when not appropriate, Mitomycin-C, has been proven to be an acceptable, albeit, less effective alternate. The goal of this chapter will be to describe novel agents that may show promise in the treatment of bladder cancer. This will include descriptions of the agents, their respective mechanism of action (e.g. molecular/biochemical pathways, cell cycle interaction, necrosis), clinical data, combinations of combinations of regimens and mode of delivery. and mode of delivery. A second goal of this chapter will be to consider whether any of these novel agents may have a role in the prevention of bladder cancer.
Automatic Lumbar Spinal CT Image Segmentation With a Dual Densely Connected U-Net

The clinical treatment of degenerative and developmental lumbar spinal stenosis (LSS) is different. Computed tomography (CT) is helpful in distinguishing degenerative and developmental LSS due to its advantage in imaging osseous and calcified tissues. However, boundaries of the vertebral body, spinal canal and dural sac have low contrast and are hard to identify in a CT image, so the diagnosis depends heavily on the knowledge of expert surgeons and radiologists. In this paper, we develop an automatic lumbar spinal CT image segmentation method to assist LSS diagnosis. The main contributions of this paper are as follows: 1) a new lumbar spinal CT image dataset is constructed that contains 2393 axial CT images collected from 279 patients, with the ground truth of pixel-level segmentation labels; 2) a dual densely connected U-shaped neural network (DDU-Net) is used to segment the spinal canal, dural sac and vertebral body in an end-to-end manner; 3) DDU-Net is capable of segmenting tissues with large scale-variant, inconspicuous edges (e.g., spinal canal) and extremely small size (e.g., dural sac); and 4) DDU-Net is practical, requiring no image preprocessing such as contrast enhancement, registration and denoising, and the running time reaches 12 FPS. In the experiment, we achieved state-of-the-art performance on the lumbar spinal image segmentation task. We expect that the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.
A Comprehensive Survey on Parallel Submanifolds in Riemannian and Pseudo-Riemannian Manifolds

A submanifold of a Riemannian manifold is called a parallel submanifold if its second fundamental form is parallel with respect to the van der Waerden–Bortolotti connection. From submanifold point of view, parallel submanifolds are the simplest Riemannian submanifolds next to totally geodesic ones. Parallel submanifolds form an important class of Riemannian submanifolds since extrinsic invariants of a parallel submanifold do not vary from point to point. In this paper, we provide a comprehensive survey on this important class of submanifolds.
Structured Prediction Helps 3D Human Motion Modelling

Human motion prediction is a challenging and important task in many computer vision application domains. Existing work only implicitly models the spatial structure of the human skeleton. In this paper, we propose a novel approach that decomposes the prediction into individual joints by means of a structured prediction layer that explicitly models the joint dependencies. This is implemented via a hierarchy of small-sized neural networks connected analogously to the kinematic chains in the human body as well as a joint-wise decomposition in the loss function. The proposed layer is agnostic to the underlying network and can be used with existing architectures for motion modelling. Prior work typically leverages the H3.6M dataset. We show that some state-of-the-art techniques do not perform well when trained and tested on AMASS, a recently released dataset 14 times the size of H3.6M. Our experiments indicate that the proposed layer increases the performance of motion forecasting irrespective of the base network, joint-angle representation, and prediction horizon. We furthermore show that the layer also improves motion predictions qualitatively. We make code and models publicly available at https://ait.ethz. ch/projects/2019/spl.