Miracle-dz/UniFAR-FTUs huggingface dataset:
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

©35th International Conference on Machine Learning, ICML 2018.All Rights Reserved. Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instability in optimization. Our experiments in model-based reinforcement learning imply that the problem is not just a numerical issue, but it may be caused by a fundamental chaos-like nature of long chains of nonlinear computations. Not only do the magnitudes of the gradients become large, the direction of the gradients becomes essentially random. We show that reparameterization gradients suffer from the problem, while likelihood ratio gradients are robust. Using our insights, we develop a model-based policy search framework, Probabilistic Inference for Particle-Based Policy Search (PIPPS), which is easily extensible, and allows for almost arbitrary models and policies, while simultaneously matching the performance of previous data-efficient learning algorithms. Finally, we invent the total propagation algorithm, which efficiently computes a union over all pathwise derivative depths during a single backwards pass, automatically giving greater weight to estimators with lower variance, sometimes improving over reparameterization gradients by 106 times.
Two modified spectral conjugate gradient methods and their global convergence for unconstrained optimization

ABSTRACT In this paper, two modified spectral conjugate gradient methods which satisfy sufficient descent property are developed for unconstrained optimization problems. For uniformly convex problems, the first modified spectral type of conjugate gradient algorithm is proposed under the Wolfe line search rule. Moreover, the search direction of the modified spectral conjugate gradient method is sufficiently descent for uniformly convex functions. Furthermore, according to the Dai–Liao's conjugate condition, the second spectral type of conjugate gradient algorithm can generate some sufficient decent direction at each iteration for general functions. Therefore, the second method could be considered as a modification version of the Dai–Liao's algorithm. Under the suitable conditions, the proposed algorithms are globally convergent for uniformly convex functions and general functions. The numerical results show that the approaches presented in this paper are feasible and efficient.
Nonhomogeneous Boundary Value Problems of Nonlinear Schrödinger Equations in a Half Plane

This paper discusses the initial-boundary-value problems (IBVP) of nonlinear Schr\"odinger equations posed in a half plane $\mathbb{R} \times \mathbb{R}^+$ with nonhomogeneous Dirichlet boundary conditions. For any given $s \ge 0$, if the initial data $\varphi (x, y)$ are in Sobolev space $H^s(\mathbb{R}\times \mathbb{R}^+) $ with the boundary data $ h ( x, t) $ in an optimal space ${\cal H}^s(0,T)$ as defined in the introduction, which is slightly weaker than the space $$H^{(2s+1)/4}_{t} ([0, T]; L_x^2(\mathbb{R} ) ) \cap L^2_t ( [ 0, T]; H^{s+ 1/2} _x ( \mathbb{R} ) ),$$ the local well-posedness of the IBVP in $ C ( [0, T] ; H^s ( \mathbb{R}\times \mathbb{R}^+ ) )$ is proved. The global well-posedness is also discussed for $s = 1$. The main idea of the proof is to derive a boundary integral operator for the corresponding nonhomogeneous boundary condition and obtain the Strichartz's estimates for this operator. The results presented in the paper hold for the IBVP posed in a half space $ \mathbb{R}^n\times \mathbb{R}^+$ with any $n>1$.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism

The relational facts in sentences are often complicated. Different relational triplets may have overlaps in a sentence. We divided the sentences into three types according to triplet overlap degree, including Normal, EntityPairOverlap and SingleEntiyOverlap. Existing methods mainly focus on Normal class and fail to extract relational triplets precisely. In this paper, we propose an end-to-end model based on sequence-to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes. We adopt two different strategies in decoding process: employing only one united decoder or applying multiple separated decoders. We test our models in two public datasets and our model outperform the baseline method significantly.
The Effect of Stimulus Size and Position on the Task Performance of an Eye Mouse: Comparing Blink and Dwell Methods in a Click Task

ABSTRACT Eye mouse devices are operated by ocular movements, which have proven to be useful for disabled people who find eye control to be the easiest input method. However, there are few studies comparing the two typical click methods for eye mouse (blink and dwell) in identical experimental conditions for general object selection operations. Especially, investigation on task performance (effectiveness and efficiency) of selecting objects is not sufficient. Therefore, this study aims to investigate the relationships between task performance of selecting a stimulus and independent variables with comparing the two different click methods. To investigate the relationship, movement distance, eye input method, stimulus position, and stimulus size were considered as independent variables. Results of the blink method revealed a higher efficiency than the dwell method, particularly as the stimulus size increased. Also, both efficiency and effectiveness of stimuli in upper parts of screen was higher than for those in lower ones. Additionally, efficiency was remarkably higher when the stimulus size increased. The results of this study may be used as guidelines to design interfaces using an eye mouse device. Especially, it can be applied to various conditions of selecting an object in the working environment using the eye mouse.
Spatiotemporal Analysis of Developing Brain Networks

Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3–20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3–6, 7–12, and 13–20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.
Researchers' uses of and disincentives for sharing their research identity information in research information management systems

This study examined how researchers used research information systems (RIMSs) and the relationships among researchers' seniority, discipline, and types and extent of RIMS use. Most researchers used RIMSs to discover research content. Fewer used RIMSs for sharing and promoting their research. Early career researchers were more frequent users of RIMSs than were associate and full professors. Likewise, assistant professors and postdocs exhibited a higher probability of using RIMSs to promote their research than did students and full professors. Humanities researchers were the least frequent users of RIMSs. Moreover, humanities scholars used RIMSs to evaluate research less than did scholars in other disciplines. The tasks of discovering papers, monitoring the literature, identifying potential collaborators, and promoting research were predictors of higher RIMS use. Researchers who engaged in promoting their research, evaluating research, or monitoring the literature showed a greater propensity to have a public RIMS profile. Furthermore, researchers mostly agreed that not being required, having no effect on their status, not being useful, or not being a norm were reasons for not having a public RIMS profile. Humanities scholars were also more likely than social scientists to agree that having a RIMS profile was not a norm in their fields.
Peeking Behind the Curtains of Serverless Platforms

Serverless computing is an emerging paradigm in which an application's resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services' easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free.
A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height

Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.
Deep Brain Stimulation in Moroccan Patients With Parkinson's Disease: The Experience of Neurology Department of Rabat

Introduction: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is known as a therapy of choice of advanced Parkinson's disease. The present study aimed to assess the beneficial and side effects of STN DBS in Moroccan Parkinsonian patients. Material and Methods: Thirty five patients underwent bilateral STN DBS from 2008 to 2016 in the Rabat University Hospital. Patients were assessed preoperatively and followed up for 6 to 12 months using the Unified Parkinson's Disease Rating Scale in four conditions (stimulation OFF and ON and medication OFF and ON), the levodopa-equivalent daily dose (LEDD), dyskinesia and fluctuation scores and PDQ39 scale for quality of life (QOL). Postoperative side effects were also recorded. Results: The mean age at disease onset was 42.31 ± 7.29 years [28–58] and the mean age at surgery was 54.66 ± 8.51 years [34–70]. The median disease duration was 11.95 ± 4.28 years [5–22]. Sixty-three percentage of patients were male. 11.4% of patients were tremor dominant while 45.71 showed akinetic-rigid form and 42.90 were classified as mixed phenotype. The LEDD before surgery was 1200 mg/day [800-1500]. All patients had motor fluctuations whereas non-motor fluctuations were present in 61.80% of cases. STN DBS decreased the LEDD by 51.72%, as the mean LEDD post-surgery was 450 [188-800]. The UPDRS-III was improved by 52.27%, dyskinesia score by 66.70% and motor fluctuations by 50%, whereas QOL improved by 27.12%. Post-operative side effects were hypophonia (2 cases), infection (3 cases), and pneumocephalus (2 cases). Conclusion: Our results showed that STN DBS is an effective treatment in Moroccan Parkinsonian patients leading to a major improvement of the most disabling symptoms (dyskinesia, motor fluctuation) and a better QOL.
Impact of COX2 Inhibitor for Regulation of PD-L1 Expression in Non-small Cell Lung Cancer

Background/Aim: There is no clear evidence in the literature regarding the regulation of programmed cell death-ligand 1 (PD-L1) expression by cyclo-oxygenase-2 (COX2). In this study, whether PD-L1 expression was regulated by COX2 activity was examined in vitro. Materials and Methods: Resected lung cancer specimens were analyzed for PD-L1 and COX2 expression by immunohistochemical analysis. Next, co-localization of PD-L1 and COX2 expression was analyzed by double-fluorescence staining. Lastly, the effect of COX2 inhibition on the expression of PD-L1 was examined using lung cancer cell lines. Results: PD-L1 expression was significantly correlated with COX2 expression in the resected specimens. The majority of cancer cells that expressed PD-L1 also co-expressed COX2. However, treatment of lung cancer cell lines with a COX2 inhibitor had no impact on PD-L1 expression. Conclusion: Our results suggest that COX2 inhibition might have no effect on the usage of immune checkpoint inhibitors in lung cancer treatment.
Patch-based MRI Analysis: From voxel to knowledge. (Analyse par patchs des IRM : du voxel à la connaissance)

This Habilitation thesis presents the work that I have done on quantitative MR analysis, computeraided diagnosis and brain monitoring. These topics are detailed in 3 chapters. In the first chapter, I introduce the principle of our patch-based segmentation method and their extensions. Afterwards, the results obtained by our patch-based segmentation method are analyzed for several applications. In the second chapter, I show how we have extended our patch-based segmentation framework to patch-based grading of brain structures. Then, the performance of our patch-based grading method to achieve Alzheimer’s disease diagnosis and prognosis is evaluated. In the third chapter, I describe the tools that we developed to perform brain monitoring. First, the pipeline proposed to perform quantitative brain analysis are detailed. Second, the construction of the standard models is presented. Moreover, new medical and neuroscientific knowledge on the development and the aging of the brain produced during their estimation are discussed. Finally, the developed open access volBrain platform is described. To conclude this manuscript, I discuss the limitations and the perspectives of my research about patchbased MRI analysis.
DeepPavlov: Open-Source Library for Dialogue Systems

Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.
Preference-based Online Learning with Dueling Bandits: A Survey

In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.
Characters or Morphemes: How to Represent Words?

In this paper, we investigate the effects of using subword information in representation learning. We argue that using syntactic subword units effects the quality of the word representations positively. We introduce a morpheme-based model and compare it against to word-based, character-based, and character n-gram level models. Our model takes a list of candidate segmentations of a word and learns the representation of the word based on different segmentations that are weighted by an attention mechanism. We performed experiments on Turkish as a morphologically rich language and English with a comparably poorer morphology. The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization

Word2vec embeddings are limited to computing vectors for in-vocabulary terms and do not take into account sub-word information. Character-based representations, such as fastText, mitigate such limitations. We optimize and compare these representations for the biomedical domain. fastText was found to consistently outperform word2vec in named entity recognition tasks for entities such as chemicals and genes. This is likely due to gained information from computed out-of-vocabulary term vectors, as well as the word compositionality of such entities. Contrastingly, performance varied on intrinsic datasets. Optimal hyper-parameters were intrinsic dataset-dependent, likely due to differences in term types distributions. This indicates embeddings should be chosen based on the task at hand. We therefore provide a number of optimized hyper-parameter sets and pre-trained word2vec and fastText models, available on https://github.com/dterg/bionlp-embed.
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resource-scarce and do not have such parallel data due to low volume of queries and non-existence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resource-scarce languages. We propose a sequence-to-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.
Redesigning LSMs for Nonvolatile Memory with NoveLSM

We present NoveLSM, a persistent LSM-based keyvalue storage system designed to exploit non-volatile memories and deliver low latency and high throughput to applications. We utilize three key techniques - a byte-addressable skip list, direct mutability of persistent state, and opportunistic read parallelism - to deliver high performance across a range of workload scenarios. Our analysis with popular benchmarks and real-world workload reveal up to a 3.8x and 2x reduction in write and read access latency compared to LevelDB. Storing all the data in a persistent skip list and avoiding block I/O provides more than 5x and 1.9x higher write throughput over LevelDB and RocksDB. Recovery time improves substantially with NoveLSM's persistent skip list.