Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Link

Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by various communities, and it is often not clear which algorithm will give the best performance on a specific clustering task. Similarly, we often have multiple ways to measure distances between data points, and the best clustering performance might require a non-trivial combination of those metrics. In this work, we study data-driven algorithm selection and metric learning for clustering problems, where the goal is to simultaneously learn the best algorithm and metric for a specific application. The family of clustering algorithms we consider is parameterized linkage based procedures that includes single and complete linkage. The family of distance functions we learn over are convex combinations of base distance functions. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and simultaneously learn both a near-optimal distance and clustering algorithm from these classes. We also carry out a comprehensive empirical evaluation of our techniques showing that they can lead to significantly improved clustering performance.
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and efficient matrix-vector-multiplication (MVM) operation. MVM being the most dominant operation in DNNs makes crossbars ideally suited. However, various sources of device and circuit non-idealities lead to errors in the MVM output, thereby reducing DNN accuracy. Towards that end, we propose crossbar re-mapping strategies to mitigate line-resistance induced accuracy degradation in DNNs, without having to re-train the learned weights, unlike most prior works. Line-resistances degrade the voltage levels along the crossbar columns, thereby inducing more errors at the columns away from the drivers. We rank the DNN weights and kernels based on a sensitivity analysis, and re-arrange the columns such that the most sensitive kernels are mapped closer to the drivers, thereby minimizing the impact of errors on the overall accuracy. We propose two algorithms $-$ static remapping strategy (SRS) and dynamic remapping strategy (DRS), to optimize the crossbar re-arrangement of a pre-trained DNN. We demonstrate the benefits of our approach on a standard VGG16 network trained using CIFAR10 dataset. Our results show that SRS and DRS limit the accuracy degradation to 2.9\% and 2.1\%, respectively, compared to a 5.6\% drop from an as it is mapping of weights and kernels to crossbars. We believe this work brings an additional aspect for optimization, which can be used in tandem with existing mitigation techniques, such as in-situ compensation, technology aware training and re-training approaches, to enhance system performance.
Planting Trees for scalable and efficient Canonical Hub Labeling

Hub labeling is widely used to improve the latency and throughput of Point-to-Point Shortest Distance (PPSD) queries in graph databases. However, constructing hub labeling, even via the state-of-the-art Pruned Landmark Labeling (PLL) algorithm is computationally intensive. PLL further has a sequential root order label dependency that makes it challenging to parallelize. Hence, the existing parallel approaches are often plagued by label size increase, poor scalability and inability to process large weighted graphs. In this paper, we develop novel algorithms that construct the minimal (guaranteed) Canonical Hub Labeling on shared and distributed-memory parallel systems in a scalable and efficient manner. Our key contribution, the PLaNT algorithm, provides an embarrassingly parallel approach for label construction that scales well beyond the limits of current practice. Our approach is the first to employ a collaborative label partitioning scheme across multiple nodes of a cluster, for completely in-memory labeling and parallel querying on massive graphs whose labels cannot fit on a single node. On a single node with 72-threads, our shared-memory algorithm is up to 47.4X faster than sequential PLL. While our labeling time is comparable to the state-of-the-art shared-memory paraPLL, our label size is 17% smaller on average. PLaNT demonstrates superior parallel scalability. It can process significantly larger graphs and construct labeling orders of magnitude faster than the state-of-the-art distributed paraPLL. Compared to the best shared-memory parallel algorithm, it achieves up to 9.5X speedup on a 64 node cluster.
Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of within-study placebo-treated number of subjects and increase of study power. The meta-analytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical (often control) data. In this paper, we introduce the package RBesT (R Bayesian Evidence Synthesis Tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by MCMC. The numerical MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables via fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations.
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning

Transfer learning across different reinforcement learning (RL) tasks is becoming an increasingly valuable area of research. We consider a goal-based multi-task RL framework and mechanisms by which previously solved tasks can reduce sample complexity and regret when the agent is faced with a new task. Specifically, we introduce two metrics on the state space that encode notions of traversibility of the state space for an agent. Using these metrics a topological covering is constructed by way of a set of landmark states in a fully self-supervised manner. We show that these landmark coverings confer theoretical advantages for transfer learning within the goal-based multi-task RL setting. Specifically, we demonstrate three mechanisms by which landmark coverings can be used for successful transfer learning. First, we extend the Landmark Options Via Reflection (LOVR) framework to this new topological covering; second, we use the landmark-centric value functions themselves as features and define a greedy zombie policy that achieves near oracle performance on a sequence of zero-shot transfer tasks; finally, motivated by the second transfer mechanism, we introduce a learned reward function that provides a more dense reward signal for goal-based RL. Our novel topological landmark covering confers beneficial theoretical results, bounding the Q values at each state-action pair. In doing so, we introduce a mechanism that performs action-pruning at infeasible actions which cannot possibly be part of an optimal policy for the current goal.
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition — RRC-MLT-2019

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.
ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: ${\lambda}$ for the complexity and ${\alpha}$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting ${\lambda}$ for a given ${\alpha}. $ However, glmnet does not simultaneously search the ${\lambda} - {\alpha}$ space for the optional elastic net model. Results: We built the R package ensr, elastic net searcher. enser extends the functionality of glment to search the ${\lambda} - {\alpha}$ space and identify an optimal ${\lambda} - {\alpha}$ pair. Availability: ensr is available from the Comprehensive R Archive Network at this https URL
Non-destructive three-dimensional measurement of hand vein based on self-supervised network

At present, supervised stereo methods based on deep neural network have achieved impressive results. However, in some scenarios, accurate three-dimensional labels are inaccessible for supervised training. In this paper, a self-supervised network is proposed for binocular disparity matching (SDMNet), which computes dense disparity maps from stereo image pairs without disparity labels: In the self-supervised training, we match the stereo images densely to approximate the disparity maps and use them to warp the left and right images to estimate the right and left images; we build the loss function between estimated images and original images for self-supervised training, which adopts perceptual loss to help improve the quality of disparity maps in both detail and structure. Then, we use SDMNet to obtain disparities of hand vein. SDMNet has achieved excellent results on KITTI 2012, KITTI 2015, simulated vein dataset and real vein dataset, outperforming many state-of-the-art supervised matching methods.
Looking Deep into the Rosette Nebula’s Heart: The (Sub)stellar Content of the Massive Young Cluster NGC 2244

As part of the ongoing effort to characterize the low-mass (sub)stellar population in a sample of massive young clusters, we have targeted the ∼2 Myr old cluster NGC 2244. The distance to NGC 2244 from Gaia DR2 parallaxes is 1.59 kpc, with errors of 1% (statistical) and 11% (systematic). We used the Flamingos-2 near-infrared camera at the Gemini-South telescope for deep multi-band imaging of the central portion of the cluster (∼2.4 pc2). We determined membership in a statistical manner, through a comparison of the cluster’s color–magnitude diagram to that of a control field. Masses and extinctions of the candidate members are then calculated with the help of evolutionary models, leading to the first initial mass function (IMF) of the cluster extending into the substellar regime, with the 90% completeness limit around 0.02 M⊙. The IMF is well represented by a broken power law (dN/dM ∝ M−α) with a break at ∼0.4 M⊙. The slope on the high-mass side (0.4–7 M⊙) is α = 2.12 ± 0.08, close to the standard Salpeter slope. In the low-mass range (0.02–0.4 M⊙), we find a slope α = 1.03 ± 0.02, which is at the high end of the typical values obtained in nearby star-forming regions (α = 0.5–1.0), but still in agreement within the uncertainties. Our results reveal no clear evidence for variations in the formation efficiency of brown dwarfs (BDs) and very low-mass stars due to the presence of OB stars, or for a change in stellar densities. Our finding rules out photoevaporation and fragmentation of infalling filaments as substantial pathways for BD formation.
Self-assembly of core-corona particles confined in a circular box

Using Monte Carlo simulations, we study the assembly of colloidal particles interacting via isotropic core-corona potentials in two dimensions and confined in a circular box. We explore the structural variety at low temperatures as function of the number of particles (N) and the size of the confining box and find a rich variety of patterns that are not observed in unconfined flat space. For a small number of particles , we identify the zero-temperature minimal energy configurations at a given box size. When the number of particles is large (), we distinguish different regimes that appear in route towards close packing configurations as the box size decreases. These regimes are characterized by the increase in the number of branching points and their coordination number. Interestingly, we obtain anisotropic open structures with unexpected variety of rotational symmetries that can be controlled by changing the model parameters, and some of the structures have chirality, in spite of the isotropy of the interactions and of the confining box. For arbitrary temperatures, we employ Monte Carlo integration to obtain the average energy and the configurational entropy of the system, which are then used to construct a phase diagram as function of temperature and box radius. Our findings show that confined core-corona particles can be a suitable system to engineer particles with highly complex internal structure that may serve as building blocks in hierarchical assembly.
State-of-the-Art on Query & Transaction Processing Acceleration

The vast amount of processing power and memory bandwidth provided by modern Graphics Processing Units (GPUs) make them a platform for data-intensive applications. The database community identified GPUs as effective co-processors for data processing. In the past years, there were many approaches to make use of GPUs at different levels of a database system. In this Internal Technical Report, based on the [1] and some other research papers, we identify possible research areas at LIP6 for GPU-accelerated database management systems. We describe some key properties, typical challenges of GPU-aware database architectures, and identify major open challenges.
Finite Vacuum Energy from Covariant Canonical Quantization

In this article we present the formulation of Covariant Canonical Quantization (CCQ), where one does not incorporate any classical dynamics in the expansion in terms of creation and annihilation operators. We show that by postponing the spacetime split one gets rid of the divergences leading to the Cosmological Constant Problem. It reduces to conventional canonical quantization when constraining to the mass-shell a priori and is otherwise fully consistent with it. The physical vacuum energies are still recovered, but only at a point that is not related to cosmology any more.
Computational Design of Skinned Quad-Robots

We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins. Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton, and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory. We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype.
Estimating brain age based on a healthy population with deep learning and structural MRI

Numerous studies have established that estimated brain age, as derived from statistical models trained on healthy populations, constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale heterogeneous dataset (N = 10,158, age range 18 - 97) of structural brain MRIs in a healthy population from multiple publicly-available sources, upon which we train a deep learning model for brain age estimation. The availability of the large-scale dataset enables a more uniform age distribution across adult life-span for effective age estimation with no bias toward certain age groups. We demonstrate that the age estimation accuracy, evaluated with mean absolute error (MAE) and correlation coefficient (r), outperforms previously reported methods in both a hold-out test set reflective of the custom population (MAE = 4.06 years, r = 0.970) and an independent life-span evaluation dataset (MAE = 4.21 years, r = 0.960) on which a previous study has evaluated. We further demonstrate the utility of the estimated age in life-span aging analysis of cognitive functions. Furthermore, we conduct extensive ablation tests and employ feature-attribution techniques to analyze which regions contribute the most predictive value, demonstrating the prominence of the frontal lobe as well as pattern shift across life-span. In summary, we achieve superior age estimation performance confirming the efficacy of deep learning and the added utility of training with data both in larger number and more uniformly distributed than in previous studies. We demonstrate the regional contribution to our brain age predictions through multiple routes and confirm the association of divergence between estimated and chronological brain age with neuropsychological measures.
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

In many real-world scenarios, an autonomous agent often encounters various tasks within a single complex environment. We propose to build a graph abstraction over the environment structure to accelerate the learning of these tasks. Here, nodes are important points of interest (pivotal states) and edges represent feasible traversals between them. Our approach has two stages. First, we jointly train a latent pivotal state model and a curiosity-driven goal-conditioned policy in a task-agnostic manner. Second, provided with the information from the world graph, a high-level Manager quickly finds solution to new tasks and expresses subgoals in reference to pivotal states to a low-level Worker. The Worker can then also leverage the graph to easily traverse to the pivotal states of interest, even across long distance, and explore non-locally. We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
On the Fault Proneness of SonarQube Technical Debt Violations: A comparison of eight Machine Learning Techniques

Background. The popularity of tools for analyzing Technical Debt, and particularly that of SonarQube, is increasing rapidly. SonarQube proposes a set of coding rules, which represent something wrong in the code that will soon be reflected in a fault or will increase maintenance effort. However, while the management of some companies is encouraging developers not to violate these rules in the first place and to produce code below a certain technical debt threshold, developers are skeptical of their importance. Objective. In order to understand which SonarQube violations are actually fault-prone and to analyze the accuracy of the fault-prediction model, we designed and conducted an empirical study on 21 well-known mature open-source projects. Method. We applied the SZZ algorithm to label the fault-inducing commits. We compared the classification power of eight Machine Learning models (Logistic Regression, Decision Tree, Random Forest, Extremely Randomized Trees, AdaBoost, Gradient Boosting, XGBoost) to obtain a set of violations that are correlated with fault-inducing commits. Finally, we calculated the percentage of violations introduced in the fault-inducing commit and removed in the fault-fixing commit, so as to reduce the risk of spurious correlations. Result. Among the 202 violations defined for Java by SonarQube, only 26 have a relatively low fault-proneness. Moreover, violations classified as ''bugs'' by SonarQube hardly never become a failure. Consequently, the accuracy of the fault-prediction power proposed by SonarQube is extremely low. Conclusion. The rules applied by SonarQube for calculating technical debt should be thoroughly investigated and their harmfulness needs to be further confirmed. Therefore, companies should carefully consider which rules they really need to apply, especially if their goal is to reduce fault-proneness.
Event extraction based on open information extraction and ontology

The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system for the relationship extraction, to highlight the importance of OIEs in event extraction, and we used the ontology to the event modeling. We tested the results of our approach with test metrics. As a result, the two-level event extraction approach has shown good performance results but requires a lot of expert intervention in the construction of classifiers and this will take time. In this context we have proposed an approach that reduces the expert intervention in the relation extraction, the recognition of entities and the reasoning which are automatic and based on techniques of adaptation and correspondence. Finally, to prove the relevance of the extracted results, we conducted a set of experiments using different test metrics as well as a comparative study.