pwc-archive/papers-with-abstracts huggingface dataset:
An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound
Authors: Ankan Ghosh Dastider, Farhan Sadik, Shaikh Anowarul Fattah
Date: 2021-05-01 00:00:00
Tasks: severity prediction

The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7-12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.
Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals
Authors: Minsoo Song, Seokjae Lim, Wonjun Kim
Date: 2021-01-08 00:00:00
Tasks: Decoder, Depth Estimation, Monocular Depth Estimation

With a great success of the generative model via deep neural networks, monocular depth estimation has been actively studied by exploiting various encoder-decoder architectures. However, the decoding process in most previous methods, which repeats simple up-sampling operations, probably fails to fully utilize underlying properties of well-encoded features for monocular depth estimation. To resolve this problem, we propose a simple but effective scheme by incorporating the Laplacian pyramid into the decoder architecture. Specifically, encoded features are fed into different streams for decoding depth residuals, which are defined by decomposition of the Laplacian pyramid, and corresponding outputs are progressively combined to reconstruct the final depth map from coarse to fine scales. This is fairly desirable to precisely estimate the depth boundary as well as the global layout. We also propose to apply weight standardization to pre-activation convolution blocks of the decoder architecture, which gives a great help to improve the flow of gradients and thus makes optimization easier. Experimental results on benchmark datasets constructed under various indoor and outdoor environments demonstrate that the proposed method is effective for monocular depth estimation compared to state-of-the-art models. The code and model are publicly available at: https://github.com/tjqansthd/LapDepth-release.
TeLU: A New Activation Function for Deep Learning
Authors: Marina Adriana Mercioni, Stefan Holban
Date: 2021-01-01 00:00:00
Tasks: Deep Learning

In this paper we proposed two novel activation functions, which we called them TeLU and TeLU learnable. These proposals are a combination of ReLU (Rectified Linear Unit), tangent(tanh), and ELU (Exponential Linear Units) without and with a learnable parameter. We prove that the activation functions TeLU and TeLU learnable give better results than other popular activation functions, including ReLU, Mish, TanhExp, using current architectures tested on Computer Vision datasets.
Temperature Effect on ZnGexSn1-xN2/GaN Multiwell Quantum Solar Cells
Authors: A.Laidouci
Date: 2019-11-01 00:00:00

In this paper, w e are interested in the simulation and modeling of ZnGe x Sn 1-x N 2 /GaN quantum wells (QWs) solar cell, according to many studies before the solar cells always are sensitive to the temperature, for this case, the effect of the temperature on photovoltaic characteristics such as power density-voltage P(V), current density-voltage I(V), and the external quantum efficiency EQE are investigated. The most parameter affected by the changing in the temperature is the open circuit voltage Voc. for optimization. It has been found at 280 K the cell gives better results of open circuit voltage and conversion efficiency their values are 2.18 V and 2.75% respectively.
Temperature Effect on InGaN/GaN Multiwell Quantum Solar Cells Performances
Authors: A.Laidouci
Date: 2018-03-01 00:00:00

10.1007/978-3-319-73192-6_52
Optimization of Copper Indium Gallium Diselenide Thin Film Solar Cell (CIGS)
Authors: A.Laidouci
Date: 2018-03-01 00:00:00

Optimization of Copper Indium Gallium Diselenide Thin Film Solar Cell (CIGS)
Temperature Effect on GaSb/GaAs Quantum Dots Solar Cell
Authors: A.Laidouci
Date: 2017-12-01 00:00:00

Temperature Effect on GaSb/GaAs Quantum Dots Solar Cell
Effet de la perte d'énergie sur la probabilité de diffusion d'ions par une surface solide à basse énergie
Authors: A.Laidouci
Date: 2016-07-01 00:00:00

Nous nous intéressons dans ce travail au calcul de la probabilité de diffusion d'ions lents (quelques Kev) par une surface solide, ce calcul est basé sur la résolution de l'équation de Boltzmann dans la théorie de transport. Dans ce modèle valable pour les faibles angles de diffusion, les collisions sont binaires et la distribution des centres diffuseurs dans la cible est homogène, ce travail est pour but l'étude de l'effet de la perte d'énergie sur la probabilité de diffusion d'ions par une surface solide à basse énergie. Nous avons décrit l'interaction projectile-atome cible par un potentiel coulombien écranté proposé par Zigler - Birsack - Littmark potentiel (ZBL), Nous avons considéré les systèmes He+/Si et He+/Ni pour différentes valeurs de l'énergie incidente des projectiles .Différentes valeurs de l'angle de diffusion ont été étudiées. Dans une première étape, nous avons calculé AP, la probabilité de diffusion en négligeant la perte d'énergie du projectile dans la cible .Cependant, pour les grandes valeurs du parcours dans la cible, la perte d'énergie devient importante et donc non négligeable. Nous avons ainsi calculé la probabilité de diffusion AP2 en tenant compte de cet effet .En calculant la correction relative apporté à la probabilité de diffusion, nous avons montré qu'elle dépend inversement à l'énergie incidente et l'angle de diffusion et proportionnellement au numéro atomique de la cible et de distance parcourue.
Hybrid Model with Time Modeling for Sequential Recommender Systems
Authors: Marlesson R. O. Santana, Anderson Soares
Date: 2021-03-07 00:00:00
Tasks: Deep Learning, Recommendation Systems, Session-Based Recommendations

Deep learning based methods have been used successfully in recommender system problems. Approaches using recurrent neural networks, transformers, and attention mechanisms are useful to model users' long- and short-term preferences in sequential interactions. To explore different session-based recommendation solutions, Booking.com recently organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. This study presents our approach to this challenge. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Further, we proposed some changes to Neural Attentive Recommendation Machine (NARM), adapted its architecture for the challenge objective, and implemented training approaches that can be used in any session-based model to improve accuracy. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.
A Study of Face Obfuscation in ImageNet
Authors: Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, Olga Russakovsky
Date: 2021-03-10 00:00:00
Tasks: Attribute, Object, object-detection, Object Detection, Object Recognition, Scene Recognition, Transfer Learning

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We first annotate faces in the dataset. Then we demonstrate that face obfuscation has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on obfuscated images and observe that the overall recognition accuracy drops only slightly (<= 1.0%). Further, we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classification, and object detection) and show that features learned on obfuscated images are equally transferable. Our work demonstrates the feasibility of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark, and suggests an important path for future visual datasets. Data and code are available at https://github.com/princetonvisualai/imagenet-face-obfuscation.
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review
Authors: Dan Hendrycks, Collin Burns, Anya Chen, Spencer Ball
Date: 2021-03-10 00:00:00

Many specialized domains remain untouched by deep learning, as large labeled datasets require expensive expert annotators. We address this bottleneck within the legal domain by introducing the Contract Understanding Atticus Dataset (CUAD), a new dataset for legal contract review. CUAD was created with dozens of legal experts from The Atticus Project and consists of over 13,000 annotations. The task is to highlight salient portions of a contract that are important for a human to review. We find that Transformer models have nascent performance, but that this performance is strongly influenced by model design and training dataset size. Despite these promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Authors: Bahar Taskesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn
Date: 2021-03-10 00:00:00
Tasks: Discrete Choice Models

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, machine learning and computer vision, however, this perception has not yet received a theoretical justification. To fill this gap, we prove that computing the Wasserstein distance between a discrete probability measure supported on two points and the Lebesgue measure on the standard hypercube is already #P-hard. This insight prompts us to seek approximate solutions for semi-discrete optimal transport problems. We thus perturb the underlying transportation cost with an additive disturbance governed by an ambiguous probability distribution, and we introduce a distributionally robust dual optimal transport problem whose objective function is smoothed with the most adverse disturbance distributions from within a given ambiguity set. We further show that smoothing the dual objective function is equivalent to regularizing the primal objective function, and we identify several ambiguity sets that give rise to several known and new regularization schemes. As a byproduct, we discover an intimate relation between semi-discrete optimal transport problems and discrete choice models traditionally studied in psychology and economics. To solve the regularized optimal transport problems efficiently, we use a stochastic gradient descent algorithm with imprecise stochastic gradient oracles. A new convergence analysis reveals that this algorithm improves the best known convergence guarantee for semi-discrete optimal transport problems with entropic regularizers.
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Authors: Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang
Date: 2021-03-10 00:00:00
Tasks: Federated Learning

Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and lack of resources prevent a site from leveraging subject-level data from other sites. We propose a tree-based model averaging approach to improve the estimation accuracy of conditional average treatment effects (CATE) at a target site by leveraging models derived from other potentially heterogeneous sites, without them sharing subject-level data. To our best knowledge, there is no established model averaging approach for distributed data with a focus on improving the estimation of treatment effects. Specifically, under distributed data networks, our framework provides an interpretable tree-based ensemble of CATE estimators that joins models across study sites, while actively modeling the heterogeneity in data sources through site partitioning. The performance of this approach is demonstrated by a real-world study of the causal effects of oxygen therapy on hospital survival rate and backed up by comprehensive simulation results.
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
Authors: Benjamin Eysenbach, Sergey Levine
Date: 2021-03-10 00:00:00
Proceeding: ICLR 2022 4
Tasks: Reinforcement Learning (RL)

Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that maximum entropy (MaxEnt) RL maximizes a lower bound on a robust RL objective, and thus can be used to learn policies that are robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL is a simple robust RL method with appealing formal guarantees.
A registration error estimation framework for correlative imaging
Authors: Guillaume Potier, Frédéric Lavancier, Stephan Kunne, Perrine Paul-Gilloteaux
Date: 2021-03-10 00:00:00

Correlative imaging workflows are now widely used in bioimaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration. We propose an application of multivariate linear regression to solve the registration problem allowing us to propose a framework for the estimation of the associated error in the case of rigid and affine transformations and with anisotropic noise. These developments can be used as a decision-support tool for the biologist to analyze multimodal correlative images and are available under Ec-CLEM, an open-source plugin under ICY.
Involution: Inverting the Inherence of Convolution for Visual Recognition
Authors: Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen
Date: 2021-03-10 00:00:00
Proceeding: CVPR 2021 1
Tasks: Image Classification

Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel-specific. Instead, we present a novel atomic operation for deep neural networks by inverting the aforementioned design principles of convolution, coined as involution. We additionally demystify the recent popular self-attention operator and subsume it into our involution family as an over-complicated instantiation. The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition, powering different deep learning models on several prevalent benchmarks, including ImageNet classification, COCO detection and segmentation, together with Cityscapes segmentation. Our involution-based models improve the performance of convolutional baselines using ResNet-50 by up to 1.6% top-1 accuracy, 2.5% and 2.4% bounding box AP, and 4.7% mean IoU absolutely while compressing the computational cost to 66%, 65%, 72%, and 57% on the above benchmarks, respectively. Code and pre-trained models for all the tasks are available at https://github.com/d-li14/involution.
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Authors: Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Date: 2021-03-10 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases. In this work, we synthesize foundational work on IML methods and evaluation into an actionable taxonomy. This taxonomy serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components. It also provides the foundation from which we describe a three-step workflow to better enable researchers and consumers to work together to discover what types of methods are useful for what use cases. Eventually, by building on the results generated from this workflow, a more complete version of the taxonomy will increasingly allow consumers to find relevant methods for their target use cases and researchers to identify applicable use cases for their proposed methods.
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events
Authors: Marc-Henri Bleu-Laine, Tejas G. Puranik, Dimitri N. Mavris, Bryan Matthews
Date: 2021-03-10 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Multiple Instance Learning, Weakly-supervised Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network-Recurrent Neural Network (MHCNN-RNN) architecture. Multi-class classifiers are then created and compared, enabling the prediction of different adverse events for any given flight by combining binary classifiers, and by modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed that the multiple binary classifiers perform better and are able to accurately forecast high speed and high path angle events during the approach phase. Multiple binary classifiers are also capable of determining the aircraft's parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.
CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis
Authors: Amin Heyrani Nobari, Muhammad Fathy Rashad, Faez Ahmed
Date: 2021-03-10 00:00:00
Tasks: Design Synthesis, Novelty Detection

Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing `creative' designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration.
Symmetry Breaking in Symmetric Tensor Decomposition
Authors: Yossi Arjevani, Joan Bruna, Michael Field, Joe Kileel, Matthew Trager, Francis Williams
Date: 2021-03-10 00:00:00
Tasks: Tensor Decomposition

In this note, we consider the highly nonconvex optimization problem associated with computing the rank decomposition of symmetric tensors. We formulate the invariance properties of the loss function and show that critical points detected by standard gradient based methods are \emph{symmetry breaking} with respect to the target tensor. The phenomena, seen for different choices of target tensors and norms, make possible the use of recently developed analytic and algebraic tools for studying nonconvex optimization landscapes exhibiting symmetry breaking phenomena of similar nature.