pwc-archive/papers-with-abstracts huggingface dataset:
Ordered Weighted Average Based grouping of nanomaterials with Arsinh and Dose Response similarity models
Authors: Alex Zabeo, Gianpietro Basei, Georgia Tsiliki, Willie Peijnenburg, Danail Hristozov
Date: 2021-10-07 00:00:00

In the context of the EU GRACIOUS project, we propose a novel procedure for similarity assessment and grouping of nanomaterials. This methodology is based on the (1) Arsinh transformation function for scalar properties, (2) full curve shape comparison by application of a modified Kolmogorov-Smirnov metric for bivariate properties, (3) Ordered Weighted Average (OWA) aggregation-based grouping distance, and (4) hierarchical clustering. The approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. To facilitate the application of the proposed methodology, a software script was developed by using the R programming language which is currently under migration to a web tool. The presented approach was tested against a dataset, derived from literature review, related to immobilisation of Daphnia magna and reporting information on several nanomaterials.
Response of RAW 264.7 and J774A.1 macrophages to particles and nanoparticles of a mesoporous bioactive glass: A comparative study"
Authors: M. J. Feito, L. Casarrubios, M. Onaderra, M. Gomez-Duro, P. Arribas, A. Polo-Montalvo, M. Vallet-Regi, D. Arcos, M. T. Portoles
Date: 2021-10-07 00:00:00

Mesoporous bioactive glasses (MBGs) are bioceramics designed to induce bone tissue regeneration and very useful materials with the ability to act as drug delivery systems. MBGs can be implanted in contact with bone tissue in different ways, as particulate material, in 3D scaffolds or as nanospheres. In this work, we assessed the effects of particles of mesoporous bioactive glass MBG-75S and mesoporous nanospheres NanoMBG-75S on RAW 264.7 and J774A.1 macrophages, which present different sensitivity and are considered as ideal models for the study of innate immune response. After evaluating several cellular parameters (morphology, size, complexity, proliferation, cell cycle and intracellular content of reactive oxygen species), the action of MBG-75S particles and NanoMBG-75S on the polarization of these macrophages towards the proinflammatory (M1) or reparative (M2) phenotype was determined by the expression of specific M1 (CD80) and M2 (CD206, CD163) markers. We previously measured the adsorption of albumin and fibrinogen on MBG-75S particles and the production of proinflammatory cytokines as TNF-{\alpha} and IL-6 by macrophages in response to these particles. This comparative study demonstrates that particles of mesoporous bioactive glass MBG-75S and mesoporous nanospheres NanoMBG-75S allow the appropriated development and function of RAW 264.7 and J774A.1 macrophages and do not induce polarization towards the M1 pro-inflammatory phenotype. Therefore, considering that these mesoporous biomaterials offer the possibility of loading drugs into their pores, the results obtained indicate their high potential for use as drug-delivery systems in bone repair and osteoporosis treatments without triggering an adverse inflammatory response.
Tissue Engineering of Skin Regeneration and Hair Growth
Authors: Mohammadreza Ahmadi
Date: 2021-10-02 00:00:00

Many people suffering from skin disorders such as chronic wounds, non-healing ulcers, and diabetic ulcers need skin repair and regeneration. Aside from the diseases listed above, the industry needed a skin rejuvenation system and regeneration for cosmetic purposes. The procedure used to deliver pluripotent stem cells to the desired tissue was known as reconstructive medicine. Mesenchymal stem cells are the most fascinating since, when put in the correct setting and stimulated with the proper growth factors, they could choose a pathway to differentiate into the desired tissue. They are also very available, inexpensive, simple to extract, and reproducible. These mesenchymal stem cells are derived from bone marrow, bone, connective tissues, fats, and other tissues. Fat is the ideal reservoir for mesenchymal cells that have recently been identified. Fibrous, collagen fibers and fibroblasts create fat.
A Method for Predicting VaR by Aggregating Generalized Distributions Driven by the Dynamic Conditional Score
Authors: Shijia Song, Handong Li
Date: 2021-10-06 00:00:00
Tasks: Management

Constructing a more effective value at risk (VaR) prediction model has long been a goal in financial risk management. In this paper, we propose a novel parametric approach and provide a standard paradigm to demonstrate the modeling. We establish a dynamic conditional score (DCS) model based on high-frequency data and a generalized distribution (GD), namely, the GD-DCS model, to improve the forecasts of daily VaR. The model assumes that intraday returns at different moments are independent of each other and obey the same kind of GD, whose dynamic parameters are driven by DCS. By predicting the motion law of the time-varying parameters, the conditional distribution of intraday returns is determined; then, the bootstrap method is used to simulate daily returns. An empirical analysis using data from the Chinese stock market shows that Weibull-Pareto -DCS model incorporating high-frequency data is superior to traditional benchmark models, such as RGARCH, in the prediction of VaR at high risk levels, which proves that this approach contributes to the improvement of risk measurement tools.
Application of DEA in International Market Selection for the export of products from Spain
Authors: Safa El Kefi
Date: 2021-09-10 00:00:00
Tasks: Benchmarking, Decision Making

This article presents a Benchmarking methodology to support decision-making for international market selection (IMS). In order to do so, we will be using an output-oriented Data Envelopment Analysis (DEA) model. This methodology considers multiple variables validated with a correlation analysis. The methodology is applied to all of the products directly exported from Spain, it takes into consideration different Inputs variables and returns us the efficient and regions generating higher benefits to access international markets with the lowest costs possible.
Developing Medical AI : a cloud-native audio-visual data collection study
Authors: Sagi Schein, Greg Arutiunian, Vitaly Burshtein, Gal Sadeh, Michelle Townshend, Bruce Friedman, Shada Sadr-azodi
Date: 2021-08-17 00:00:00

Designing Artificial Intelligence (AI) solutions that can operate in real-world situations is a highly complex task. Deploying such solutions in the medical domain is even more challenging. The promise of using AI to improve patient care and reduce cost has encouraged many companies to undertake such endeavours. For our team, the goal has been to improve early identification of deteriorating patients in the hospital. Identifying patient deterioration in lower acuity wards relies, to a large degree on the attention and intuition of clinicians, rather than on the presence of physiological monitoring devices. In these care areas, an automated tool which could continuously observe patients and notify the clinical staff of suspected deterioration, would be extremely valuable. In order to develop such an AI-enabled tool, a large collection of patient images and audio correlated with corresponding vital signs, past medical history and clinical outcome would be indispensable. To the best of our knowledge, no such public or for-pay data set currently exists. This lack of audio-visual data led to the decision to conduct exactly such study. The main contributions of this paper are, the description of a protocol for audio-visual data collection study, a cloud-architecture for efficiently processing and consuming such data, and the design of a specific data collection device.
A Three-Way Model for Collective Learning on Multi-Relational Data
Authors: Nickel, Maximilian, Volker Tresp, and Hans-Peter Kriegel.
Date: 2011-01-01 00:00:00
Proceeding: ICML 2011 1
Tasks: Entity Resolution, Relational Reasoning

Relational learning is becoming increasingly important in many areas of application. Here, we
present a novel approach to relational learning
based on the factorization of a three-way tensor.
We show that unlike other tensor approaches, our
method is able to perform collective learning via
the latent components of the model and provide
an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on
both a new dataset and a dataset commonly used
in entity resolution. Furthermore, we show on
common benchmark datasets that our approach
achieves better or on-par results, if compared to
current state-of-the-art relational learning solutions, while it is significantly faster to compute.
Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
Authors: Jorge Francisco Ciprián-Sánchez, Gilberto Ochoa-Ruiz, Lucile Rossi, Frédéric Morandini
Date: 2021-07-30 00:00:00
Proceeding: Applied Sciences 2021 7
Tasks: Fire Detection, Segmentation

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.
Weisfeiler-lehman neural machine for link prediction
Authors: Muhan Zhang, Yixin Chen
Date: 2017-08-01 00:00:00
Proceeding: KDD 2017 8
Tasks: Link Prediction, Prediction

In this paper, we propose a next-generation link prediction method,
Weisfeiler-Lehman Neural Machine (Wlnm), which learns topological features in the form of graph patterns that promote the
formation of links. Wlnm has unmatched advantages including
higher performance than state-of-the-art methods and universal
applicability over various kinds of networks. Wlnm extracts an
enclosing subgraph of each target link and encodes the subgraph
as an adjacency matrix. The key novelty of the encoding comes
from a fast hashing-based Weisfeiler-Lehman (WL) algorithm that
labels the vertices according to their structural roles in the subgraph
while preserving the subgraph’s intrinsic directionality. After that,
a neural network is trained on these adjacency matrices to learn a
predictive model. Compared with traditional link prediction methods, Wlnm does not assume a particular link formation mechanism
(such as common neighbors), but learns this mechanism from the
graph itself. We conduct comprehensive experiments to show that
Wlnm not only outperforms a great number of state-of-the-art
link prediction methods, but also consistently performs well across
networks with different characteristics.
Dimensions of Neural-symbolic Integration - A Structured Survey
Authors: Sebastian Bader, Pascal Hitzler
Date: 2005-11-10 00:00:00
Tasks: Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.
Effectiveness of isolation measures with app support to contain COVID-19 epidemics: a parametric approach
Authors: Andrea Maiorana, Marco Meneghelli, Mario Resnati
Date: 2020-11-04 00:00:00

In this study, we analyze the effectiveness of measures aimed at finding and isolating infected individuals to contain epidemics like COVID-19, as the suppression induced over the effective reproduction number. We develop a mathematical model to compute the relative suppression of the effective reproduction number of an epidemic that such measures produce. This outcome is expressed as a function of a small set of parameters that describe the main features of the epidemic and summarize the effectiveness of the isolation measures. In particular, we focus on the impact when a fraction of the population uses a mobile application for epidemic control. Finally, we apply the model to COVID-19, providing several computations as examples, and a link to a public repository to run custom calculations. These computations display in a quantitative manner the importance of recognizing infected individuals from symptoms and contact-tracing information, and isolating them as early as possible. The computations also assess the impact of each variable on the suppression of the epidemic.
An Efficient Transformer-Based Model for Vietnamese Punctuation Prediction
Authors: Hieu Tran, Cuong V. Dinh, Quang Pham, Binh T. Nguyen
Date: 2021-07-19 00:00:00
Proceeding: IEA/AIE 2021 7
Tasks: Prediction

In both formal and informal texts, missing punctuation marks make the texts confusing and challenging to read. This paper aims to conduct exhaustive experiments to investigate the benefits of the pre-trained Transformer-based models on two Vietnamese punctuation datasets. The experimental results show our models can achieve encouraging results, and adding Bi-LSTM or/and CRF layers on top of the proposed models can also boost model performance. Finally, our best model can significantly bypass state-of-the-art approaches on both the novel and news datasets for the Vietnamese language. It can gain the corresponding performance up to 21.45%  and 18.27%  in the overall F1-scores.
Cross-domain Feature Selection for Language Identification
Authors: Marco Lui, Timothy Baldwin
Date: 2011-11-11 00:00:00
Proceeding: IJCNLP 2011 11
Tasks: feature selection, Language Identification, Transfer Learning

We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language identification system, and develop a feature selection method that generalizes across domains. Our results demonstrate that our method provides improvements in transductive transfer learning for language identification. We provide an implementation of the method and show that our system is faster than popular standalone language identification systems, while maintaining competitive accuracy.
Proposing a System Level Machine Learning Hybrid Architecture and Approach for a Comprehensive Autism Spectrum Disorder Diagnosis
Authors: Ryan Liu, Spencer He
Date: 2021-09-18 00:00:00
Tasks: Diagnostic

Autism Spectrum Disorder (ASD) is a severe neuropsychiatric disorder that affects intellectual development, social behavior, and facial features, and the number of cases is still significantly increasing. Due to the variety of symptoms ASD displays, the diagnosis process remains challenging, with numerous misdiagnoses as well as lengthy and expensive diagnoses. Fortunately, if ASD is diagnosed and treated early, then the patient will have a much higher chance of developing normally. For an ASD diagnosis, machine learning algorithms can analyze both social behavior and facial features accurately and efficiently, providing an ASD diagnosis in a drastically shorter amount of time than through current clinical diagnosis processes. Therefore, we propose to develop a hybrid architecture fully utilizing both social behavior and facial feature data to improve the accuracy of diagnosing ASD. We first developed a Linear Support Vector Machine for the social behavior based module, which analyzes Autism Diagnostic Observation Schedule (ADOS) social behavior data. For the facial feature based module, a DenseNet model was utilized to analyze facial feature image data. Finally, we implemented our hybrid model by incorporating different features of the Support Vector Machine and the DenseNet into one model. Our results show that the highest accuracy of 87% for ASD diagnosis has been achieved by our proposed hybrid model. The pros and cons of each module will be discussed in this paper.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Authors: Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma
Date: 2021-10-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Adversarial Robustness

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversarial training has demonstrated promising results. However, despite preliminary understandings developed for adversarial training, it is still not clear, from the architectural perspective, what configurations can lead to more robust DNNs. In this paper, we address this gap via a comprehensive investigation on the impact of network width and depth on the robustness of adversarially trained DNNs. Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness. We also provide a theoretical analysis explaning why such network configuration can help robustness. These architectural insights can help design adversarially robust DNNs. Code is available at \url{https://github.com/HanxunH/RobustWRN}.
FAST-RIR: Fast neural diffuse room impulse response generator
Authors: Anton Ratnarajah, Shi-Xiong Zhang, Meng Yu, Zhenyu Tang, Dinesh Manocha, Dong Yu
Date: 2021-10-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), CPU, GPU, Room Impulse Response (RIR), speech-recognition, Speech Recognition

We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse reflections for a given acoustic environment. Our FAST-RIR is capable of generating RIRs for a given input reverberation time with an average error of 0.02s. We evaluate our generated RIRs in automatic speech recognition (ASR) applications using Google Speech API, Microsoft Speech API, and Kaldi tools. We show that our proposed FAST-RIR with batch size 1 is 400 times faster than a state-of-the-art diffuse acoustic simulator (DAS) on a CPU and gives similar performance to DAS in ASR experiments. Our FAST-RIR is 12 times faster than an existing GPU-based RIR generator (gpuRIR). We show that our FAST-RIR outperforms gpuRIR by 2.5% in an AMI far-field ASR benchmark.
Pyxis: An Open-Source Performance Dataset of Sparse Accelerators
Authors: Linghao Song, Yuze Chi, Jason Cong
Date: 2021-10-08 00:00:00

Specialized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures or/and representations exist in a wide range of applications. However, it is challenging to design accelerators for sparse applications because no architecture or performance-level analytic models are able to fully capture the spectrum of the sparse data. Accelerator researchers rely on real execution to get precise feedback for their designs. In this work, we present PYXIS, a performance dataset for specialized accelerators on sparse data. PYXIS collects accelerator designs and real execution performance statistics. Currently, there are 73.8 K instances in PYXIS. PYXIS is open-source, and we are constantly growing PYXIS with new accelerator designs and performance statistics. PYXIS can benefit researchers in the fields of accelerator, architecture, performance, algorithm, and many related topics.
Decomposition method for complex target RCS measuring
Authors: Alexander Maslovskiy Department of Theoretical Radiophysics, Ukraine Nikolay Kolchigin Department of Theoretical Radiophysics, Ukraine Maxim Legenkiy Department of Theoretical Radiophysics, Ukraine Mariya Antyufeyeva Department of Theoretical Radiophysics, V.N.Karazin Kharkiv National University, Kharkiv, Ukraine
Date: 2017-11-09 00:00:00
Proceeding: IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON) 2017 11
Tasks: Object

In this paper a method of monostatic RCS measuring in real conditions for complex shaped objects is proposed. The basic idea of the method is to provide measuring in near field zone for different parts of the object (fragments) separately. This technique is titled "decomposition method". After such measurements all RCS data are summed and one can obtain the average RCS of investigated object. Such method is much more accessible in comparison with natural measurements in far field zone. In this paper the decomposition method is tested numerically. For this a model of complex shape object (tank T-90) is divided into the fragments for some direction of view. It is shown that the sum of RCS of the fragments is close to the full object RCS for corresponding direction.
How to Build a Curb Dataset with LiDAR Data for Autonomous Driving
Authors: Dongfeng Bai, Tongtong Cao, Jingming Guo, Bingbing Liu
Date: 2021-10-08 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Motion Planning, object-detection, Object Detection

Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...