pwc-archive/papers-with-abstracts huggingface dataset:
The correlated variability control problem: a dominant approach
Authors: Marcela Ordorica Arango, Alessio Franci
Date: 2021-10-14 00:00:00

Given a population of interconnected input-output agents repeatedly exposed to independent random inputs, we talk of correlated variability when agents' outputs are variable (i.e., they change randomly at each input repetition) but correlated (i.e., they do not vary independently across input repetitions). Correlated variability appears at multiple levels in neuronal systems, from the molecular level of protein expression to the electrical level of neuronal excitability, but its functions and origins are still debated. Motivated by advancing our understanding of correlated variability, we introduce the (linear) "correlated variability control problem" as the problem of controlling steady-state correlations in a linear dynamical network in which agents receive independent random inputs. Although simple, the chosen setting reveals important connections between network structure, in particular, the existence and the dimension of dominant (i.e., slow) dynamics in the network, and the emergence of correlated variability.
Microfluidic bioanalytical flow cells for biofilm studies: A review
Authors: Mohammad Pousti, MirPouyan Zarabadi, Mehran Abbaszadeh Amirdehi, François Paquet-Mercier, Jesse Greener
Date: 2021-10-13 00:00:00

Bacterial biofilms are among the oldest and most prevalent multicellular life forms on Earth and are increasingly relevant in research areas related to industrial fouling, medicine and biotechnology. The main hurdles to obtaining definitive experimental results include time-varying biofilm properties, structural and chemical heterogeneity, and especially their strong sensitivity to environmental cues. Therefore, in addition to judicious choice of measurement tools, a well-designed biofilm study requires strict control over experimental conditions, more so than most chemical studies. Due to excellent control over a host of physiochemical parameters, microfluidic flow cells have become indispensable in microbiological studies. Not surprisingly, the number of lab-on-chip studies focusing on biofilms and other microbiological systems with expanded analytical capabilities has expanded rapidly in the past decade. In this paper, we comprehensively review the current state of microfluidic bioanalytical research applied to bacterial biofilms and offer a perspective on new approaches that are expected to drive continued advances in this field.
Understanding of a brain spatial map based on threshold-free function dendrogramization
Authors: Hyekyoung Lee, Hyejin Kang, Youngmin Huh, Hongyoon Choi, Dong Soo Lee
Date: 2021-10-13 00:00:00
Tasks: Functional Connectivity

Linear matrix factorizations (LMFs) such as independent component analysis (ICA), principal component analysis (PCA), and their extensions, have been widely used for finding relevant spatial maps in brain imaging data. The last step of an LMF before interpretation is usually to extract the activated brain regions from the map by thresholding. However, it is difficult to determine an appropriate threshold level. Thresholding can remove the underlying properties of spatial maps and their features imposed by the model. In this study, we propose a threshold-free activated region extraction method which involves simplifying a brain spatial map to a dendrogram through Morse filtration. Since a dendrogram is related to the change of clustering structure in Rips filtration, we first show the relationship between the Rips filtration of a graph and the Morse filtration of a function. Then, we dendrogramize a spatial map in order to visualize the activated brain regions and the range of their importance in a spatial map. The proposed method can be applied to any spatial maps that a user wants to threshold and interpret. In experiments, we applied the proposed method to independent component maps (ICMs) obtained from resting-state fMRI data, and the dominant subnetworks obtained by the PCA of a correlation-based functional connectivity of FDG PET Alzheimer's disease neuroimaging initiative (ADNI) data. We found that dendrogramization can help to understand a brain spatial map without thresholding.
A parameter study on optimal scaffolds in a simple model for bone regeneration
Authors: Patrick Dondl, Marius Zeinhofer
Date: 2021-10-14 00:00:00

We propose a simple model for scaffold aided bone regeneration. In this model, only macroscopic quantities, e.g., locally averaged osteoblast densities, are considered. This allows for use of this model in an optimization algorithm, whose outcome is an optimal scaffold porosity distribution. This optimal scaffold naturally depends on the choice of parameters in the model, and we provide a parameter study with a particular focus on patients with reduced bone regeneration or reduced vascularization capacity.
ETF Risk Models
Authors: Zura Kakushadze, Willie Yu
Date: 2021-10-14 00:00:00
Tasks: Binary Classification

We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of https://ssrn.com/abstract=2600798 (for binary classifications) or general risk model construction of https://ssrn.com/abstract=2722093 (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings.
General Compound Hawkes Processes for Mid-Price Prediction
Authors: Myles Sjogren, Timothy DeLise
Date: 2021-10-13 00:00:00
Tasks: Prediction

High frequency financial data is burdened by a level of randomness that is unavoidable and obfuscates the task of modelling. This idea is reflected in the intraday evolution of limit orders book data for many financial assets and suggests several justifications for the use of stochastic models. For instance, the arbitrary distribution of inter arrival times and the subsequent dependence structure between consecutive book events. This has lead to the development of many stochastic models for the dynamics of limit order books. In this paper we look to examine the adaptability of one family of such models, the General Compound Hawkes Process (GCHP) models, to new data and new tasks. We further focus on the prediction problem for the mid-price within a limit order book and the practical applications of these stochastic models, which is the main contribution of this paper. To this end we examine the use of the GCHP for predicting the direction and volatility of futures and stock data and discuss possible extensions of the model to help improve its predictive capabilities.
Reason induced visual attention for explainable autonomous driving
Authors: Sikai Chen, Jiqian Dong, Runjia Du, Yujie Li, Samuel Labi
Date: 2021-10-11 00:00:00
Tasks: Autonomous Driving

Deep learning (DL) based computer vision (CV) models are generally considered as black boxes due to poor interpretability. This limitation impedes efficient diagnoses or predictions of system failure, thereby precluding the widespread deployment of DLCV models in safety-critical tasks such as autonomous driving. This study is motivated by the need to enhance the interpretability of DL model in autonomous driving and therefore proposes an explainable DL-based framework that generates textual descriptions of the driving environment and makes appropriate decisions based on the generated descriptions. The proposed framework imitates the learning process of human drivers by jointly modeling the visual input (images) and natural language, while using the language to induce the visual attention in the image. The results indicate strong explainability of autonomous driving decisions obtained by focusing on relevant features from visual inputs. Furthermore, the output attention maps enhance the interpretability of the model not only by providing meaningful explanation to the model behavior but also by identifying the weakness of and potential improvement directions for the model.
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Authors: Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan
Date: 2021-09-09 00:00:00
Proceeding: NAACL (NLPMC) 2021 6

In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard to obtain. We present an algorithm to create synthetic training data with an explicit focus on capturing medically relevant information. We utilize GPT-3 as the backbone of our algorithm and scale 210 human labeled examples to yield results comparable to using 6400 human labeled examples (~30x) leveraging low-shot learning and an ensemble method. In detailed experiments, we show that this approach produces high quality training data that can further be combined with human labeled data to get summaries that are strongly preferable to those produced by models trained on human data alone both in terms of medical accuracy and coherency.
Unsupervised Text Mining of COVID-19 Records
Authors: Mohamad Zamini
Date: 2021-09-08 00:00:00

Since the beginning of coronavirus, the disease has spread worldwide and drastically changed many aspects of the human's lifestyle. Twitter as a powerful tool can help researchers measure public health in response to COVID-19. According to the high volume of data production on social networks, automated text mining approaches can help search, read and summarize helpful information. This paper preprocessed the existing medical dataset regarding COVID-19 named CORD-19 and annotated the dataset for supervised classification tasks. At this time of the COVID-19 pandemic, we made a preprocessed dataset for the research community. This may contribute towards finding new solutions for some social interventions that COVID-19 has made. The preprocessed version of the mentioned dataset is publicly available through Github.
Memory-Based Semantic Parsing
Authors: Parag Jain, Mirella Lapata
Date: 2021-09-07 00:00:00
Tasks: Decoder, Semantic Parsing

We present a memory-based model for context-dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or modify the parse from the previous utterance, assuming there is a dependency between the current and previous parses. In this work, we propose to represent contextual information using an external memory. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances. We evaluate our approach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance without using task-specific decoders.
End-to-end Keyword Spotting using Xception-1d
Authors: Iván Vallés-Pérez, Juan Gómez-Sanchis, Marcelino Martínez-Sober, Joan Vila-Francés, Antonio J. Serrano-López, Emilio Soria-Olivas
Date: 2021-10-09 00:00:00
Tasks: Keyword Spotting

The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several computer vision tasks. We obtained about 96\% accuracy when classifying audio clips belonging to 35 different categories, beating human annotation at the most complex tasks proposed.
Music Playlist Title Generation: A Machine-Translation Approach
Authors: Seungheon Doh, Junwon Lee, Juhan Nam
Date: 2021-10-03 00:00:00
Proceeding: NLP4MusA 2021 11
Tasks: Data Augmentation, Machine Translation, Translation

We propose a machine-translation approach to automatically generate a playlist title from a set of music tracks. We take a sequence of track IDs as input and a sequence of words in a playlist title as output, adapting the sequence-to-sequence framework based on Recurrent Neural Network (RNN) and Transformer to the music data. Considering the orderless nature of music tracks in a playlist, we propose two techniques that remove the order of the input sequence. One is data augmentation by shuffling and the other is deleting the positional encoding. We also reorganize the existing music playlist datasets to generate phrase-level playlist titles. The result shows that the Transformer models generally outperform the RNN model. Also, removing the order of input sequence improves the performance further.
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks
Authors: Peng Kou, Deliang Liang, Chen Wang, Zihao Wu, Lin Gaoa
Date: 2020-04-15 00:00:00
Proceeding: Elsevier Applied Energy 2020 4
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

Reinforcement learning-based schemes are being recently applied for model-free voltage control in active distribution networks. However, existing reinforcement learning methods face challenges when it comes to continuous state and action spaces problems or problems with operation constraints. To address these limitations, this paper proposes an optimal voltage control scheme based on the safe deep reinforcement learning. In this scheme, the optimal voltage control problem is formulated as a constrained Markov decision process, in which both state and action spaces are continuous. To solve this problem efficiently, the deep deterministic policy gradient algorithm is utilized to learn the reactive power control policies, which determine the optimal control actions from the states. In contrast to existing reinforcement learning methods, deep deterministic policy gradient is naturally capable of addressing control problems with continuous state and action spaces. This is due to the utilization of deep neural networks to approximate both value function and policy. In addition, in order to handle the operation constraints in active distribution networks, a safe exploration approach is proposed to form a safety layer, which is composed directly on top the deep deterministic policy gradient actor network. This safety layer predicts the change in the constrained states and prevents the violation of active distribution networks operation constraints. Numerical simulations on modified IEEE test systems demonstrate that the proposed scheme successfully maintains all bus voltage within the allowed range, and reduces the system loss by 15% compared to the no control case.
STEM: An Approach to Multi-Source Domain Adaptation With Guarantees
Authors: Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, Dinh Phung
Date: 2021-10-01 00:00:00

Multi-source Domain Adaptation (MSDA) is more practical but challenging than the conventional unsupervised domain adaptation due to the involvement of diverse multiple data sources. Two fundamental challenges of MSDA are: (i) how to deal with the diversity in the multiple source domains and (ii) how to cope with the data shift between the target domain and the source domains. In this paper, to address the first challenge, we propose a theoretical-guaranteed approach to combine domain experts locally trained on its own source domain to achieve a combined multi-source teacher that globally predicts well on the mixture of source domains. To address the second challenge, we propose to bridge the gap between the target domain and the mixture of source domains in the latent space via a generator or feature extractor. Together with bridging the gap in the latent space, we train a student to mimic the predictions of the teacher expert on both source and target examples. In addition, our approach is guaranteed with rigorous theory offered insightful justifications of how each component influences the transferring performance. Extensive experiments conducted on three benchmark datasets show that our proposed method achieves state-of-the-art performances to the best of our knowledge.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
Authors: Bobo Xi, Jiaojiao Li, Yunsong Li, Qian Du
Date: 2021-10-12 00:00:00
Proceeding: IGARSS 2021 10
Tasks: Classification, Computational Efficiency, Graph Classification, Hyperspectral Image Classification, image-classification

Graph convolutional network (GCN) is one of the most favorable semi-supervised approaches, which demonstrates encouraging performance for hyperspectral image classification (HSIC), especially under the condition of small sample sizes. In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC. Different from prevenient GCN, we devise a prototypical layer comprising a distance-based cross-entropy (DCE) loss function and a novel temporal entropy-based regularizer (TER) in the frameworks of SSGPN. This effective layer can facilitate to generate more discriminative embedding features along with the representative prototypes to each class, so as to achieve accurate identification of various land-cover categories. Additionally, to promote computational efficiency, we present a graph normalization (G-Norm) to accelerate the convergence speed and boost the training procedure. Experimental results demonstrate that our proposed SSGPN can obtain promising performance compared with the state-of-the-art methods.
Emoji Prediction from Twitter Data using Deep Learning Approach
Authors: V.N.Durga Pavithra Kollipara, V.N.Hemanth Kollipara; M.Durga Prakash
Date: 2021-10-04 00:00:00
Proceeding: Asian Conference on Innovation in Technology (ASIANCON) 2021 10
Tasks: Deep Learning, Multi-class Classification, Prediction, Sentence, Text Classification

Emojis are a small visual representation of emotions or objects that are usually used in text messages to enhance the communication experience between individuals. With the rise in the widespread use of social media platforms like Twitter and instant messaging, many users are using these emojis in their text messages to convey broad feelings efficiently, which sometimes cannot be expressed using just words. This combination of text and emojis to improve emotion has become an essential part of how people communicate in the 21st century. Thus, giving rise to a problem statement that is to identify the relationship between these text messages and the emojis used in them. In this paper, we propose an approach to predict multiple emojis for a given text-based tweet message. Our proposal contains three modules, where the first module preprocesses the given text data, the second module is the model on which the data is trained, and a multi-class classifier to predict the emojis evoked by the given text. The objective of this model is to understand the underlying semantics of the text sentence using natural language processing techniques to predict reasonable emojis.
Dan: Deep attention neural network for news recommendation
Authors: Qiannan Zhu
Date: 2019-07-17 00:00:00
Proceeding: AAAI 2019 7
Tasks: Deep Attention, Diversity, News Recommendation

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing richer hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model.
Qualitative Comparison of Community Detection Algorithms
Authors: Günce Orman, Vincent Labatut, Hocine Cherifi
Date: 2012-07-16 00:00:00
Tasks: Community Detection

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis reveals they display only some of the properties observed in real-world community structures. We then apply five community detection algorithms on these networks and find out the performance assessed quantitatively does not necessarily agree with a qualitative analysis of the identified communities. It therefore seems both approaches should be applied to perform a relevant comparison of the algorithms.
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection
Authors: Belhassen Bayar, Matthew C. Stamm
Date: 2018-04-11 00:00:00
Proceeding: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018 4
Tasks: Image Manipulation, Image Manipulation Detection

Identifying the authenticity and processing history
of an image is an important task in multimedia forensics.
By analyzing traces left by different image manipulations, researchers have been able to develop several algorithms capable
of detecting targeted editing operations. While this approach has
led to the development of several successful forensic algorithms,
an important problem remains: creating forensic detectors for
different image manipulations is a difficult and time consuming
process. Furthermore, forensic analysts need ‘general purpose’
forensic algorithms capable of detecting multiple different image
manipulations. In this paper, we address both of these problems
by proposing a new general purpose forensic approach using
convolutional neural networks (CNNs). While CNNs are capable
of learning classification features directly from data, in their
existing form they tend to learn features representative of an
image’s content. To overcome this issue, we have developed
a new type of CNN layer, called a constrained convolutional
layer, that is able to jointly suppress an image’s content and
adaptively learn manipulation detection features. Through a
series of experiments, we show that our proposed constrained
CNN is able to learn manipulation detection features directly
from data. Our experimental results demonstrate that our CNN
can detect multiple different editing operations with up to 99.97%
accuracy and outperform the existing state-of-the-art general
purpose manipulation detector. Furthermore, our constrained
CNN can still accurately detect image manipulations in realistic
scenarios where there is a source camera model mismatch
between the training and testing data