pwc-archive/papers-with-abstracts huggingface dataset:
Detecting and Correcting IMU Movements During Joint Angle Estimation
Authors: Chunzhi Yi, Feng Jiang, Baichun Wei, Chifu Yang, Zhen Ding, Jubo Jin, Jie Liu
Date: 2021-06-09 00:00:00

Inertial measurement units (IMUs) increasingly function as a basic component of wearable sensor network (WSN)systems. IMU-based joint angle estimation (JAE) is a relatively typical usage of IMUs, with extensive applications. However, the issue that IMUs move with respect to their original placement during JAE is still a research gap, and limits the robustness of deploying the technique in real-world application scenarios. In this study, we propose to detect and correct the IMU movement online in a relatively computationally lightweight manner. Particularly, we first experimentally investigate the influence of IMU movements. Second, we design the metrics for detecting IMU movements by mathematically formulating how the IMU movement affects the IMU measurements. Third, we determine the optimal thresholds of metrics by synthetic IMU data from a significantly amended simulation model. Finally, a correction method is proposed to correct the effects of IMU movements. We demonstrate our method on both synthetic data and real-user data. The results demonstrate our method is a promising solution to detecting and correcting IMU movements during JAE.
Exponentially-Weighted Energy Dispersion Index for the Nonlinear Interference Analysis of Finite-Blocklength Shaping
Authors: Kaiquan Wu, Gabriele Liga, Yunus Can Gültekin, Alex Alvarado
Date: 2021-06-08 00:00:00

A metric called exponentially-weighted energy dispersion index (EEDI) is proposed to explain the blocklength-dependent effective signal-to-noise ratio (SNR) in probabilistically shaped fiber-optic systems. EEDI is better than energy dispersion index (EDI) at capturing the dependency of the effective SNR on the blocklength for long-distance transmission.
The SPL-IT Query by Example Search on Speech system for MediaEval 2014
Authors: Jorge Proença, Arlindo Veiga, Fernando Perdigão
Date: 2014-10-16 00:00:00
Tasks: Dynamic Time Warping, Keyword Spotting, Phoneme Recognition

This document briefly describes the system submitted by the Speech Processing Lab of Instituto de Telecomunicações, pole of  Coimbra (SPL-IT) to the Query by Example Search on Speech Task (QUESST) of MediaEval 2014. Our approach is based on merging results of a phoneme recognition system using three different languages. A version of Dynamic Time Warping (DTW) using posteriorgram distances was created to allow finding some of the peculiar search cases of this task. Our primary submission merges two approaches: simple DTW for detecting entire queries and a version where cutting final portions of queries is allowed. The late submission merges 5 approaches that account for all the search possibilities described for the task, though improved results were only observed in the evaluation dataset for type 3 queries.
General Rough Modeling of Cluster Analysis
Authors: A. Mani
Date: 2021-06-02 00:00:00
Tasks: Clustering

In this research, a general theoretical framework for clustering is proposed over specific partial algebraic systems by the present author. Her theory helps in isolating minimal assumptions necessary for different concepts of clustering information in any form to be realized in a situation (and therefore in a semantics). \emph{It is well-known that of the limited number of proofs in the theory of hard and soft clustering that are known to exist, most involve statistical assumptions}. Many methods seem to work because they seem to work in specific empirical practice. A new general rough method of analyzing clusterings is invented, and this opens the subject to clearer conceptions and contamination-free theoretical proofs. Numeric ideas of validation are also proposed to be replaced by those based on general rough approximation. The essence of the approach is explained in brief and supported by an example.
Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty
Authors: Giacomo Albi, Lorenzo Pareschi, Mattia Zanella
Date: 2021-06-09 00:00:00

After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a compartmental model with a social structure and stochastic inputs, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. Specific contact patterns in the home, work, school and other locations have been considered. Results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed.
The Origins of COVID-19
Authors: J. C. Phillips
Date: 2021-06-08 00:00:00

The titled subject has attracted much interest. Here we summarize the substantial results obtained by a physical model of protein evolution based on hydropathic domain dynamics. In a recent Letter eighteen biologists suggested that the titled subject should be studied in a way inclusive of broad expertise (1). There is an even broader view that has been developed over several decades by physicists (2,3). This view is based on analyzing amino acid sequences of proteins. These sequences are now available on-line at Uniprot, and represent a treasure-trove of data (4).
The whole prefrontal cortex is premotor cortex
Authors: Justin M. Fine, Benjamin Y. Hayden
Date: 2021-06-08 00:00:00

We propose that the entirety of the prefrontal cortex can be seen as fundamentally premotor in nature. By this, we mean that the prefrontal cortex consists of an action abstraction hierarchy whose core function is the potentiation and depotentiation of possible action plans at different levels of granularity. We argue that the apex of the hierarchy should revolve around the process of goal selection, which we posit is inherently a form of abstract action optimization. Anatomical and functional evidence supports the idea that this hierarchy originates on the orbital surface of the brain and extends dorsally to motor cortex. Our view, therefore, positions the orbitofrontal cortex as the central site for the optimization of goal selection policies, and suggests that other proposed roles are aspects of this more general function. We conclude by proposing that the dynamical systems approach, which works well in motor systems, can be extended to the rest of prefrontal cortex. Our proposed perspective will reframe outstanding questions, open up new areas of inquiry, and will align theories of prefrontal function with evolutionary principles.
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling
Authors: Benjamin Walter
Date: 2021-05-31 00:00:00
Tasks: image-classification, Image Classification

Image classification is considered, and a hierarchical max-pooling model with additional local pooling is introduced. Here the additional local pooling enables the hierachical model to combine parts of the image which have a variable relative distance towards each other. Various convolutional neural network image classifiers are introduced and compared in view of their rate of convergence. The finite sample size performance of the estimates is analyzed by applying them to simulated and real data.
On the Explanation of Similarity for Developing and Deploying CBR Systems
Authors: Kerstin Bach, Paul Jarle Mork
Date: 2021-05-09 00:00:00

During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires transferring implicit knowledge of domain experts into knowledge representations. While an entire CBR system is very explanatory, the similarity measure determines the ranking but do not necessarily show which features contribute to high (or low) rankings. In this paper we present our work on opening the knowledge engineering process for similarity modelling. This work present is a result of an interdisciplinary research collaboration between AI and public health researchers developing e-Health applications. During this work explainability and transparency of the development process is crucial to allow in-depth quality assurance of the by the domain experts.
Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection
Authors: Djamila Romaissa Beddiar, Md Saroar Jahan, Mourad Oussalah
Date: 2021-05-25 00:00:00
Tasks: Data Augmentation, Decoder, Deep Learning, Hate Speech Detection, Machine Translation, Mixture-of-Experts, Translation

With proliferation of user generated contents in social media platforms, establishing mechanisms to automatically identify toxic and abusive content becomes a prime concern for regulators, researchers, and society. Keeping the balance between freedom of speech and respecting each other dignity is a major concern of social media platform regulators. Although, automatic detection of offensive content using deep learning approaches seems to provide encouraging results, training deep learning-based models requires large amounts of high-quality labeled data, which is often missing. In this regard, we present in this paper a new deep learning-based method that fuses a Back Translation method, and a Paraphrasing technique for data augmentation. Our pipeline investigates different word-embedding-based architectures for classification of hate speech. The back translation technique relies on an encoder-decoder architecture pre-trained on a large corpus and mostly used for machine translation. In addition, paraphrasing exploits the transformer model and the mixture of experts to generate diverse paraphrases. Finally, LSTM, and CNN are compared to seek enhanced classification results. We evaluate our proposal on five publicly available datasets; namely, AskFm corpus, Formspring dataset, Warner and Waseem dataset, Olid, and Wikipedia toxic comments dataset. The performance of the proposal together with comparison to some related state-of-art results demonstrate the effectiveness and soundness of our proposal.
IIIT-H System for MediaEval 2014 QUESST
Authors: Santosh Kesiraju, Gautam Mantena, Kishore Prahallad
Date: 2014-10-16 00:00:00
Tasks: Dynamic Time Warping, Keyword Spotting

This paper describes the experiments and observations for Query-by-Example Search on Speech Task (QUESST) at MediaEval 2014. In this paper, we describe two different representations of speech that were explored for the task. We also show the capabilities and limitations of non-segmental dynamic time warping (NS-DTW) technique for searching various types of queries. This paper mainly focuses on the experiments and analysis of the existing NS-DTW algorithm for various types of queries. The observations show that for a specific representation of speech, the algorithm is capable of detecting partial matches
TUKE System for MediaEval 2014 QUESST
Authors: Jozef Vavrek, Peter Viszlay, Martin Lojka, Matúš Pleva, Jozef Juhár
Date: 2014-10-16 00:00:00
Tasks: Clustering, Keyword Spotting

Two approaches to QbE (Query-by-Example) retrieving system, proposed by the Technical University of Kosice (TUKE)for the query by example search on speech task (QUESST), are presented in this paper. Our main interest was focused on building such QbE system, which is able to retrieve all given queries with and without using any external speech resources. Therefore we developed posteriorgram-based keyword matching system, which utilizes a novel weighted fast sequential variant of DTW (WFS-DTW) algorithm in order to detect occurrences of each query within the particular utterance file, using two GMM-based acoustic units modeling approaches. The first one, referred as low-resource approach, employs language-dependent phonetic decoders to convert queries and utterances into posteriorgrams. The second one, defined as zero-resource approach, implements combination of unsupervised segmentation and clustering techniques by using only provided utterance files.
GTTS-EHU Systems for QUESST at MediaEval 2014
Authors: Luis J. Rodriguez-Fuentes, Amparo Varona, Mikel Penagarikano, Germán Bordel, Mireia Diez
Date: 2014-10-16 00:00:00
Tasks: Action Detection, Activity Detection, Keyword Spotting

This paper briefly describes the systems presented by the Software Technologies Working Group (http://gtts.ehu.es, GTTS) of the University of the Basque Country (UPV/EHU) to the Query-by-Example Search on Speech Task (QUESST) at MediaEval 2014. The GTTS-EHU systems consist of four modules: (1) feature extraction; (2) speech activity detection; (3) DTW-based query matching; and (4) score calibration and fusion. The submitted systems follow the same approach used in our SWS 2013 submissions, with two minor changes (needed to address the new task): the search stops at the most likely query detection (no further detections are looked for) and a score is produced for each (query, document) pair. The two approximate matching types introduced in QUESST have not received special treatment. This year, we have just explored the use of reduced feature sets, obtaining worse results but at lower computational costs.
Salient Positions based Attention Network for Image Classification
Authors: Sheng Fang, Kaiyu Li, Zhe Li
Date: 2021-06-09 00:00:00
Tasks: Classification, image-classification, Image Classification

The self-attention mechanism has attracted wide publicity for its most important advantage of modeling long dependency, and its variations in computer vision tasks, the non-local block tries to model the global dependency of the input feature maps. Gathering global contextual information will inevitably need a tremendous amount of memory and computing resources, which has been extensively studied in the past several years. However, there is a further problem with the self-attention scheme: is all information gathered from the global scope helpful for the contextual modelling? To our knowledge, few studies have focused on the problem. Aimed at both questions this paper proposes the salient positions-based attention scheme SPANet, which is inspired by some interesting observations on the attention maps and affinity matrices generated in self-attention scheme. We believe these observations are beneficial for better understanding of the self-attention. SPANet uses the salient positions selection algorithm to select only a limited amount of salient points to attend in the attention map computing. This approach will not only spare a lot of memory and computing resources, but also try to distill the positive information from the transformation of the input feature maps. In the implementation, considering the feature maps with channel high dimensions, which are completely different from the general visual image, we take the squared power of the feature maps along the channel dimension as the saliency metric of the positions. In general, different from the non-local block method, SPANet models the contextual information using only the selected positions instead of all, along the channel dimension instead of space dimension. Our source code is available at https://github.com/likyoo/SPANet.
Temporally Sorting Images from Real-World Events
Authors: Rafael Padilha, Fernanda A. Andaló, Bahram Lavi, Luís A.M. Pereira, Anderson Rocha
Date: 2021-07-01 00:00:00
Tasks: Image Classification

As smartphones become ubiquitous in modern life, every major event — from musical concerts to terrorist attempts — is massively captured by multiple devices and instantly uploaded to the Internet. Once shared through social media, the chronological order between available media pieces cannot be reliably recovered, hindering the understanding and reconstruction of that event. In this work, we propose data-driven methods for temporally sorting images originated from heterogeneous sources and captured from distinct angles, viewpoints, and moments. We model the chronological sorting task as an ensemble of binary classifiers whose answers are combined hierarchically to estimate an image’s temporal position within the duration of the event. We evaluate our method on images from the Notre-Dame Catedral fire and the Grenfell Tower fire events and discuss research challenges for analyzing data from real-world forensic events. Finally, we employ visualization techniques to understand what our models have learned, offering additional insights to the problem.
Newly Synthesized Elements and Pristine Dust in the Cassiopeia A Supernova Remnant
Authors: R. G. Arendt, E. Dwek, S. H. Moseley
Date: 1999-01-05 00:00:00

Spectroscopic observations at 2.4 - 45 microns of the young supernova remnant Cas A with the Infrared Space Observatory (ISO) Short Wavelength Spectrometer (SWS) reveal strong emission lines of O, Ne, Si, S, and Ar. These lines are observed at high velocities (several thousand km/s), and are therefore associated with the supernova ejecta known as the fast-moving knots (FMKs). Continuum emission from dust is also seen in the Cas A spectrum. The continuum strength is spatially well correlated with the O and Ar line strengths, indicating that the dust emission also arises from the FMKs. The dust continuum has an emission feature at ~22 microns which cannot be fit by typical astronomical silicates, but can be fit with a particular class of silicate minerals. This suggests that the dust in Cas A is silicate material that has freshly condensed from the Cas A ejecta into a mineral form that is uncharacteristic of typical ISM dust grains.
Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise
Authors: Jiajia Li, Peihua Feng, Liang Zhao, Junying Chen, Mengmeng Du, Yangyang Yu, Jian Song, Ying Wu
Date: 2021-05-26 00:00:00

Epilepsy is a neurological disorder with recurrent seizures of complexity and randomness. Until now, the mechanism of epileptic randomness has not been fully elucidated. Inspired by the recent finding that astrocyte GTPase-activating protein (G-protein)-coupled receptors could be involved in stochastic epileptic seizures, we proposed a neuron-astrocyte network model, incorporating the noise of the astrocytic second messager, inositol triphosphate (IP3) which is modulated by the G-protein)-coupled receptor activation. Based on this model, we have statistically analysed the transitions of epileptic seizures by performing tens of simulation trials. Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency. Meanwhile, a bistable state of neuronal firing emerges under certain noise intensity, during which the neuronal firing pattern switches between regular sparse spiking and epileptic seizure states. This random presence of epileptic seizures is absent when the noise intensity continues to increase, accompanying with an increase in the epileptic depolarization block duration. The simulation results also shed light on the fact that calcium signals in astrocytes play significant roles in the pattern formations of the epileptic seizure. Our results provide a potential pathway for understanding the epileptic randomness.
Protein design by multiobjective optimization: evolutionary and non-evolutionary approaches
Authors: 01 July 2017Sandeep V. Belure, Ofer M. Shir, Vikas Nanda
Date: 2017-07-01 00:00:00
Tasks: Combinatorial Optimization, Efficient Exploration, Multiobjective Optimization, Protein Design, Specificity

Traditional simulation-based protein design considers energy minimization of candidate conformations as a singleobjective combinatorial optimization problem. In this paper we consider a challenging protein design problem, producing twelve protein species based on collagen that uniquely assort into four groups of three: a problem defined herein as a 4-level heterotrimer. We formulate a bi-objective combinatorial minimization problem that targets both stability and specificity of the 4-level heterotrimer. In order to approximate its Pareto frontier, we utilize both evolutionary and non-evolutionary approaches, operating in either Pareto or aggregation fashions. Our practical observations suggest that the SMS-EMOA with Evolution Strategies' operators is more effective than standard heuristics deployed in computational protein design, such as Simulated Annealing, Replica Exchange or the Canonical Genetic Algorithm. We investigate the attained Pareto optimal sets using Barrier Tree analysis, aiming to provide insights into the chemical search-space, as well as to explain the observed algorithmic trends. In particular, we identify Replica Exchange as a promising non-evolutionary technique for this problem class, due to its efficient exploration capabilities. Overall, a common high-level protocol for simultaneous landscape analysis of evolutionary and non-evolutionary search methodologies is put forward for the first time.
Automatic Construction of Context-Aware Sentiment Lexicon in the Financial Domain Using Direction-Dependent Words
Authors: JiHye Park, Hye Jin Lee, Sungzoon Cho
Date: 2021-06-10 00:00:00
Tasks: Sentiment Analysis

Increasing attention has been drawn to the sentiment analysis of financial documents. The most popular examples of such documents include analyst reports and economic news, the analysis of which is frequently used to capture the trends in market sentiments. On the other hand, the significance of the role sentiment analysis plays in the financial domain has given rise to the efforts to construct a financial domain-specific sentiment lexicon. Sentiment lexicons lend a hand for solving various text mining tasks, such as unsupervised classification of text data, while alleviating the arduous human labor required for manual labeling. One of the challenges in the construction of an effective sentiment lexicon is that the semantic orientation of a word may change depending on the context in which it appears. For instance, the word ``profit" usually conveys positive sentiments; however, when the word is juxtaposed with another word ``decrease," the sentiment associated with the phrase ``profit decreases" now becomes negative. Hence, the sentiment of a given word may shift as one begins to consider the context surrounding the word. In this paper, we address this issue by incorporating context when building sentiment lexicon from a given corpus. Specifically, we construct a lexicon named Senti-DD for the Sentiment lexicon composed of Direction-Dependent words, which expresses each term a pair of a directional word and a direction-dependent word. Experiment results show that higher classification performance is achieved with Senti-DD, proving the effectiveness of our method for automatically constructing a context-aware sentiment lexicon in the financial domain.