pwc-archive/papers-with-abstracts huggingface dataset:
The leaky integrator that could: Or recursive polynomial regression for online signal analysis
Authors: Hugh L Kennedy
Date: 2022-06-09 00:00:00
Tasks: Change Detection, Edge Detection, regression

Fitting a local polynomial model to a noisy sequence of uniformly sampled observations or measurements (i.e. regressing) by minimizing the sum of weighted squared errors (i.e. residuals) may be used to design digital filters for a diverse range of signal-analysis problems, such as detection, classification and tracking, in biomedical, financial, and aerospace applications, for instance. Furthermore, the recursive realization of such filters, using a network of so-called leaky integrators, yields simple digital components with a low computational complexity and an infinite impulse response (IIR) that are ideal in embedded online sensing systems with high data rates. Target tracking, pulse-edge detection, peak detection and anomaly/change detection are considered in this tutorial as illustrative examples. Erlang-weighted polynomial regression provides a design framework within which the various design trade-offs of state estimators (e.g. bias errors vs. random errors) and IIR smoothers (e.g. frequency isolation vs. time localization) may be intuitively balanced. Erlang weights are configured using a smoothing parameter which determines the decay rate of the exponential tail; and a shape parameter which may be used to discount more recent data, so that a greater relative emphasis is placed on a past time interval. In Morrison's 1969 treatise on sequential smoothing and prediction, the exponential weight (i.e. the zero shape-parameter case) and the Laguerre polynomials that are orthogonal with respect to this weight, are described in detail; however, more general Erlang weights and the resulting associated Laguerre polynomials are not considered there, nor have they been covered in detail elsewhere since. Thus, one of the purposes of this tutorial is to explain how Erlang weights may be used to shape and improve the response of recursive regression filters.
Manifold Graph Signal Restoration using Gradient Graph Laplacian Regularizer
Authors: Fei Chen, Gene Cheung, Xue Zhang
Date: 2022-06-09 00:00:00
Tasks: Graph Embedding

In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an underlying graph. However, for signals slowly varying across graph kernels, GLR suffers from an undesirable "staircase" effect. In this paper, focusing on manifold graphs -- collections of uniform discrete samples on low-dimensional continuous manifolds -- we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction. Specifically, for a graph endowed with sampling coordinates (e.g., 2D images, 3D point clouds), we first define a gradient operator, using which we construct a gradient graph for nodes' gradients in sampling manifold space. This maps to a gradient-induced nodal graph (GNG) and a positive semi-definite (PSD) Laplacian matrix with planar signals as the 0 frequencies. For manifold graphs without explicit sampling coordinates, we propose a graph embedding method to obtain node coordinates via fast eigenvector computation. We derive the means-square-error minimizing weight parameter for GGLR efficiently, trading off bias and variance of the signal estimate. Experimental results show that GGLR outperformed previous graph signal priors like GLR and graph total variation (GTV) in a range of graph signal restoration tasks.
N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification
Authors: A. Ali Heydari, Oscar A. Davalos, Katrina K. Hoyer, Suzanne S. Sindi
Date: 2022-05-08 00:00:00

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods-especially deep learning models-have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural-attention to detect salient genes for use in cell-type identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.
In search for an alternative to the computer metaphor of the mind and brain
Authors: Damian G. Kelty-Stephen, Paul E. Cisek, Benjamin De Bari, James Dixon, Luis H. Favela, Fred Hasselman, Fred Keijzer, Vicente Raja, Jeffrey B. Wagman, Brandon J. Thomas, Madhur Mangalam
Date: 2022-06-09 00:00:00
Tasks: Diversity, Philosophy

The brain-as-computer metaphor has anchored the professed computational nature of the mind, wresting it down from the intangible logic of Platonic philosophy to a material basis for empirical science. However, as with many long-lasting metaphors in science, the computer metaphor has been explored and stretched long enough to reveal its boundaries. These boundaries highlight widening gaps in our understanding of the brain's role in an organism's goal-directed, intelligent behaviors and thoughts. In search of a more appropriate metaphor that reflects the potentially noncomputable functions of mind and brain, eight author groups answer the following questions: (1) What do we understand by the computer metaphor of the brain and cognition? (2) What are some of the limitations of this computer metaphor? (3) What metaphor should replace the computational metaphor? (4) What findings support alternative metaphors? Despite agreeing about feeling the strain of the strictures of computer metaphors, the authors suggest an exciting diversity of possible metaphoric options for future research into the mind and brain.
Tropical Density Estimation of Phylogenetic Trees
Authors: Ruriko Yoshida, David Barnhill, Keiji Miura, Daniel Howe
Date: 2022-06-09 00:00:00
Tasks: Density Estimation

Much evidence from biological theory and empirical data indicates that, gene tree, phylogenetic trees reconstructed from different genes (loci), do not have to have exactly the same tree topologies. Such incongruence between gene trees might be caused by some ``unusual'' evolutionary events, such as meiotic sexual recombination in eukaryotes or horizontal transfers of genetic material in prokaryotes. However, most of gene trees are constrained by the tree topology of its species tree, that is, the phylogenetic tree of a given species following their evolutionary history. In order to discover ``outlying'' gene trees which do not follow the ``main distribution(s)'' of trees, we propose to apply the ``tropical metric'' with the max-plus algebra from tropical geometry to a non-parametric estimation of gene trees over the space of phylogenetic trees. In this research we apply the ``tropical metric,'' a well-defined metric over the space of phylogenetic trees under the max-plus algebra, to non-parametric estimation of gene trees distribution over the tree space. Kernel density estimator (KDE) is one of the most popular non-parametric estimation of a distribution from a given sample, and we propose an analogue of the classical KDE in the setting of tropical geometry with the tropical metric which measures the length of an intrinsic geodesic between trees over the tree space. We estimate the probability of an observed tree by empirical frequencies of nearby trees, with the level of influence determined by the tropical metric. Then, with simulated data generated from the multispecies coalescent model, we show that the non-parametric estimation of gene tree distribution using the tropical metric performs better than one using the Billera-Holmes-Vogtmann (BHV) metric developed by Weyenberg et al. in terms of computational times and accuracy. We then apply it to Apicomplexa data.
Characterization of Blast Waveforms Produced by Different Driver Gasses in an Open-Ended Shock Tube Model
Authors: Evan L. Reeder, Mei Ling Liber, Owen D. Traubert, Christopher J. O Connell, Ryan C. Turner, Matthew J. Robson
Date: 2022-06-07 00:00:00

With the evolution of modern warfare and the increased use of improvised explosive devices (IEDs), there has been an increase in blast-induced traumatic brain injuries (bTBI) among military personnel and civilians. The increased prevalence of bTBI necessitates bTBI models that result in a properly scaled injury for the model organism being used. The primary laboratory model for bTBI is the shock tube, wherein a compressed gas ruptures a thin membrane, generating a shockwave. To generate a shock wave that is properly scaled from human to rodent subjects the shock wave must have a short duration and high peak overpressure while fitting a Friedlander waveform, the ideal representation of a blast wave. A large variety of factors have been experimentally characterized in attempts to create an ideal waveform, however we found current research on the gas composition being used to drive shock wave formation to be lacking. To better understand the effect the driver gas has on the waveform being produced, we utilized a previously established murine shock tube bTBI model in conjunction with several distinct driver gasses. In agreement with previous findings, helium produced a shock wave most closely fitting the Friedlander waveform in contrast to the plateau-like waveforms produced by some other gases. The peak pressure at the exit of the shock tube and 5 cm from the exit have a strong negative correlation with the density of the gas being used: helium the least dense gas used produces the highest peak overpressure. Density of the driver gas also exerts a strong positive effect on the duration of the shock wave, with helium producing the shortest duration wave. Due to its ability to produce a Friedlander waveform and produce a waveform following proper injury scaling guidelines, helium is an ideal gas for use in shock tube models for bTBI.
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Authors: Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber
Date: 2010-03-01 00:00:00
Tasks: Handwritten Digit Recognition

Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images, and graphics cards to greatly speed up learning.
Deep Learning for Musical Form: Recognition and Analysis
Authors: Daniel Szelogowski
Date: 2022-04-29 00:00:00
Proceeding: Master Thesis 2022 4
Tasks: Classification, Deep Learning, Form, Genre classification, Segmentation

Musical form analysis is a rigorous task that frequently challenges the expertise of human analysts and signal processing algorithms alike. While numerous systems have been proposed to perform the tasks of musical segmentation, genre classification, and single-label segment classification in popular music, none have specifically focused on the analytical process used by classical musicians. Classical music form analysis facilitates a combination of these tasks, including form classification, structural segmentation, and multilabel large- and small-segment classification – tasks that lack feasible algorithms, machine learning models, and extensive research. Form analysis has many applications in the world of music, and a viable analytical system would greatly benefit performing musicians and academic researchers, both in musicology and signal processing. As well, current datasets used for related research tasks lack standardized analytical conventions, including form classification, and suffer from erroneous annotations and extensibility due to the data sources used for the music. In this thesis, we propose a new system to perform the task of automatic musical form analysis using deep learning models, as well as a new standardized dataset.
Graph Compression: Toward a Generalized Algorithm
Authors: Daniel Szelogowski
Date: 2022-05-09 00:00:00
Proceeding: Preprint 2022 5

Currently, most graph compression algorithms focus on in-memory compression (such as for web graphs) – few are feasible for external compression, and there is no generalized approach to either task. These compressed representations are versatile and can be applied to a great number of different applications, with the most common being social network and search systems. We present a new set of compression approaches, both lossless and lossy, for external memory graph compression. These new algorithms may also be applicable for runtime usage (i.e., running graph algorithms on the compressed representation).
Lock-Free Data-Structure Iterators
Authors: Erez Petrank, Shahar Timnat
Date: 2013-10-18 00:00:00
Proceeding: Distributed Computing 2013 10

Concurrent data structures are often used with large concurrent software. An iterator that traverses the data structure items is a highly desirable interface that often exists for sequential data structures but is missing from (almost all) concurrent data-structure implementations. In this paper we introduce a technique for adding a linearizable wait-free iterator to a wait-free or a lock-free data structure that implements a set. We use this technique to implement an iterator for the wait-free and lock-free linked-lists and for the lock-free skip-list.
Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms
Authors: Bahrami, Mahsa ; Forouzanfar, Mohamad
Date: 2022-02-15 00:00:00
Proceeding: IEEE Transactions on Instrumentation and Measurement 2022 2
Tasks: Sleep apnea detection

https://ieeexplore.ieee.org/abstract/document/9714370
Novelty Detection: A Perspective from Natural Language Processing
Authors: Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, Pushpak Bhattacharyya
Date: 2022-03-01 00:00:00
Proceeding: CL (ACL) 2022 3
Tasks: Natural Language Inference, Novelty Detection

The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of redundancy. A considerable portion of the Web contents are duplicates, and we need efficient mechanisms to retain new information and filter out redundant information. However, detecting redundancy at the semantic level and identifying novel text is not straightforward because the text may have less lexical overlap yet convey the same information. On top of that, non-novel/redundant information in a document may have assimilated from multiple source documents, not just one. The problem surmounts when the subject of the discourse is documents, and numerous prior documents need to be processed to ascertain the novelty/non-novelty of the current one in concern. In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem. We explore the role of pre-trained Textual Entailment (TE) models to deal with multiple source contexts and present the outcome of our current investigations. We argue that a multipremise entailment task is one close approximation toward identifying semantic-level non-novelty. Our recent approach either performs comparably or achieves significant improvement over the latest reported results on several datasets and across several related tasks (paraphrasing, plagiarism, rewrite). We critically analyze our performance with respect to the existing state of the art and show the superiority and promise of our approach for future investigations. We also present our enhanced dataset TAP-DLND 2.0 and several baselines to the community for further research on document-level novelty detection.
Improved N-Best Extraction with an Evaluation on Language Data
Authors: Johanna Björklund, Frank Drewes, Anna Jonsson
Date: 2022-03-01 00:00:00
Proceeding: CL (ACL) 2022 3

We show that a previously proposed algorithm for the N-best trees problem can be made more efficient by changing how it arranges and explores the search space. Given an integer N and a weighted tree automaton (wta) M over the tropical semiring, the algorithm computes N trees of minimal weight with respect to M. Compared with the original algorithm, the modifications increase the laziness of the evaluation strategy, which makes the new algorithm asymptotically more efficient than its predecessor. The algorithm is implemented in the software Betty, and compared to the state-of-the-art algorithm for extracting the N best runs, implemented in the software toolkit Tiburon. The data sets used in the experiments are wtas resulting from real-world natural language processing tasks, as well as artificially created wtas with varying degrees of nondeterminism. We find that Betty outperforms Tiburon on all tested data sets with respect to running time, while Tiburon seems to be the more memory-efficient choice.
Linguistic Parameters of Spontaneous Speech for Identifying Mild Cognitive Impairment and Alzheimer Disease
Authors: Veronika Vincze, Martina Katalin Szabó, Ildikó Hoffmann, László Tóth, Magdolna Pákáski, János Kálmán, Gábor Gosztolya
Date: 2022-03-01 00:00:00
Proceeding: CL (ACL) 2022 3

In this article, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic, and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients, and mAD patients, we carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the article, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we analyze the efficacy of each feature group separately. Our model that uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68%–70%, 2-class accuracy values: 77.3%–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.
Automatic Extraction of Nested Entities in Clinical Referrals in Spanish
Authors: Pablo Báez, Felipe Bravo-Marquez, Jocelyn Dunstan, Matías Rojas, Fabián Villena
Date: 2022-04-07 00:00:00
Proceeding: ACM Transactions on Computing for Healthcare 2022 4
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Word Embeddings

Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community.
Robust deep learning based protein sequence design using ProteinMPNN
Authors: J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
Date: 2022-06-04 00:00:00
Proceeding: bioRxiv 2022 6
Tasks: Deep Learning, Drug Discovery, Protein Design, Protein Folding, Protein Function Prediction, Protein Structure Prediction

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins.
Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
Authors: Ran Cao; Leyuan Fang; Ting Lu; Nanjun He
Date: 2022-05-30 00:00:00
Proceeding: IEEE Geoscience and Remote Sensing Letters ( Volume: 18 ) 2022 5
Tasks: Classification, Remote Sensing Image Classification, Scene Classification

Remote sensing scene classification aims to assign automatically each aerial image a specific sematic label. In this letter, we propose a new method, called self-attention-based deep feature fusion (SAFF), to aggregate deep layer features and emphasize the weights of the complex objects of remote sensing scene images for remote sensing scene classification. First, the pretrained convolutional neural network (CNN) model is applied to extract the abstract multilayer feature maps from the original aerial imagery. Then, a nonparametric self-attention layer is proposed for spatial-wise and channel-wise weightings, which enhances the effects of the spatial responses of the representative objects and uses the infrequently occurring features more sufficiently. Thus, it can extract more discriminative features. Finally, the aggregated features are fed into a support vector machine (SVM) for classification. The proposed method is experimented on several data sets, and the results prove the effectiveness and efficiency of the scheme for remote sensing scene classification.