pwc-archive/papers-with-abstracts huggingface dataset:
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
Authors: Joshua Pan, Jason J. Kwon, Jessica A. Talamas, Ashir A. Borah, Francisca Vazquez, Jesse S. Boehm, Aviad Tsherniak, Marinka Zitnik, James M. McFarland, William C. Hahn
Date: 2021-11-11 00:00:00
Tasks: Dictionary Learning

In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach ('Webster') recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and learned aspects of the cellular hierarchy in an unsupervised manner. Modeling compound sensitivity profiles in terms of genetically defined functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.
Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review
Authors: Abbas Salami, Javier Andreu-Perez, Helge Gillmeister
Date: 2021-11-11 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG), ERP

Depersonalisation/derealisation disorder (DPD) refers to frequent and persistent detachment from bodily self and disengagement from the outside world. As a dissociative disorder, DPD affects 1-2% of the population, but takes 7-12 years on average to be accurately diagnosed. In this systematic review, we comprehensively describe research targeting the neural correlates of core DPD symptoms, covering publications between 1992 and 2020 that have used electrophysiological techniques. The aim was to investigate the diagnostic potential of these relatively inexpensive and convenient neuroimaging tools. We review the EEG power spectrum, components of the event-related potential (ERP), as well as vestibular and heartbeat evoked potentials as likely electrophysiological biomarkers to study DPD symptoms. We argue that acute anxiety- or trauma-related impairments in the integration of interoceptive and exteroceptive signals play a key role in the formation of DPD symptoms, and that future research needs analysis methods that can take this integration into account. We suggest tools for prospective studies of electrophysiological DPD biomarkers, which are urgently needed to fully develop their diagnostic potential.
Functional Connectivity of the Brain Across Rodents and Humans
Authors: Nan Xu, Theodore J. LaGrow, Nmachi Anumba, Azalea Lee, Xiaodi Zhang, Behnaz Yousefi, Yasmine Bassil, Gloria Perrin Clavijo, Vahid Khalilzad Sharghi, Eric Maltbie, Lisa Meyer-Baese, Maysam Nezafati, Wen-Ju Pan, Shella Keilholz
Date: 2021-11-10 00:00:00
Tasks: Functional Connectivity

Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
An ensemble approach to the structure-function problem in microbial communities
Authors: Chandana Gopalakrishnappa, Karna Gowda, Kaumudi Prabhakara, Seppe Kuehn
Date: 2021-11-11 00:00:00

The metabolic activity of microbes has played an essential role in the evolution and persistence of life on Earth. Microbial metabolism plays a primary role in the flow of carbon, nitrogen and other elements through the biosphere on a global scale. Microbes perform these metabolic activities in the context of complex communities comprised of many species that interact in dynamic and spatially-structured environments. Molecular genetics has revealed many of the metabolic pathways microbes utilize to generate energy and biomass. However, most of this knowledge is derived from model organisms, so we have a limited view of role of the massive genomic diversity in the wild on metabolic phenotypes. Further, we are only beginning to glimpse the principles governing how the metabolism of a community emerges from the collective action of its constituent members. As a result, one of the biggest challenges in the field is to understand how the metabolic activity of a community emerges from the genomic structure of the constituents. Here we propose an approach to this problem that rests on the quantitative analysis of metabolic activity in ensembles of microbial communities. We propose quantifying metabolic fluxes in diverse communities, either in the laboratory or the wild. We suggest that using sequencing data to quantify the genomic, taxonomic or transcriptional variation across an ensemble of communities can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited and then review the analytical techniques available for quantifying metabolite dynamics in communities. Finally, we discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.
The space of equidistant phylogenetic cactuses
Authors: Katharina T. Huber, Vincent Moulton, Megan Owen, Andreas Spillner, Katherine St. John
Date: 2021-11-11 00:00:00
Tasks: ARC

We introduce and investigate the space of \emph{equidistant} $X$-\emph{cactuses}. These are rooted, arc weighted, phylogenetic networks with leaf set $X$, where $X$ is a finite set of species, and all leaves have the same distance from the root. The space contains as a subset the space of ultrametric trees on $X$ that was introduced by Gavryushkin and Drummond. We show that equidistant-cactus space is a CAT(0)-metric space which implies, for example, that there are unique geodesic paths between points. As a key step to proving this, we present a combinatorial result concerning \emph{ranked} rooted $X$-cactuses. In particular, we show that such networks can be encoded in terms of a pairwise compatibility condition arising from a poset of collections of pairs of subsets of $X$ that satisfy certain set-theoretic properties. As a corollary, we also obtain an encoding of ranked, rooted $X$-trees in terms of partitions of $X$, which provides an alternative proof that the space of ultrametric trees on $X$ is CAT(0). As with spaces of phylogenetic trees, we expect that our results should provide the basis for and new directions in performing statistical analyses for collections of phylogenetic networks with arc lengths.
A new radiomics feature: image frequency analysis
Authors: Takuma Usuzaki, Kengo Takahash, Kazuma Umemiya
Date: 2021-11-10 00:00:00
Tasks: Relation

Radiomics is a promising technology that focuses on improvements of image analysis, using an automated high-throughput extraction of quantitative features. However, the character of lesion is affected by the surrounding tissue. A lesion on medical image should be characterized from the inter-relation between lesion and surrounding tissue as well as property of the lesion itself. The aim of this study is to introduce a new radiomics feature which quantitatively analyze the inter-relation between lesion and surrounding tissue focusing on the value change of rows and columns in a medical image.
Correlation Estimation in Hybrid Systems
Authors: Baron Law
Date: 2021-11-11 00:00:00

A simple method is proposed to estimate the instantaneous correlations between state variables in a hybrid system from the empirical correlations between observable market quantities such as spot rate, stock price and implied volatility. The new algorithm is extremely fast since only low-dimension linear systems are involved. If the resulting matrix from the linear systems is not positive semidefinite, the shrinking method, which requires only bisection-style iterations, is recommended to convert the matrix to positive semidefinite. The square of short-term at-the-money implied volatility is suggested as the proxy for the unobservable stochastic variance. When the implied volatility is not available, a simple trick is provided to fill in the missing correlations. Numerical study shows that the estimates are reasonably accurate, when using more than 1,000 data points. In addition, the algorithm is robust to misspecified interest rate model parameters and the short-sampling-period assumption. G2++ and Heston are used for illustration but the method can be extended to other affine term structure, local volatility and jump diffusion models, with or without stochastic interest rate.
Learning via Long Short-Term Memory (LSTM) network for predicting strains in Railway Bridge members under train induced vibration
Authors: Amartya Dutta, Kamaljyoti Nath
Date: 2021-11-08 00:00:00

Bridge health monitoring using machine learning tools has become an efficient and cost-effective approach in recent times. In the present study, strains in railway bridge member, available from a previous study conducted by IIT Guwahati has been utilized. These strain data were collected from an existing bridge while trains were passing over the bridge. LSTM is used to train the network and to predict strains in different members of the railway bridge. Actual field data has been used for the purpose of predicting strain in different members using strain data from a single member, yet it has been observed that they are quite agreeable to those of ground truth values. This is in spite of the fact that a lot of noise existed in the data, thus showing the efficacy of LSTM in training and predicting even from noisy field data. This may easily open up the possibility of collecting data from the bridge with a much lesser number of sensors and predicting the strain data in other members through LSTM network.
Discrimination in Online Ad Delivery
Authors: Latanya Sweeney
Date: 2013-01-29 00:00:00

A Google search for a person's name, such as "Trevon Jones", may yield a personalized ad for public records about Trevon that may be neutral, such as "Looking for Trevon Jones?", or may be suggestive of an arrest record, such as "Trevon Jones, Arrested?". This writing investigates the delivery of these kinds of ads by Google AdSense using a sample of racially associated names and finds statistically significant discrimination in ad delivery based on searches of 2184 racially associated personal names across two websites. First names, assigned at birth to more black or white babies, are found predictive of race (88% black, 96% white), and those assigned primarily to black babies, such as DeShawn, Darnell and Jermaine, generated ads suggestive of an arrest in 81 to 86 percent of name searches on one website and 92 to 95 percent on the other, while those assigned at birth primarily to whites, such as Geoffrey, Jill and Emma, generated more neutral copy: the word "arrest" appeared in 23 to 29 percent of name searches on one site and 0 to 60 percent on the other. On the more ad trafficked website, a black-identifying name was 25% more likely to get an ad suggestive of an arrest record. A few names did not follow these patterns. All ads return results for actual individuals and ads appear regardless of whether the name has an arrest record in the company's database. The company maintains Google received the same ad text for groups of last names (not first names), raising questions as to whether Google's technology exposes racial bias.
Calculating certainty in decision trees: Certainty vs Entropy.
Authors: Alejandro Penate-Diaz
Date: 2021-11-12 00:00:00
Proceeding: Unpublished 2021 11

This research introduces a certainty function, to replace the entropy function used to calculate information gain when building decision trees, in algorithms like ID3[1], C4.5[2] and C5.0. The new function is simpler than the entropy function and calculates certainty more accurately, potentially consuming less time in the process.
I will also introduce another formula that help deal with causal analysis. It is the certainty-raising inequality. This formula can be derived from the original certainty function, and provide insights into the cause-effect relations in the data.
Control of Painlevé Paradox in a Robotic System
Authors: Davide Marchese, Marco Coraggio, S. John Hogan, Mario di Bernardo
Date: 2019-07-09 00:00:00

The Painlev\'e paradox is a phenomenon that causes instability in mechanical systems subjects to unilateral constraints. While earlier studies were mostly focused on abstract theoretical settings, recent work confirmed the occurrence of the paradox in realistic set-ups. In this paper, we investigate the dynamics and presence of the Painlev\'e phenomenon in a twolinks robot in contact with a moving belt, through a bifurcation study. Then, we use the results of this analysis to inform the design of control strategies able to keep the robot sliding on the belt and avoid the onset of undesired lift-off. To this aim, through numerical simulations, we synthesise and compare a PID strategy and a hybrid force/motion control scheme, finding that the latter is able to guarantee better performance and avoid the onset of bouncing motion due to the Painlev\'e phenomenon.
Component separation with flexible models. Application to the separation of astrophysical emissions
Authors: Jean-Francois Cardoso, Maude Martin, Jacques Delabrouille, Marc Betoule, Guillaume Patanchon
Date: 2008-03-12 00:00:00

This paper offers a new point of view on component separation, based on a model of additive components which enjoys a much greater flexibility than more traditional linear component models. This flexibility is needed to process the complex full-sky observations of the CMB expected from the Planck space mission, for which it was developed, but it may also be useful in any context where accurate component separation is needed.
Multi-Detector Multi-Component spectral matching and applications for CMB data analysis
Authors: J. Delabrouille, J. -F. Cardoso, G. Patanchon
Date: 2002-11-22 00:00:00

We present a new method for analyzing multi--detector maps containing contributions from several components. Our method, based on matching the data to a model in the spectral domain, permits to estimate jointly the spatial power spectra of the components and of the noise, as well as the mixing coefficients. It is of particular relevance for the analysis of millimeter--wave maps containing a contribution from CMB anisotropies.
Holographic Factorization Machines for Recommendation
Authors: Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh
Date: 2019-07-17 00:00:00
Proceeding: AAAI 2019 7
Tasks: Collaborative Filtering, Retrieval

Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized parameters to model pairwise feature interactions, making it highly expressive and powerful. This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size. Our approach replaces the inner product in FMs with holographic reduced representations (HRRs), which are theoretically motivated by associative retrieval and compressed outer products. Empirically, we found that this leads to consistent improvements over vanilla FMs by up to 4% improvement in terms of mean squared error, with improvements larger at smaller parameterization. Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures. We conduct extensive experiments on nine publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all nine, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor
Authors: Anonymous
Date: 2022-09-28 00:00:00
Proceeding: International Conference on Learning Representations 2022 9
Tasks: 3D Point Cloud Classification, Feature Engineering, Neural Architecture Search, Point Cloud Classification, Semantic Segmentation

Kernel Point Convolution (KPConv) achieves cutting-edge performance on 3D point cloud applications. Unfortunately, the large size of KPConv network limits its usage in mobile scenarios. In addition, we observe that KPConv ignores the kernel relationship and treats each kernel point equally when formulating neighbor-kernel correlation via Euclidean distance. This leads to a weak representation power. To mitigate the above issues, we propose a module named Mobile Attention Kernel Point Convolution (MAKPConv) to improve the efficiency and quality of KPConv. MAKPConv employs a depthwise kernel to reduce resource consumption, and re-calibrates the contribution of kernel points towards each neighbor point via Neighbor-Kernel attention to improve representation power. Furthermore, we capitalize Inverted Residual Bottleneck (IRB) to craft a design space and employ a predictor-based Neural Architecture Search (NAS) approach to automate the design of efficient 3D networks based on MAKPConv. To fully exploit the immense design space via an accurate predictor, we identify the importance of carrying feature engineering on searchable features to improve neural architecture representations, and propose a Wide & Deep Predictor to unify dense and sparse neural architecture representations for lower error in performance prediction. Experimental evaluations show that our NAS-crafted MAKPConv network uses 96% fewer parameters on 3D point cloud classification and segmentation benchmarks with better performance. Compared with state-of-the-art NAScrafted model SPVNAS, our NAS-crafted MAKPConv network achieves 1% better mIOU with 83% fewer parameters and 52% fewer Multiply-Accumulates.
A Convolutional Click Prediction Model
Authors: Qiang Liu, Feng Yu, Shu Wu, Liang Wang
Date: 2015-01-01 00:00:00
Proceeding: CIKM 2015 1
Tasks: model, Prediction

The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network. CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on two public large-scale datasets indicate that CCPM is effective on click prediction.
A Convolutional Click Prediction Model
Authors: Qiang Liu, Feng Yu, Shu Wu, Liang Wang
Date: 2015-01-01 00:00:00
Tasks: model, Prediction

The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network. CCPM can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on two public large-scale datasets indicate that CCPM is effective on click prediction.
Factorization Machines
Authors: Steffen Rendle
Date: 2010-01-01 00:00:00
Tasks: parameter estimation, Recommendation Systems

In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even in problems with huge sparsity (like recommender systems) where SVMs fail. We show that the model equation of FMs can be calculated in linear time and thus FMs can be optimized directly. So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution. We show the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings. On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized models like SVD++, PITF or FPMC. The drawback of these models is that they are not applicable for general prediction tasks but work only with special input data. Furthermore their model equations and optimization algorithms are derived individually for each task. We show that FMs can mimic these models just by specifying the input data (i.e. the feature vectors). This makes FMs easily applicable even for users without expert knowledge in factorization models.