pwc-archive/papers-with-abstracts huggingface dataset:
New drugs and stock market: how to predict pharma market reaction to clinical trial announcements
Authors: Semen Budennyy, Alexey Kazakov, Elizaveta Kovtun, Leonid Zhukov
Date: 2022-08-11 00:00:00

Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. In this work, we provide statistical evidence for the result promulgation influence on the public pharma market value. Whereas most works focus on retrospective impact analysis, the present research aims to predict the numerical values of announcement-induced changes in stock prices. For this purpose, we develop a pipeline that includes a BERT-based model for extracting sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. The challenge of the problem lies in inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to the negative news. Moreover, such phenomenon as the drop in stocks after the positive announcements affirms the counterintuitiveness of the price behavior. Importantly, we discover two crucial factors that should be considered while working within a predictive framework. The first factor is the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of small drug diversification. The second one is the network effect of the events related to the same company or nosology. All findings and insights are gained on the basis of one of the biggest FDA (the Food and Drug Administration) announcement datasets, consisting of 5436 clinical trial announcements from 681 companies over the last five years.
Cross Section Doppler Broadening prediction using Physically Informed Deep Neural Networks
Authors: Arthur Pignet, Luiz Leal, Vaibhav Jaiswal
Date: 2022-08-11 00:00:00

Temperature dependence of the neutron-nucleus interaction is known as the Doppler broadening of the cross-sections. This is a well-known effect due to the thermal motion of the target nuclei that occurs in the neutron-nucleus interaction. The fast computation of such effects is crucial for any nuclear application. Mechanisms have been developed that allow determining the Doppler effects in the cross-section, most of them based on the numerical resolution of the equation known as Solbrig's kernel, which is a cross-section Doppler broadening formalism derived from a free gas atoms distribution hypothesis. This paper explores a novel non-linear approach based on deep learning techniques. Deep neural networks are trained on synthetic and experimental data, serving as an alternative to the cross-section Doppler Broadening (DB). This paper explores the possibility of using physically informed neural networks, where the network is physically regularized to be the solution of a partial derivative equation, inferred from Solbrig's kernel. The learning process is demonstrated by using the fission, capture, and scattering cross sections for $^{235}U$ in the energy range from thermal to 2250 eV.
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Authors: Thibaud Godon, Pier-Luc Plante, Baptiste Bauvin, Elina Francovic-Fontaine, Alexandre Drouin, Jacques Corbeil
Date: 2022-08-11 00:00:00
Tasks: Ensemble Learning

Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool
Authors: Hannah Bast, Matthias Hertel, Natalie Prange
Date: 2022-08-15 00:00:00
Tasks: Entity Linking

We present Elevant, a tool for the fully automatic fine-grained evaluation of a set of entity linkers on a set of benchmarks. Elevant provides an automatic breakdown of the performance by various error categories and by entity type. Elevant also provides a rich and compact, yet very intuitive and self-explanatory visualization of the results of a linker on a benchmark in comparison to the ground truth. A live demo, the link to the complete code base on GitHub and a link to a demo video are provided under https://elevant.cs.uni-freiburg.de .
Exploring Generative Models for Joint Attribute Value Extraction from Product Titles
Authors: Kalyani Roy, Tapas Nayak, Pawan Goyal
Date: 2022-08-15 00:00:00
Tasks: Attribute, Attribute Value Extraction

Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. In this paper, we propose to tackle the AVE task using generative frameworks. We present two types of generative paradigms, namely, word sequence-based and positional sequence-based, by formulating the AVE task as a generation problem. We conduct experiments on two datasets where the generative approaches achieve the new state-of-the-art results. This shows that we can use the proposed framework for AVE tasks without additional tagging or task-specific model design.
A Hybrid Model of Classification and Generation for Spatial Relation Extraction
Authors: Feng Wang Peifeng Li, Qiaoming Zhu
Date: 2022-08-15 00:00:00
Proceeding: COLING 2022 10
Tasks: Natural Language Understanding, Relation, Relation Extraction

Extracting spatial relations from texts is a fundamental task for natural language understanding and previous studies only regard it as a classification task, ignoring those spatial relations with null roles due to their poor information. To address the above issue, we first view spatial relation extraction as a generation task and propose a novel hybrid model HMCGR for this task. HMCGR contains a generation and a classification model, while the former can generate those null-role relations and the latter can extract those non-null-role relations to complement each other. Moreover, a reflexivity evaluation mechanism is applied to further improve the accuracy based on the reflexivity principle of spatial relation. Experimental results on SpaceEval show that HMCGR outperforms the SOTA baselines significantly.
An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs
Authors: Endri Kacupaj, Kuldeep Singh, Maria Maleshkova, Jens Lehmann
Date: 2022-08-13 00:00:00
Tasks: Answer Generation, Conversational Question Answering, Knowledge Graphs, Question Answering

We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility.
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Authors: Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu, Jonathan Weill, Noam Koenigstein
Date: 2022-08-13 00:00:00
Tasks: text similarity

Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. Despite significant progress in the field, the explanations for similarity predictions remain challenging, especially in unsupervised settings. In this work, we present an unsupervised technique for explaining paragraph similarities inferred by pre-trained BERT models. By looking at a pair of paragraphs, our technique identifies important words that dictate each paragraph's semantics, matches between the words in both paragraphs, and retrieves the most important pairs that explain the similarity between the two. The method, which has been assessed by extensive human evaluations and demonstrated on datasets comprising long and complex paragraphs, has shown great promise, providing accurate interpretations that correlate better with human perceptions.
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
Authors: Itzik Malkiel, Dvir Ginzburg, Oren Barkan, Avi Caciularu, Yoni Weill, Noam Koenigstein
Date: 2022-08-13 00:00:00
Tasks: Representation Learning, Triplet

We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task. We focus on downstream tasks of learning similarities for recommendations where we show that MetricBERT outperforms state-of-the-art alternatives, sometimes by a substantial margin. We conduct extensive evaluations of our method and its different variants, showing that our training objective is highly beneficial over a traditional contrastive loss, a standard cosine similarity objective, and six other baselines. As an additional contribution, we publish a dataset of video games descriptions along with a test set of similarity annotations crafted by a domain expert.
Automated Utterance Labeling of Conversations Using Natural Language Processing
Authors: Maria Laricheva, Chiyu Zhang, Yan Liu, GuanYu Chen, Terence Tracey, Richard Young, Giuseppe Carenini
Date: 2022-08-12 00:00:00
Tasks: Domain Adaptation

Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors. Utterance labelling is a common strategy for analyzing this type of data. The development of NLP algorithms allows researchers to automate this task. However, psychological conversational data present some challenges to NLP researchers, including multilabel classification, a large number of classes, and limited available data. This study explored how automated labels generated by NLP methods are comparable to human labels in the context of conversations on adulthood transition. We proposed strategies to handle three common challenges raised in psychological studies. Our findings showed that the deep learning method with domain adaptation (RoBERTa-CON) outperformed all other machine learning methods; and the hierarchical labelling system that we proposed was shown to help researchers strategically analyze conversational data. Our Python code and NLP model are available at https://github.com/mlaricheva/automated_labeling.
Cooperative guidance of multiple missiles: a hybrid co-evolutionary approach
Authors: Xuejing Lan, Junda Chen, Zhijia Zhao, Tao Zou
Date: 2022-08-15 00:00:00
Tasks: continuous-control, Continuous Control

Cooperative guidance of multiple missiles is a challenging task with rigorous constraints of time and space consensus, especially when attacking dynamic targets. In this paper, the cooperative guidance task is described as a distributed multi-objective cooperative optimization problem. To address the issues of non-stationarity and continuous control faced by cooperative guidance, the natural evolutionary strategy (NES) is improved along with an elitist adaptive learning technique to develop a novel natural co-evolutionary strategy (NCES). The gradients of original evolutionary strategy are rescaled to reduce the estimation bias caused by the interaction between the multiple missiles. Then, a hybrid co-evolutionary cooperative guidance law (HCCGL) is proposed by integrating the highly scalable co-evolutionary mechanism and the traditional guidance strategy. Finally, three simulations under different conditions demonstrate the effectiveness and superiority of this guidance law in solving cooperative guidance tasks with high accuracy. The proposed co-evolutionary approach has great prospects not only in cooperative guidance, but also in other application scenarios of multi-objective optimization, dynamic optimization and distributed control.
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram
Authors: Nathan Lutes, Venkata Sriram Siddhardh Nadendla, K. Krishnamurthy
Date: 2022-08-14 00:00:00
Tasks: Computational Efficiency, EEG, Electroencephalogram (EEG)

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN's performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06 percent with a true positive rate of 98.50 percent, a true negative rate of 99.20 percent and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation
Authors: Vasco Lopes, Miguel Santos, Bruno Degardin, Luís A. Alexandre
Date: 2022-07-22 00:00:00
Tasks: Neural Architecture Search

Neural Architecture Search (NAS) methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to converge to local minima as soon as generated architectures seem to yield good results. This paper proposes GEA, a novel approach for guided NAS. GEA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation. Subsequently, GEA continuously extracts knowledge about the search space without increased complexity by generating several off-springs from an existing architecture at each generation. More, GEA forces exploitation of the most performant architectures by descendant generation while simultaneously driving exploration through parent mutation and favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method, and extensive ablation studies evaluate the importance of different parameters. Results show that GEA achieves state-of-the-art results on all data sets of NAS-Bench-101, NAS-Bench-201 and TransNAS-Bench-101 benchmarks.
Adaptive novelty detection with false discovery rate guarantee
Authors: Ariane Marandon, Lihua Lei, David Mary, Etienne Roquain
Date: 2022-08-13 00:00:00
Tasks: Novelty Detection

This paper studies the semi-supervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-controlling procedures that are often committed to a pre-specified p-value function, AdaDetect learns the transformation in a data-adaptive manner to focus the power on the directions that distinguish between inliers and outliers. Inspired by the multiple testing literature, we further propose variants of AdaDetect that are adaptive to the proportion of nulls while maintaining the finite-sample FDR control. The methods are illustrated on synthetic datasets and real-world datasets, including an application in astrophysics.
Inverse Extended Kalman Filter -- Part II: Highly Non-Linear and Uncertain Systems
Authors: Himali Singh, Arpan Chattopadhyay, Kumar Vijay Mishra
Date: 2022-08-13 00:00:00
Tasks: State Estimation

Counter-adversarial system design problems have lately motivated the development of inverse Bayesian filters. For example, inverse Kalman filter (I-KF) has been recently formulated to estimate the adversary's Kalman-filter-tracked estimates and hence, predict the adversary's future steps. The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF). The companion paper proposed the theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs). In this paper, we develop this theory for highly non-linear models, which employ second-order, Gaussian sum, and dithered forward EKFs. In particular, we derive theoretical stability guarantees for the inverse second-order EKF using the bounded non-linearity approach. To address the limitation of the standard I-EKFs that the system model and forward filter are perfectly known to the defender, we propose reproducing kernel Hilbert space-based EKF to learn the unknown system dynamics based on its observations, which can be employed as an inverse filter to infer the adversary's estimate. Numerical experiments demonstrate the state estimation performance of the proposed filters using recursive Cram\'{e}r-Rao lower bound as a benchmark.
Finite Sample Complexity of Sequential Monte Carlo Estimators on Multimodal Target Distributions
Authors: Joseph Mathews, Scott C. Schmidler
Date: 2022-08-13 00:00:00

We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly useful when the target distribution is multimodal and global mixing of the Markov kernel is slow; in such cases our approach establishes the benefits of SMC over the corresponding Markov chain Monte Carlo (MCMC) estimator. The lack of global mixing is addressed by sequentially controlling the bias introduced by SMC resampling procedures. We apply these results to obtain complexity bounds for approximating expectations under mixtures of log-concave distributions and show that SMC provides a fully polynomial time randomized approximation scheme for some difficult multimodal problems where the corresponding Markov chain sampler is exponentially slow. Finally, we compare the bounds obtained by our approach to existing bounds for tempered Markov chains on the same problems.
Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
Authors: Sudipto Banerjee, Xiang Chen, Ian Frankenburg, Daniel Zhou
Date: 2022-08-12 00:00:00
Tasks: Gaussian Processes, regression, State Space Models, Transfer Learning

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary inputs. The emulated learner can then be used to train the system from noisy data achieved by melding information from observed data with the emulated mechanistic system. This joint melding of mechanistic systems employ hierarchical state-space models with Gaussian process regression. Assuming the dynamical system is controlled by a finite collection of inputs, Gaussian process regression learns the effect of these parameters through a number of training runs, driving the stochastic innovations of the spatiotemporal state-space component. This enables efficient modeling of the dynamics over space and time. This article details exact inference with analytically accessible posterior distributions in hierarchical matrix-variate Normal and Wishart models in designing the emulator. This step obviates expensive iterative algorithms such as Markov chain Monte Carlo or variational approximations. We also show how emulation is applicable to large-scale emulation by designing a dynamic Bayesian transfer learning framework. Inference on $\bm \eta$ proceeds using Markov chain Monte Carlo as a post-emulation step using the emulator as a regression component. We demonstrate this framework through solving inverse problems arising in the analysis of ordinary and partial nonlinear differential equations and, in addition, to a black-box computer model generating spatiotemporal dynamics across a graphical model.
Understanding the "Pathway" Towards a Searcher's Learning Objective
Authors: Kelsey Urgo, Jaime Arguello
Date: 2022-08-15 00:00:00

Search systems are often used to support learning-oriented goals. This trend has given rise to the "search-as-learning" movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher's \emph{type} of learning objective influence their trajectory (or \emph{pathway}) towards that objective? We report on a lab study ($N=36$) in which participants gathered information to meet a specific type of learning objective. To characterize learning objectives \emph{and pathways}, we leveraged Anderson and Krathwohl's (A\&K's) taxonomy \cite{anderson2001taxonomy}. Participants completed learning-oriented search tasks that varied along three cognitive processes (apply, evaluate, create) and three knowledge types (factual, conceptual, procedural knowledge). A \emph{pathway} is defined as a sequence of \emph{learning instances} (e.g., subgoals) that were also each classified into cells from A\&K's taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants' think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the learning objective on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the learning objective on the types of A\&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon \emph{transitions} between A\&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.