pwc-archive/papers-with-abstracts huggingface dataset:
Epigenetic factor competition reshapes the EMT landscape
Authors: M. Ali Al-Radhawi, Shubham Tripathi, Yun Zhang, Eduardo D. Sontag, Herbert Levine
Date: 2022-09-13 00:00:00

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as Gene Regulatory Networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators in addition to local transcription factors, and show the counter-intuitive consequences of such interactions. We apply our approach to recent experimental findings on the Epithelial-Mesenchymal Transition (EMT). We show that it can explain the puzzling experimental data as well provide new verifiable predictions.
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data
Authors: Willem Bonnaffé, Tim Coulson
Date: 2022-09-13 00:00:00
Tasks: Time Series, Time Series Analysis, Uncertainty Quantification

1. Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions nonparametrically from time series data. NODEs, however, are slow to fit, and inferred interactions have not been truthed. 2. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks, and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate, and rotifer population, as well as in the hare and lynx system. 3. Our BNGM approach allows us to cut down the fitting time of NODE systems to only a few seconds. The method provides accurate estimates of ecological interactions in the artificial system, as linear and nonlinear true interactions are estimated with minimal error. In the real systems, dynamics are driven by a mixture of linear and nonlinear ecological interactions, of which only the strongest are consistent across replicates. 4. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting, cross-validation, and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.
Lack of corundum, carbon residues and revealing gaps on dental implants
Authors: Guy Florian Draenert, Gergo Mitov
Date: 2022-09-13 00:00:00

Surface modification is an important topic to improve dental implants. Corundum residues, which are part of current dental implant blasting, disappeared on Straumann dental implants in recent publications. In our investigations of the surface of 4 different Straumann implants using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) we found the following three main findings: surfaces are nearly corundum-free, disseminated gap-framed corundum particles and significant molecular carbon residues. The data strongly suggest that Straumann applies a modified surface technology on dental implants to remove corundum residues and involving unclear carbons. One explanation could be, a Straumann patent involving a dextran coating allowing easy corundum particle removal by aqueous solution, while unintended molecular carbon residues cannot explain all findings. This change of the production process without a new approval by the FDA would be a violation of US federal law and the carbon bindings are a possible danger to patients.
A stochastic volatility model for the valuation of temperature derivatives
Authors: Aurélien Alfonsi, Nerea Vadillo
Date: 2022-09-13 00:00:00

This paper develops a new stochastic volatility model for the temperature that is a natural extension of the Ornstein-Uhlenbeck model proposed by Benth and Benth (2007). This model allows to be more conservative regarding extreme events while keeping tractability. We give a method based on Conditional Least Squares to estimate the parameters on daily data and estimate our model on eight major European cities. We then show how to calculate efficiently the average payoff of weather derivatives both by Monte-Carlo and Fourier transform techniques. This new model allows to better assess the risk related to temperature volatility.
Automatically Assessing Students Performance with Smartphone Data
Authors: J. Fernandes, J. Sá Silva, A. Rodrigues, S. Sinche, F. Boavida
Date: 2022-07-06 00:00:00

As the number of smart devices that surround us increases, so do the opportunities to create smart socially-aware systems. In this context, mobile devices can be used to collect data about students and to better understand how their day-to-day routines can influence their academic performance. Moreover, the Covid-19 pandemic led to new challenges and difficulties, also for students, with considerable impact on their lifestyle. In this paper we present a dataset collected using a smartphone application (ISABELA), which include passive data (e.g., activity and location) as well as self-reported data from questionnaires. We present several tests with different machine learning models, in order to classify students' performance. These tests were carried out using different time windows, showing that weekly time windows lead to better prediction and classification results than monthly time windows. Furthermore, it is shown that the created models can predict student performance even with data collected from different contexts, namely before and during the Covid-19 pandemic. SVMs, XGBoost and AdaBoost-SAMME with Random Forest were found to be the best algorithms, showing an accuracy greater than 78%. Additionally, we propose a pipeline that uses a decision level median voting algorithm to further improve the models' performance, by using historic data from the students to further improve the prediction. Using this pipeline, it is possible to further increase the performance of the models, with some of them obtaining an accuracy greater than 90%.
4G 5G Cell-level Multi-indicator Forecasting based on Dense-MLP
Authors: Jiacheng Yin, Wenwen Li, Xidong Wang, Xiaozhou Ye, Ye Ouyang
Date: 2022-07-22 00:00:00

With the development of 4G/5G, the rapid growth of traffic has caused a large number of cell indicators to exceed the warning threshold, and network quality has deteriorated. It is necessary for operators to solve the congestion in advance and effectively to guarantee the quality of user experience. Cell-level multi-indicator forecasting is the foundation task for proactive complex network optimization. In this paper, we propose the 4G/5G Cell-level multi-indicator forecasting method based on the dense-Multi-Layer Perceptron (MLP) neural network, which adds additional fully-connected layers between non-adjacent layers in an MLP network. The model forecasted the following week's traffic indicators of 13000 cells according to the six-month historical indicators of 65000 cells in the 4G&5G network, which got the highest weighted MAPE score (0.2484) in the China Mobile problem statement in the ITU-T AI/ML in 5G Challenge 2021. Furthermore, the proposed model has been integrated into the AsiaInfo 4G/5G energy-saving system and deployed in Jiangsu Province of China.
Black-box Dataset Ownership Verification via Backdoor Watermarking
Authors: Yiming Li, Mingyan Zhu, Xue Yang, Yong Jiang, Tao Wei, Shu-Tao Xia
Date: 2022-08-04 00:00:00

Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$, BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at \url{https://github.com/THUYimingLi/DVBW}.
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game
Authors: Claudio Greco, Alberto Testoni, Raffaella Bernardi, Stella Frank
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9
Tasks: Diversity

Pre-trained Vision and Language Transformers achieve high performance on downstream tasks due to their ability to transfer representational knowledge accumulated during pretraining on substantial amounts of data. In this paper, we ask whether it is possible to compete with such models using features based on transferred (pre-trained, frozen) representations combined with a lightweight architecture. We take a multimodal guessing task as our testbed, GuessWhat?!. An ensemble of our lightweight model matches the performance of the finetuned pre-trained transformer (LXMERT). An uncertainty analysis of our ensemble shows that the lightweight transferred representations close the data uncertainty gap with LXMERT, while retaining model diversity leading to ensemble boost. We further demonstrate that LXMERT’s performance gain is due solely to its extra V&L pretraining rather than because of architectural improvements. These results argue for flexible integration of multiple features and lightweight models as a viable alternative to large, cumbersome, pre-trained models.
A Cross-lingual Comparison of Human and Model Relative Word Importance
Authors: Felix Morger, Stephanie Brandl, Lisa Beinborn, Nora Hollenstein
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9

Relative word importance is a key metric for natural language processing. In this work, we compare human and model relative word importance to investigate if pretrained neural language models focus on the same words as humans cross-lingually. We perform an extensive study using several importance metrics (gradient-based saliency and attention-based) in monolingual and multilingual models, including eye-tracking corpora from four languages (German, Dutch, English, and Russian). We find that gradient-based saliency, first-layer attention, and attention flow correlate strongly with human eye-tracking data across all four languages. We further analyze the role of word length and word frequency in determining relative importance and find that it strongly correlates with length and frequency, however, the mechanisms behind these non-linear relations remain elusive. We obtain a cross-lingual approximation of the similarity between human and computational language processing and insights into the usability of several importance metrics.
In Search of Meaning and Its Representations for Computational Linguistics
Authors: Simon Dobnik, Robin Cooper, Adam Ek, Bill Noble, Staffan Larsson, Nikolai Ilinykh, Vladislav Maraev, Vidya Somashekarappa
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9

In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.
Can We Use Small Models to Investigate Multimodal Fusion Methods?
Authors: Lovisa Hagström, Tobias Norlund, Richard Johansson
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9
Tasks: Math

Many successful methods for fusing language with information from the visual modality have recently been proposed and the topic of multimodal training is ever evolving. However, it is still largely not known what makes different vision-and-language models successful. Investigations into this are made difficult by the large sizes of the models used, requiring large training datasets and causing long train and compute times. Therefore, we propose the idea of studying multimodal fusion methods in a smaller setting with small models and datasets. In this setting, we can experiment with different approaches for fusing multimodal information with language in a controlled fashion, while allowing for fast experimentation. We illustrate this idea with the math arithmetics sandbox. This is a setting in which we fuse language with information from the math modality and strive to replicate some fusion methods from the vision-and-language domain. We find that some results for fusion methods from the larger domain translate to the math arithmetics sandbox, indicating a promising future avenue for multimodal model prototyping.
Embodied Interaction in Mental Health Consultations: Some Observations on Grounding and Repair
Authors: Jing Hui Law, Patrick Healey, Rosella Galindo Esparza
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9
Tasks: Management, Position

Shared physical space is an important resource for face-to-face interaction. People use the position and orientation of their bodies—relative to each other and relative to the physical environment—to determine who is part of a conversation, to manage conversational roles (e.g. speaker, addressee, side-participant) and to help co-ordinate turn-taking. These embodied uses of shared space also extend to more fine-grained aspects of interaction, such as gestures and body movements, to support topic management, orchestration of turns and grounding. This paper explores the role of embodied resources in (mis)communication in a corpus of mental health consultations. We illustrate some of the specific ways in which clinicians and patients can exploit embodiment and the position of objects in shared space to diagnose and manage moments of misunderstanding.
Where Am I and Where Should I Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task
Authors: Sheikh Mannan, Nikhil Krishnaswamy
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9

In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects’ joystick motions as well as the subjects’ proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans.
From Speed to Car and Back: An Exploratory Study about Associations between Abstract Nouns and Images
Authors: Ludovica Cerini, Eliana Di Palma, Alessandro Lenci
Date: 2022-09-01 00:00:00
Proceeding: CLASP 2022 9

Abstract concepts, notwithstanding their lack of physical referents in real world, are grounded in sensorimotor experience. In fact, images depicting concrete entities may be associated to abstract concepts, both via direct and indirect grounding processes. However, what are the links connecting the concrete concepts represented by images and abstract ones is still unclear. To investigate these links, we conducted a preliminary study collecting word association data and image-abstract word pair ratings, to identify whether the associations between visual and verbal systems rely on the same conceptual mappings. The goal of this research is to understand to what extent linguistic associations could be confirmed with visual stimuli, in order to have a starting point for multimodal analysis of abstract and concrete concepts.
A Corpus-based Study of Derivational Morphology and its Theoretical Implications
Authors: Junya Morita
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

The present study investigates the formal and semantic properties of derivational morphology, dealing in particular with -able derivatives in English (e.g. the recorder is pocketable ). Focusing principally on hapax legomena in a large corpus, a reliable indicator of online coinage, -able derivatives are extracted from it. Detailed observation of them is carried out and then their theoretical analysis is conducted in the framework of generative morphology. The data analysis elucidates (i) a core aspect of -able : it productively attaches to transitive verbs to produce modalized passive adjectives whose external arguments are restricted to Theme arguments and (ii) a peripheral facet: the basic meaning of -able as well as its prototypical base category and external argument are extended, on a small scale, to other kinds of meaning and category. Based on these empirical observations, major and minor formation rules are proposed to deal respectively with regular and sub-regular -able derivation.
Syntactic and morphological features after verbs of perception: Bulgarian in Balkan context
Authors: Ekaterina Tarpomanova
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

The paper analyses the types of constructions that express a subordinate event after a verb of perception in the languages of the Balkan Sprachbund. The subordinate clauses that may follow a verb of perception are a result of common historical processes in Bulgarian, Albanian, Romanian and Greek: the substitution of infinitive by subjunctive and the neutralization of modal and declarative conjunctions after verbs of perception. Additionally, in Albanian and Romanian among the non-finite verbal forms gerund may be found after perception verbs. For the analysed syntactic structures in Bulgarian a corpus approach is further applied in order to support the linguistic analysis with quantitative data.
On the Valency Frames of type Subject-Predicate in Bulgarian
Authors: Petya Osenova
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Vocal Bursts Type Prediction

The paper presents some observations on the semantic constraints of the intransitive subjects with respect to the predicates they combine with. For these observations a valency dictionary of Bulgarian was used. Here two clarifications are to be made. First, the intransitive predicates are viewed in a broader perspective. They combine true intransitives as well as intransitive usages of transitive verbs. The complexity comes from the modeling of these verbs in the morphological dictionary. Second, the semantic constraints that are considered here, are limited to a set of semantic roles and build on the lexicographic classes of verbs in WordNet.
Analysis of Similes in Serbian Literary Texts (1860-1920) using computational methods
Authors: Cvetana Krstev, Jelena Jaćimović, Duško Vitas
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Retrieval, Specificity

Similes are rhetorical figures which play an important role in literary texts. This paper presents a finite-state methodology developed for the description of adjectival similes, which enables their retrieval and annotation in Serbian novels written in the mid-19th and early 20th centuries. The results of a textometric analysis reveal the most frequent adjectival similes and the specificity of their usage, with respect to the author, title, or publication date, in a subset of the SrpELTeC corpus.
Classification of L2 Thesis Statement Writing Performance Using Syntactic Complexity Indices
Authors: Kutay Uzun
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9

This study primarily aimed to find out if machine learning classification algorithms could accurately classify L2 thesis statement writing performance as high or low using syntactic complexity indices. Secondarily, the study aimed to reveal how the syntactic complexity indices from which classification algorithms gained the largest amount of information interacted with L2 thesis statement writing performance. The data set of the study consisted of 137 high-performing and 69 low-performing thesis statements written by undergraduate learners of English in a foreign language context. Experiments revealed that the Locally Weighted Learning algorithm could classify L2 thesis statement writing performance with 75.61% accuracy, 20.01% above the baseline. Balancing the data set via Synthetic Minority Oversampling produced the same accuracy percentage with the Stochastic Gradient Descent algorithm, resulting in a slight increase in Kappa Statistic. In both imbalanced and balanced data sets, it was seen that the number of coordinate phrases, coordinate phrase per t-unit, coordinate phrase per clause and verb phrase per t-unit were the variables from which the classification algorithms gained the largest amount of information. Mann-Whitney U tests showed that the high-performing thesis statements had a larger amount of coordinate phrases and higher ratios of coordinate phrase per t-unit and coordinate phrase per clause. The verb phrase per t-unit ratio was seen to be lower in high-performing thesis statements than their low-performing counterparts.
Categorisation of Bulgarian Legislative Documents
Authors: Nikola Obreshkov, Martin Yalamov, Svetla Koeva
Date: 2020-09-01 00:00:00
Proceeding: CLIB 2020 9
Tasks: Term Extraction

The paper presents the categorisation of Bulgarian MARCELL corpus in toplevel EuroVoc domains. The Bulgarian MARCELL corpus is part of a recently developed multilingual corpus representing the national legislation in seven European countries. We performed several experiments with JEX Indexer, with neural networks and with a basic method measuring the domain-specific terms in documents annotated in advance with IATE terms and EuroVoc descriptors (combined with grouping of a primary document and its satellites, term extraction and parsing of the titles of the documents). The evaluation shows slight overweight of the basic method, which makes it appropriate as the categorisation should be a module of a NLP Pipeline for Bulgarian that is continuously feeding and annotating the Bulgarian MARCELL corpus with newly issued legislative documents.