pwc-archive/papers-with-abstracts huggingface dataset:
A Weibo Dataset for the 2022 Russo-Ukrainian Crisis
Authors: Yi R. Fung, Heng Ji
Date: 2022-03-09 00:00:00
Tasks: Misinformation

Online social networks such as Twitter and Weibo play an important role in how people stay informed and exchange reactions. Each crisis encompasses a new opportunity to study the portability of models for various tasks (e.g., information extraction, complex event understanding, misinformation detection, etc.), due to differences in domain, entities, and event types. We present the Russia-Ukraine Crisis Weibo (RUW) dataset, with over 3.5M user posts and comments in the first release. Our data is available at https://github.com/yrf1/RussiaUkraine_weibo_dataset.
General properties of the Solutions to Moving Boundary Problems for Black-Sholes Equations
Authors: Hyong-Chol O, Tae-Song Choe
Date: 2022-03-11 00:00:00
Tasks: Financial Analysis

We study general properties such as the solution representation of a moving boundary value problem of the Black-Scholes equation, its min-max estimation, lower and upper gradient estimates, and strict monotonicity with respect to the spatial variables of the solution. These results are used in the study of a structural model of pricing puttable bond with credit risk. We first prove the solution representation of a special fixed boundary value problem of the Black-Scholes equation, the min-max estimate, the lower and upper gradient estimates, and the strict monotonicity with respect to the spatial variables of the solution. Then, these results are applied to give the solution representation of a moving boundary value problem of the Black-Scholes equation with moving boundary in the form of an exponential function, the min-max estimate, the lower and upper gradient estimates, and the strict monotonicity results on the spatial variables of the solution. Finally, we illustrate how these results can be used in the derivation of analytical pricing formulae and financial analysis of price functions of puttable bonds with credit risk (corporate bonds with one early redemption date). Our results can be used for the derivation and analysis of the analytical pricing formulae of the one-factor structural model of a more general puttable bonds with credit risk (corporate bond with several early redemption dates).
Analytical Pricing of 2 Factor Structural PDE model for a Puttable Bond with Credit Risk
Authors: Hyong Chol O, Dae Song Choe, Gyong-Dok Rim
Date: 2022-03-11 00:00:00

In this paper is proposed a 2 factor structural PDE model of pricing puttable bond with credit risk and derived the analytical pricing formula. To this end, first, a 2 factor structural (PDE) model of pricing zero coupon bond with credit risk is provided, the analytical pricing formula is derived under some conditions for default boundary and default recovery, and the strict monotonicity of the bond price function with respect to the firm value variable is proved. Then a (2 factor) pricing model of the option on zero coupon bond with credit risk is provided and under some condition on the exercise price its analytical pricing formula is derived by transforming the 2 factor model into a terminal boundary value problem for Black-Scholes equation with time dependent coefficient using zero coupon bond as numeraire. Using it, we provide the pricing formulae of the puttable and callable bonds with credit risk.
Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
Authors: Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman
Date: 2022-03-11 00:00:00
Tasks: Evolutionary Algorithms

Model-Based Evolutionary Algorithms (MBEAs) can be highly scalable by virtue of linkage (or variable interaction) learning. This requires, however, that the linkage model can capture the exploitable structure of a problem. Usually, a single type of linkage structure is attempted to be captured using models such as a linkage tree. However, in practice, problems may exhibit multiple linkage structures. This is for instance the case in multi-objective optimization when the objectives have different linkage structures. This cannot be modelled sufficiently well when using linkage models that aim at capturing a single type of linkage structure, deteriorating the advantages brought by MBEAs. Therefore, here, we introduce linkage kernels, whereby a linkage structure is learned for each solution over its local neighborhood. We implement linkage kernels into the MBEA known as GOMEA that was previously found to be highly scalable when solving various problems. We further introduce a novel benchmark function called Best-of-Traps (BoT) that has an adjustable degree of different linkage structures. On both BoT and a worst-case scenario-based variant of the well-known MaxCut problem, we experimentally find a vast performance improvement of linkage-kernel GOMEA over GOMEA with a single linkage tree as well as the MBEA known as DSMGA-II.
A persistent-homology-based turbulence index & some applications of TDA on financial markets
Authors: Miguel A. Ruiz-Ortiz, José Carlos Gómez-Larrañaga, Jesús Rodríguez-Viorato
Date: 2022-03-10 00:00:00
Tasks: Time Series, Topological Data Analysis

Topological Data Analysis (TDA) is a modern approach to Data Analysis focusing on the topological features of data; it has been widely studied in recent years and used extensively in Biology, Physics, and many other areas. However, financial markets have been studied slightly through TDA. Here we present a quick review of some recent applications of TDA on financial markets, including applications in the early detection of turbulence periods in financial markets and how TDA can help to get new insights while investing. Also, we propose a new turbulence index based on persistent homology -- the fundamental tool for TDA -- that seems to capture critical transitions in financial data; we tested our index with different financial time series (S&P500, Russel 2000, S&P/BMV IPC and Nikkei 225) and crash events (Black Monday crash, dot-com crash, 2007-08 crash and COVID-19 crash). Furthermore, we include an introduction to persistent homology so the reader can understand this paper without knowing TDA.
Particle Swarm Optimization based on Novelty Search
Authors: Mr. Rajesh Misra, Dr. Kumar S Ray
Date: 2022-02-10 00:00:00

In this paper we propose a Particle Swarm Optimization algorithm combined with Novelty Search. Novelty Search finds novel place to search in the search domain and then Particle Swarm Optimization rigorously searches that area for global optimum solution. This method is never blocked in local optima because it is controlled by Novelty Search which is objective free. For those functions where there are many more local optima and second global optimum is far from true optimum, the present method works successfully. The present algorithm never stops until it searches entire search area. A series of experimental trials prove the robustness and effectiveness of the present algorithm on complex optimization test functions.
Prediction Sets Adaptive to Unknown Covariate Shift
Authors: Hongxiang Qiu, Edgar Dobriban, Eric Tchetgen Tchetgen
Date: 2022-03-11 00:00:00
Tasks: Prediction, Uncertainty Quantification

Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift -- a prevalent issue in practice -- poses a serious unsolved challenge. In this paper, we show that prediction sets with finite-sample coverage guarantee are uninformative and propose a novel flexible distribution-free method, PredSet-1Step, to efficiently construct prediction sets with an asymptotic coverage guarantee under unknown covariate shift. We formally show that our method is \textit{asymptotically probably approximately correct}, having well-calibrated coverage error with high confidence for large samples. We illustrate that it achieves nominal coverage in a number of experiments and a data set concerning HIV risk prediction in a South African cohort study. Our theory hinges on a new bound for the convergence rate of the coverage of Wald confidence intervals based on general asymptotically linear estimators.
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
Authors: Nikolaj Thams, Rikke Søndergaard, Sebastian Weichwald, Jonas Peters
Date: 2022-03-11 00:00:00
Tasks: regression, Time Series, Time Series Analysis

Instrumental variable (IV) regression relies on instruments to infer causal effects from observational data with unobserved confounding. We consider IV regression in time series models, such as vector auto-regressive (VAR) processes. Direct applications of i.i.d. techniques are generally inconsistent as they do not correctly adjust for dependencies in the past. In this paper, we outline the difficulties that arise due to time structure and propose methodology for constructing identifying equations that can be used for consistent parametric estimation of causal effects in time series data. One method uses extra nuisance covariates to obtain identifiability (an idea that can be of interest even in the i.i.d. case). We further propose a graph marginalization framework that allows us to apply nuisance IV and other IV methods in a principled way to time series. Our methods make use of a version of the global Markov property, which we prove holds for VAR(p) processes. For VAR(1) processes, we prove identifiability conditions that relate to Jordan forms and are different from the well-known rank conditions in the i.i.d. case (they do not require as many instruments as covariates, for example). We provide methods, prove their consistency, and show how the inferred causal effect can be used for distribution generalization. Simulation experiments corroborate our theoretical results. We provide ready-to-use Python code.
Classification from Positive and Biased Negative Data with Skewed Labeled Posterior Probability
Authors: Shotaro Watanabe, Hidetoshi Matsui
Date: 2022-03-11 00:00:00
Tasks: Binary Classification, Classification, Weakly-supervised Learning

The binary classification problem has a situation where only biased data are observed in one of the classes. In this paper, we propose a new method to approach the positive and biased negative (PbN) classification problem, which is a weakly supervised learning method to learn a binary classifier from positive data and negative data with biased observations. We incorporate a method to correct the negative impact due to skewed confidence, which represents the posterior probability that the observed data are positive. This reduces the distortion of the posterior probability that the data are labeled, which is necessary for the empirical risk minimization of the PbN classification problem. We verified the effectiveness of the proposed method by numerical experiments and real data analysis.
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
Authors: Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens
Date: 2022-03-11 00:00:00
Tasks: Active Learning, Collaborative Filtering, Information Retrieval, Recommendation Systems, Retrieval

Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.
Supporting Schema References in Keyword Queries over Relational Databases
Authors: Paulo Martins, Altigran da Silva, João Cavalcanti, Edleno de Moura
Date: 2022-03-11 00:00:00

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. These systems take the keywords from the input query, locate the elements of the target database that correspond to these keywords, and look for ways to "connect" these elements using information on referential integrity constraints, i.e., key/foreign key pairs. Although several such systems have been proposed in the literature, most of them only support queries whose keywords refer to the contents of the target database and just very few support queries in which keywords refer to elements of the database schema. This paper proposes LATHE, a novel R-KwS designed to support such queries. To this end, in our work, we first generalize the well-known concepts of Query Matches (QMs) and Candidate Joining Networks (CJNs) to handle keywords referring to schema elements and propose new algorithms to generate them. Then, we introduce an approach to automatically select the CJNs that are more likely to represent the user intent when issuing a keyword query. This approach includes two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning void useless CJNs. We present a comprehensive set of experiments performed with query sets and datasets previously used in experiments with state-of-the-art R-KwS systems and methods. Our results indicate that LATHE can handle a wider variety of keyword queries while remaining highly effective, even for large databases with intricate schemas.
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
Authors: Mehwish Alam, Andreea Iana, Alexander Grote, Katharina Ludwig, Philipp Müller, Heiko Paulheim
Date: 2022-03-11 00:00:00
Tasks: Articles, Diversity, Recommendation Systems, Sentiment Analysis, Stance Detection

News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.
A New String Edit Distance and Applications
Authors: Taylor Petty, Jan Hannig, Tunde I Huszar, Hari Iyer
Date: 2022-03-11 00:00:00
Tasks: DNA analysis, Spelling Correction

String edit distances have been used for decades in applications ranging from spelling correction and web search suggestions to DNA analysis. Most string edit distances are variations of the Levenshtein distance and consider only single-character edits. In forensic applications polymorphic genetic markers such as short tandem repeats (STRs) are used. At these repetitive motifs the DNA copying errors consist of more than just single base differences. More often the phenomenon of ``stutter'' is observed, where the number of repeated units differs (by whole units) from the template. To adapt the Levenshtein distance to be suitable for forensic applications where DNA sequence similarity is of interest, a generalized string edit distance is defined that accommodates the addition or deletion of whole motifs in addition to single-nucleotide edits. A dynamic programming implementation is developed for computing this distance between sequences. The novelty of this algorithm is in handling the complex interactions that arise between multiple- and single-character edits. Forensic examples illustrate the purpose and use of the Restricted Forensic Levenshtein (RFL) distance measure, but applications extend to sequence alignment and string similarity in other biological areas, as well as dynamic programming algorithms more broadly.
Hippocampal and auditory contributions to speech segmentation
Authors: Neus Ramos-Escobar, Manuel Mercier, Agnès Trébuchon-Fonséca, Antoni Rodriguez-Fornells, Clément François, Daniele Schön
Date: 2022-03-11 00:00:00
Tasks: Hippocampus, Segmentation

Statistical learning has been proposed as a mechanism to structure and segment the continuous flow of information in several sensory modalities. Previous studies proposed that the medial temporal lobe, and in particular the hippocampus, may be crucial to parse the stream in the visual modality. However, the involvement of the hippocampus in auditory statistical learning, and specifically in speech segmentation is less clear. To explore the role of the hippocampus in speech segmentation based on statistical learning, we exposed seven pharmaco-resistant temporal lobe epilepsy patients to a continuous stream of trisyllabic pseudowords and recorded intracranial stereotaxic electro-encephalography (sEEG). We used frequency-tagging analysis to quantify neuronal synchronization of the hippocampus and auditory regions to the temporal structure of words and syllables of the stream. Results show that while auditory regions highly respond to syllable frequency, the hippocampus responds mostly to word frequency. These findings provide direct evidence of the involvement of the hippocampus in speech segmentation process and suggest a hierarchical organization of auditory information during speech processing.
Effects of time-varying habitat connectivity on metacommunity persistence
Authors: Subhendu Bhandary, Debabrata Biswas, Tanmoy Banerjee, Partha Sharathi Dutta
Date: 2022-03-11 00:00:00

Network structure or connectivity pattern is critical in determining collective dynamics among interacting species in ecosystems. Conventional research on species persistence in spatial populations has focused on static network structure, though most real network structures change in time, forming time-varying networks. This raises the question, in metacommunities, how does the pattern of synchrony vary with temporal evolution in the network structure. The synchronous dynamics among species are known to reduce metacommunity persistence. Here, we consider a time-varying metacommunity small-world network consisting of a chaotic three-species food chain oscillator in each patch/node. The rate of change in the network connectivity is determined by the natural frequency or its subharmonics of the constituent oscillator to allow sufficient time for the evolution of species in between successive rewirings. We find that over a range of coupling strengths and rewiring periods, even higher rewiring probabilities drive a network from asynchrony towards synchrony. Moreover, in networks with a small rewiring period, an increase in average degree (more connected networks) pushes the asynchronous dynamics to synchrony. On the contrary, in networks with a low average degree, a higher rewiring period drives the synchronous dynamics to asynchrony resulting in increased species persistence. Our results also follow the calculation of synchronization time and robust across other ecosystem models. Overall, our study opens the possibility of developing temporal connectivity strategies to increase species persistence in ecological networks.
The Long Arc of Fairness: Formalisations and Ethical Discourse
Authors: Pola Schwöbel, Peter Remmers
Date: 2022-03-08 00:00:00
Tasks: ARC, Decision Making, Fairness, Navigate

In recent years, the idea of formalising and modelling fairness for algorithmic decision making (ADM) has advanced to a point of sophisticated specialisation. However, the relations between technical (formalised) and ethical discourse on fairness are not always clear and productive. Arguing for an alternative perspective, we review existing fairness metrics and discuss some common issues. For instance, the fairness of procedures and distributions is often formalised and discussed statically, disregarding both structural preconditions of the status quo and downstream effects of a given intervention. We then introduce dynamic fairness modelling, a more comprehensive approach that realigns formal fairness metrics with arguments from the ethical discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal metrics to quantify decision procedures and outcomes and (3) mid-term or long-term downstream effects. By contextualising these elements of fairness-related processes, dynamic fairness modelling explicates formerly latent ethical aspects and thereby provides a helpful tool to navigate trade-offs between different fairness interventions. To illustrate the framework, we discuss an example application -- the current European efforts to increase the number of women on company boards, e.g. via quota solutions -- and present early technical work that fits within our framework.
Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning
Authors: Qingfeng Yao, Jilong Wang, Shuyu Yang, Cong Wang, Hongyin Zhang, Qifeng Zhang, Donglin Wang
Date: 2022-03-02 00:00:00
Tasks: Deep Reinforcement Learning, Motion Planning

The essence of quadrupeds' movements is the movement of the center of gravity, which has a pattern in the action of quadrupeds. However, the gait motion planning of the quadruped robot is time-consuming. Animals in nature can provide a large amount of gait information for robots to learn and imitate. Common methods learn animal posture with a motion capture system or numerous motion data points. In this paper, we propose a video imitation adaptation network (VIAN) that can imitate the action of animals and adapt it to the robot from a few seconds of video. The deep learning model extracts key points during animal motion from videos. The VIAN eliminates noise and extracts key information of motion with a motion adaptor, and then applies the extracted movements function as the motion pattern into deep reinforcement learning (DRL). To ensure similarity between the learning result and the animal motion in the video, we introduce rewards that are based on the consistency of the motion. DRL explores and learns to maintain balance from movement patterns from videos, imitates the action of animals, and eventually, allows the model to learn the gait or skills from short motion videos of different animals and to transfer the motion pattern to the real robot.