pwc-archive/papers-with-abstracts huggingface dataset:
Estimating $α$-Rank by Maximizing Information Gain
Authors: Tabish Rashid, Cheng Zhang, Kamil Ciosek
Date: 2021-01-22 00:00:00

Game theory has been increasingly applied in settings where the game is not known outright, but has to be estimated by sampling. For example, meta-games that arise in multi-agent evaluation can only be accessed by running a succession of expensive experiments that may involve simultaneous deployment of several agents. In this paper, we focus on $\alpha$-rank, a popular game-theoretic solution concept designed to perform well in such scenarios. We aim to estimate the $\alpha$-rank of the game using as few samples as possible. Our algorithm maximizes information gain between an epistemic belief over the $\alpha$-ranks and the observed payoff. This approach has two main benefits. First, it allows us to focus our sampling on the entries that matter the most for identifying the $\alpha$-rank. Second, the Bayesian formulation provides a facility to build in modeling assumptions by using a prior over game payoffs. We show the benefits of using information gain as compared to the confidence interval criterion of ResponseGraphUCB (Rowland et al. 2019), and provide theoretical results justifying our method.
Network Clustering for Multi-task Learning
Authors: Dehong Gao, Wenjing Yang, Huiling Zhou, Yi Wei, Yi Hu, Hao Wang
Date: 2021-01-22 00:00:00
Tasks: Clustering, Document Classification, Multi-Task Learning

The Multi-Task Learning (MTL) technique has been widely studied by word-wide researchers. The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task. Since the specific layers directly follow the hard layers, the MTL model needs to estimate this direct change (from general to specific) as well. To alleviate this problem, we introduce the novel cluster layer, which groups tasks into clusters during training procedures. In a cluster layer, the tasks in the same cluster are further required to share the same network. By this way, the cluster layer produces the general presentation for the same cluster, while produces relatively specific presentations for different clusters. As transitions the cluster layers are used between the hard layers and the specific layers. The MTL model thus learns general representations to specific representations gradually. We evaluate our model with MTL document classification and the results demonstrate the cluster layer is quite efficient in MTL.
Distance Estimation for BLE-based Contact Tracing -- A Measurement Study
Authors: Bernhard Etzlinger, Barbara Nußbaummüller, Philipp Peterseil, Karin Anna Hummel
Date: 2021-01-22 00:00:00

Mobile contact tracing apps are -- in principle -- a perfect aid to condemn the human-to-human spread of an infectious disease such as COVID-19 due to the wide use of smartphones worldwide. Yet, the unknown accuracy of contact estimation by wireless technologies hinders the broader use. We address this challenge by conducting a measurement study with a custom testbed to show the capabilities and limitations of Bluetooth Low Energy (BLE) in different scenarios. Distance estimation is based on interpreting the signal pathloss with a basic linear and a logarithmic model. Further, we compare our results with accurate ultra-wideband (UWB) distance measurements. While the results indicate that distance estimation by BLE is not accurate enough, a contact detector can detect contacts below 2.5 m with a true positive rate of 0.65 for the logarithmic and of 0.54 for the linear model. Further, the measurements reveal that multi-path signal propagation reduces the effect of body shielding and thus increases detection accuracy in indoor scenarios.
Impact of Response Latency on User Behaviour in Mobile Web Search
Authors: Ioannis Arapakis, Souneil Park, Martin Pielot
Date: 2021-01-22 00:00:00
Tasks: Information Retrieval, Retrieval

Traditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval community. However, an in-depth analysis of the interaction between the response latency and users' subjective search experience in the mobile setting has been missing so far. To address this gap, we conduct a controlled study that aims to reveal how response latency affects mobile web search. Our preliminary results indicate that mobile web search users are four times more tolerant to response latency reported for desktop web search users. However, when exceeding a certain threshold of 7-10 sec, the delays have a sizeable impact and users report feeling significantly more tensed, tired, terrible, frustrated and sluggish, all which contribute to a worse subjective user experience.
Misplaced trust? The relationship between trust, ability to identify commercially influenced results, and search engine preference
Authors: Sebastian Schultheiß, Dirk Lewandowski
Date: 2021-01-22 00:00:00

People have a high level of trust in search engines, especially Google, but only limited knowledge of them, as numerous studies have shown. This leads to the question: To what extent is this trust justified considering the lack of familiarity among users with how search engines work and the business models they are founded on? We assume that trust in Google, search engine preferences, and knowledge of result types are interrelated. To examine this assumption, we conducted a representative online survey with n = 2,012 German internet users. We show that users with little search engine knowledge are more likely to trust and use Google than users with more knowledge. A contradiction revealed itself - users strongly trust Google, yet they are unable to adequately evaluate search results. This may be problematic since it can potentially affect knowledge acquisition. Consequently, there is a need to promote user information literacy to create a more solid foundation for user trust in search engines.
My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking
Authors: Luis A. Leiva, Ioannis Arapakis, Costas Iordanou
Date: 2021-01-22 00:00:00

This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology. We demonstrate how straightforward is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. Based on our results, we propose an adversarial method to mitigate user profiling techniques that make use of mouse cursor tracking, such as the recurrent neural net we analyze in this paper. We also release our data and a web browser extension that implements our adversarial method, so that others can benefit from this work in practice.
LonelyText: A Short Messaging Based Classification of Loneliness
Authors: Mawulolo K. Ameko, Sonia Baee, Laura E. Barnes
Date: 2021-01-22 00:00:00
Tasks: Classification, General Classification

Loneliness does not only have emotional implications on a person but also on his/her well-being. The study of loneliness has been challenging and largely inconclusive in findings because of the several factors that might correlate to the phenomenon. We present one approach to predicting this event by discovering patterns of language associated with loneliness. Our results show insights and promising directions for mining text from instant messaging to predict loneliness.
Chemistry42: An AI-based platform for de novo molecular design
Authors: Yan A. Ivanenkov, Alex Zhebrak, Dmitry Bezrukov, Bogdan Zagribelnyy, Vladimir Aladinskiy, Daniil Polykovskiy, Evgeny Putin, Petrina Kamya, Alexander Aliper, Alex Zhavoronkov
Date: 2021-01-22 00:00:00
Tasks: Drug Discovery

Chemistry42 is a software platform for de novo small molecule design that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methods. Chemistry42 is unique in its ability to generate novel molecular structures with predefined properties validated through in vitro and in vivo studies. Chemistry42 is a core component of Insilico Medicine Pharma.ai drug discovery suite that also includes target discovery and multi-omics data analysis (PandaOmics) and clinical trial outcomes predictions (InClinico).
Computability-logic web: an alternative to deep learning
Authors: Keehang Kwon
Date: 2020-11-20 00:00:00
Tasks: Deep Learning

{\em Computability logic} (CoL) is a powerful, mathematically rigorous computational model. In this paper, we show that CoL-web, a web extension to CoL, naturally supports web programming where database updates are involved. To be specific, we discuss an implementation of the AI ATM based on CoL (CL9 to be exact). More importantly, we argue that CoL-web supports a general AI and, therefore, is a good alternative to neural nets and deep learning. We also discuss how to integrate neural nets into CoL-web.
Sequential operators in computability logic
Authors: Giorgi Japaridze
Date: 2007-12-09 00:00:00
Tasks: Negation

Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html) is a semantical platform and research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for (interactive) computational problems, understood as games between a machine and its environment; logical operators represent operations on such entities; and "truth" is understood as existence of an effective solution, i.e., of an algorithmic winning strategy. The formalism of CL is open-ended, and may undergo series of extensions as the study of the subject advances. The main groups of operators on which CL has been focused so far are the parallel, choice, branching, and blind operators. The present paper introduces a new important group of operators, called sequential. The latter come in the form of sequential conjunction and disjunction, sequential quantifiers, and sequential recurrences. As the name may suggest, the algorithmic intuitions associated with this group are those of sequential computations, as opposed to the intuitions of parallel computations associated with the parallel group of operations: playing a sequential combination of games means playing its components in a sequential fashion, one after one. The main technical result of the present paper is a sound and complete axiomatization of the propositional fragment of computability logic whose vocabulary, together with negation, includes all three -- parallel, choice and sequential -- sorts of conjunction and disjunction. An extension of this result to the first-order level is also outlined.
Deepfakes and the 2020 US elections: what (did not) happen
Authors: João Paulo Meneses
Date: 2021-01-22 00:00:00

Alarmed by the volume of disinformation that was assumed to have taken place during the 2016 US elections, scholars, politics and journalists predicted the worst when the first deepfakes began to emerge in 2018. After all, US Elections 2020 were believed to be the most secure in American history. This paper seeks explanations for an apparent contradiction: we believe that it was precisely the multiplication and conjugation of different types of warnings and fears that created the conditions that prevented malicious political deepfakes from affecting the 2020 US elections. From these warnings, we identified four factors (more active role of social networks, new laws, difficulties in accessing Artificial Intelligence and better awareness of society). But while this formula has proven to be effective in the case of the United States, 2020, it is not correct to assume that it can be repeated in other political contexts.
It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports
Authors: Nathan Cooper, Carlos Bernal-Cárdenas, Oscar Chaparro, Kevin Moran, Denys Poshyvanyk
Date: 2021-01-22 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Retrieval, Text Retrieval

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality.
Joint Partial Optimal Transport for Open Set Domain Adaptation
Authors: Renjun Xu, Pelen Liu, Yin Zhang, Fang Cai, Jindong Wang, Shuoying Liang, Heting Ying, Jianwei Yin
Date: 2020-07-11 00:00:00
Tasks: Domain Adaptation

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.
Contact-mediated cellular communication supplements positional information to regulate spatial patterning during development
Authors: Chandrashekar Kuyyamudi, Shakti N. Menon, Sitabhra Sinha
Date: 2021-01-22 00:00:00

Development in multi-cellular organisms is marked by a high degree of spatial organization of the cells attaining distinct fates in the embryo. We show that receptor-ligand interaction between cells in close physical proximity adaptively regulates the local process of selective gene expression in the presence of a global field set up by a diffusing morphogen that provides positional cues. This allows information from the cellular neighborhood to be incorporated into the emergent thresholds of morphogen concentration that dictate cell fate, consistent with recent experiments.
SUTRA: A Novel Approach to Modelling Pandemics with Applications to COVID-19
Authors: Manindra Agrawal, Madhuri Kanitkar, Deepu Phillip, Tanima Hajra, Arti Singh, Avaneesh Singh, Prabal Pratap Singh, Mathukumalli Vidyasagar
Date: 2021-01-22 00:00:00

The Covid-19 pandemic has two key properties: (i) asymptomatic cases (both detected and undetected) that can result in new infections, and (ii) time-varying characteristics due to new variants, Non-Pharmaceutical Interventions etc. We develop a model called SUTRA (Susceptible, Undetected though infected, Tested positive, and Removed Analysis) that takes into account both of these two key properties. While applying the model to a region, two parameters of the model can be learnt from the number of daily new cases found in the region. Using the learnt values of the parameters the model can predict the number of daily new cases so long as the learnt parameters do not change substantially. Whenever any of the two parameters changes due to the key property (ii) above, the SUTRA model can detect that the values of one or both of the parameters have changed. Further, the model has the capability to relearn the changed parameter values, and then use these to carry out the prediction of the trajectory of the pandemic for the region of concern. The SUTRA approach can be applied at various levels of granularity, from an entire country to a district, more specifically, to any large enough region for which the data of daily new cases are available. We have applied the SUTRA model to thirty-two countries, covering more than half of the world's population. Our conclusions are: (i) The model is able to capture the past trajectories very well. Moreover, the parameter values, which we can estimate robustly, help quantify the impact of changes in the pandemic characteristics. (ii) Unless the pandemic characteristics change significantly, the model has good predictive capability. (iii) Natural immunity provides significantly better protection against infection than the currently available vaccines.
Synthesis of High‐purity Solid SiO2 Nanodumbbells Via Induced Aggregation for Levitated Optomechanics
Authors: Yu-Hao Deng
Date: 2021-01-25 00:00:00

Optically levitated nanodumbbells in vacuum are excellent candidates for thermodynamics, macroscopic quantum mechanics, precision measurements and quantum sensing. Silica (SiO2) material, with extremely low absorption of near-infrared light and super mechanical strength, has been the most potential material for optically levitated systems. Here we synthesize high-purity solid SiO2 nanodumbbells via Stöber method by introducing acetone for the induced aggregation of SiO2 nanospheres. The nanodumbbells show high uniformity and their sizes are tunable. Previous experimental results demonstrated that the synthetic nanodumbbells can be applied in GHz nanomechanical rotors and can withstand the tensile strength of over 13 GPa. This work supports batch production and high yield of SiO2 nanodumbbells, which engineers a new material platform to advance levitated optomechanics.

https://link.springer.com/article/10.1007/s12633-021-00952-5
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks
Authors: Aaron B. Buhendwa, Stefan Adami, Nikolaus A. Adams
Date: 2021-01-25 00:00:00
Tasks: Position

In this work, physics-informed neural networks are applied to incompressible two-phase flow problems. We investigate the forward problem, where the governing equations are solved from initial and boundary conditions, as well as the inverse problem, where continuous velocity and pressure fields are inferred from scattered-time data on the interface position. We employ a volume of fluid approach, i.e. the auxiliary variable here is the volume fraction of the fluids within each phase. For the forward problem, we solve the two-phase Couette and Poiseuille flow. For the inverse problem, three classical test cases for two-phase modeling are investigated: (i) drop in a shear flow, (ii) oscillating drop and (iii) rising bubble. Data of the interface position over time is generated by numerical simulation. An effective way to distribute spatial training points to fit the interface, i.e. the volume fraction field, and the residual points is proposed. Furthermore, we show that appropriate weighting of losses associated with the residual of the partial differential equations is crucial for successful training. The benefit of using adaptive activation functions is evaluated for both the forward and inverse problem.
High‐purity Solid SiO2 Nanodumbbells
Authors: Silicon
Date: 2021-01-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Abstract：Optically levitated nanodumbbells in vacuum are excellent candidates for thermodynamics, macroscopic quantum mechanics, precision measurements and quantum sensing. Silica (SiO2) material, with extremely low absorption of near-infrared light and super mechanical strength, has been the most potential material for optically levitated systems. Here we synthesize high-purity solid SiO2 nanodumbbells via Stöber method by introducing acetone for the induced aggregation of SiO2 nanospheres. The nanodumbbells show high uniformity and their sizes are tunable. Previous experimental results demonstrated that the synthetic nanodumbbells can be applied in GHz nanomechanical rotors and can withstand the tensile strength of over 13 GPa. This work supports batch production and high yield of SiO2 nanodumbbells, which engineers a new material platform to advance levitated optomechanics.
【Cite this article： Deng, YH. Synthesis of High‐purity Solid SiO2 Nanodumbbells Via Induced Aggregation for Levitated Optomechanics. Silicon (2021). https://doi.org/10.1007/s12633-021-00952-5 】
High‐purity Solid SiO2 Nanodumbbells for Levitated Optomechanics
Authors: Silicon (2021)
Date: 2021-01-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Optically levitated nanodumbbells in vacuum are excellent candidates for thermodynamics, macroscopic quantum mechanics, precision measurements and quantum sensing. Silica (SiO2) material, with extremely low absorption of near-infrared light and super mechanical strength, has been the most potential material for optically levitated systems. Here we synthesize high-purity solid SiO2 nanodumbbells via Stöber method by introducing acetone for the induced aggregation of SiO2 nanospheres. The nanodumbbells show high uniformity and their sizes are tunable. Previous experimental results demonstrated that the synthetic nanodumbbells can be applied in GHz nanomechanical rotors and can withstand the tensile strength of over 13 GPa. This work supports batch production and high yield of SiO2 nanodumbbells, which engineers a new material platform to advance levitated optomechanics.