pwc-archive/papers-with-abstracts huggingface dataset:
Bivariate vine copula based regression, bivariate level and quantile curves
Authors: Marija Tepegjozova, Claudia Czado
Date: 2022-05-05 00:00:00
Tasks: regression, valid

The statistical analysis of univariate quantiles is a well developed research topic. However, there is a need for research in multivariate quantiles. We construct bivariate (conditional) quantiles using the level curves of vine copula based bivariate regression model. Vine copulas are graph theoretical models identified by a sequence of linked trees, which allow for separate modelling of marginal distributions and the dependence structure. We introduce a novel graph structure model (given by a tree sequence) specifically designed for a symmetric treatment of two responses in a predictive regression setting. We establish computational tractability of the model and a straight forward way of obtaining different conditional distributions. Using vine copulas the typical shortfalls of regression, as the need for transformations or interactions of predictors, collinearity or quantile crossings are avoided. We illustrate the copula based bivariate level curves for different copula distributions and show how they can be adjusted to form valid quantile curves. We apply our approach to weather measurements from Seoul, Korea. This data example emphasizes the benefits of the joint bivariate response modelling in contrast to two separate univariate regressions or by assuming conditional independence, for bivariate response data set in the presence of conditional dependence.
A Structured Analysis of Journalistic Evaluations for News Source Reliability
Authors: Manuel Pratelli, Marinella Petrocchi
Date: 2022-05-05 00:00:00
Tasks: Misinformation

In today's era of information disorder, many organizations are moving to verify the veracity of news published on the web and social media. In particular, some agencies are exploring the world of online media and, through a largely manual process, ranking the credibility and transparency of news sources around the world. In this paper, we evaluate two procedures for assessing the risk of online media exposing their readers to m/disinformation. The procedures have been dictated by NewsGuard and The Global Disinformation Index, two well-known organizations combating d/misinformation via practices of good journalism. Specifically, considering a fixed set of media outlets, we examine how many of them were rated equally by the two procedures, and which aspects led to disagreement in the assessment. The result of our analysis shows a good degree of agreement, which in our opinion has a double value: it fortifies the correctness of the procedures and lays the groundwork for their automation.
Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings
Authors: Georgios Sidiropoulos, Evangelos Kanoulas
Date: 2022-05-04 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Open-Domain Question Answering, Passage Ranking, Question Answering, Retrieval

Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the user question. We observe a significant drop in the performance of the dual-encoder model when encountering typos and explore ways to improve its robustness by combining data augmentation with contrastive learning. Our experiments on two large-scale passage ranking and open-domain question answering datasets show that our proposed approach outperforms competing approaches. Additionally, we perform a thorough analysis on robustness. Finally, we provide insights on how different typos affect the robustness of embeddings differently and how our method alleviates the effect of some typos but not of others.
Which French speech recognition system for assistant robots?
Authors: Wiam FADEL, Imane ARAF, Toumi BOUCHENTOUF, Pierre-André BUVET, François BOURZEIX, Omar BOURJA
Date: 2022-03-04 00:00:00
Proceeding: IEEE 2022 3
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

Artificial intelligence-based speech recognition systems are already available and capable of recognizing the French language. Still, it is quite time-consuming to compare which one will be effective for an assistant robot. The study aims to select the best French-language speech recognition system with the least error in a real environment. In this paper, we present related works on how an Automatic Speech Recognition (ASR) system works, the models used by each of its components, several open-source French datasets, and the frequently used evaluation techniques. Next, we compare deep learning-based speech recognition APIs and pre-trained models for French on two different datasets using the Word Error Rate (WER) metric. The experimental results reveal that Google’s Speech-to-Text API outperforms the other systems, namely VOSK API, Wav2vec 2.0, QuartzNet, and Speech Brain’s Convolutional, Recurrent, and Fully-connected Networks (CRDNN) model.
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
Authors: Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan, Viktor K. Prasanna
Date: 2022-05-01 00:00:00
Tasks: Prediction, Segmentation

Machine learning algorithms have shown potential to improve prefetching performance by accurately predicting future memory accesses. Existing approaches are based on the modeling of text prediction, considering prefetching as a classification problem for sequence prediction. However, the vast and sparse memory address space leads to large vocabulary, which makes this modeling impractical. The number and order of outputs for multiple cache line prefetching are also fundamentally different from text prediction. We propose TransFetch, a novel way to model prefetching. To reduce vocabulary size, we use fine-grained address segmentation as input. To predict unordered sets of future addresses, we use delta bitmaps for multiple outputs. We apply an attention-based network to learn the mapping between input and output. Prediction experiments demonstrate that address segmentation achieves 26% - 36% higher F1-score than delta inputs and 15% - 24% higher F1-score than page & offset inputs for SPEC 2006, SPEC 2017, and GAP benchmarks. Simulation results show that TransFetch achieves 38.75% IPC improvement compared with no prefetching, outperforming the best-performing rule-based prefetcher BOP by 10.44%, and ML-based prefetcher Voyager by 6.64%.
Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing
Authors: Sahraoui Dhelim, Tahar Kechadi, Liming Chen, Nyothiri Aung, Huansheng Ning, Luigi Atzori
Date: 2022-04-13 00:00:00
Tasks: Distributed Computing, Edge-computing, Navigate

The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one. State-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. Such centralized design is unfavorable as it suffers from several drawbacks caused by the long latency required for cloud access, such as low quality visualization. To solve this issue, in this paper, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm, which makes use of edge devices computing power to fulfil the required computational cost for heavy tasks such as collision detection in virtual universe and computation of 3D physics in virtual simulation. The computational cost related to an entity in the Metaverse such as collision detection or physics emulation are performed at the end-device of the associated physical entity. To prove the effectiveness of the proposed architecture, we simulate a distributed social metaverse application. Simulation results shows that the proposed architecture can reduce the latency by 50% when compared with the legacy cloud-based Metaverse applications.
Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE
Authors: Najem N Sirhan, Manel Martınez-Ramon
Date: 2022-04-16 00:00:00
Tasks: Scheduling

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.
Stochastic Relative Degree and Path-wise Control of Nonlinear Stochastic Systems
Authors: Alberto Mellone, Giordano Scarciotti
Date: 2022-05-05 00:00:00

We address the path-wise control of systems described by a set of nonlinear stochastic differential equations. For this class of systems, we introduce a notion of stochastic relative degree and a change of coordinates which transforms the dynamics to a stochastic normal form. The normal form is instrumental for the design of a state-feedback control which linearises and makes the dynamics deterministic. We observe that this control is idealistic, i.e. it is not practically implementable because it employs a feedback of the Brownian motion (which is never available) to cancel the noise. Using the idealistic control as a starting point, we introduce a hybrid control architecture which achieves \emph{practical} path-wise control. This hybrid controller uses measurements of the state to perform periodic compensations for the noise contribution to the dynamics. We prove that the hybrid controller retrieves the idealistic performances in the limit as the compensating period approaches zero. We address the problem of asymptotic output tracking, solving it in the idealistic and in the practical framework. We finally validate the theory by means of a numerical example.
A Market for Trading Forecasts: A Wagering Mechanism
Authors: Aitazaz Ali Raja, Pierre Pinson, Jalal Kazempour, Sergio Grammatico
Date: 2022-05-05 00:00:00

In many areas of industry and society, e.g., energy, healthcare, logistics, agents collect vast amounts of data that they deem proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, we envision a platform for improving predictions via implicit pooling of private information in return for possible remuneration. Specifically, we design a wagering-based forecast elicitation market platform, where a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. We propose a payoff mechanism and prove that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, we discuss the properties of the forecast aggregation operator and scoring rules to emphasize their effect on the sellers' payoff. Finally, we provide numerical examples to illustrate the structure and properties of the proposed market platform.
On inherent limitations in robustness and performance for a class of prescribed-time algorithms
Authors: Rodrigo Aldana-López, Richard Seeber, Hernan Haimovich, David Gómez-Gutiérrez
Date: 2022-05-05 00:00:00

Prescribed-time algorithms based on time-varying gains may have remarkable properties, such as regulation in a user-prescribed finite time that is the same for every nonzero initial condition and that holds even under matched disturbances. However, at the same time, such algorithms are known to lack robustness to measurement noise. This note shows that the lack of robustness of a class of prescribed-time algorithms is of an extreme form. Specifically, we show the existence of arbitrarily small measurement noises causing considerable deviations, divergence, and other detrimental consequences. We also discuss some drawbacks and trade-offs of existing workarounds as motivation for further analysis.
Supervisor Obfuscation Against Covert Actuator Attackers
Authors: Ruochen Tai, Liyong Lin, Rong Su
Date: 2022-05-05 00:00:00

This work investigates the problem of synthesizing obfuscated supervisors against covert actuator attackers. For a non-resilient supervisor S, for which there exist some covert actuator attackers that are capable of inflicting damage, we propose an algorithm to compute all the obfuscated supervisors, with the requirements that: 1) any obfuscated supervisor S' is resilient against any covert actuator attacker, and 2) the original closed-behavior of the closed-loop system under S is preserved, that is, any obfuscated supervisor S' is control equivalent to the original non-resilient supervisor S. We prove that the designed algorithm to synthesize obfuscated supervisors against covert actuator attack is sound and complete.
Detection of signaling mechanisms from cellular responses to multiple cues
Authors: Soutick Saha, Hye-ran Moon, Bumsoo Han, Andrew Mugler
Date: 2022-05-05 00:00:00

Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to {deduce} the minimal signaling network. We focus on cells' response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to {deduce} molecular mechanisms from cell-level data.
Phenomenology and dynamics of competitive ecosystems beyond the niche-neutral regimes
Authors: Nava Leibovich, Jeremy Rothschild, Sidhartha Goyal, Anton Zilman
Date: 2022-05-05 00:00:00
Tasks: Diversity

Structure, composition and stability of ecological populations are shaped by the inter- and intra-species interactions within these communities. It remains to be fully understood how the interplay of these interactions with other factors, such as immigration, control the structure, diversity and the long term stability of ecological systems in the presence of noise and fluctuations. We address this problem using a minimal model of interacting multi-species ecological communities that incorporates competition, immigration and demographic noise. We find that the complete phase diagram exhibits rich behavior with multiple regimes that go beyond the classical 'niche' and 'neutral' regimes, extending and modifying the 'neutral-like' or 'niche-like' dichotomy. In particular, we observe novel regimes that cannot be characterized as either 'niche' or 'neutral' where a multimodal species abundance distribution is observed. We characterize the transitions between the different regimes and show how they arise from the underlying kinetics of the species turnover, extinction and invasion. Our model serves as a minimal null model of noisy competitive ecological systems, against which more complex models that include factors such as mutations and environmental noise can be compared.
Compressed AFM-IR hyperspectral nanoimaging of single Leishmania parasites
Authors: A. Hornemann, M. Marschall, S. Metzner, P. Patoka, S. Cortes, G. Wübbeler, A. Hoehl, E. Rühl, B. Kästner, C. Elster
Date: 2022-05-04 00:00:00

Infrared hyperspectral imaging is a powerful approach in the field of materials and life sciences. However, the extension to modern sub-diffraction nanoimaging still remains a highly inefficient technique, as it acquires data via inherent sequential schemes. Here, we introduce the mathematical technique of low-rank matrix reconstruction to the sub-diffraction scheme of atomic force microscopy-based infrared spectroscopy (AFM-IR), for efficient hyperspectral infrared nanoimaging. To demonstrate its application potential, we chose the trypanosomatid unicellular parasites Leishmania species as a realistic target of biological importance. The mid-infrared spectral fingerprint window covering the spectral range from 1300 to 1900 cm$^{-1}$ was chosen and a step width of 220 nm was applied for nanoimaging of single parasites. Multivariate statistics approaches such as hierarchical cluster analysis (HCA) were used for extracting the chemically distinct spatial locations. Subsequently, we randomly selected only 5% from an originally gathered data cube of 134 (x) $\times$ 50 (y) $\times$ 148 (spectral) AFM-IR measurements and reconstructed the full data set by low-rank matrix recovery. The technique is evaluated by showing agreement in the cluster regions between full and reconstructed data cubes. We conclude that the corresponding measurement time of more than 14 hours can be reduced to less than 1 hour. These findings can significantly boost the practical applicability of hyperspectral nanoimaging in both academic and industrial settings involving nano- and bio-materials.
Temporally and Spatially variant-resolution illumination patterns in computational ghost imaging
Authors: Dong Zhou, Jie Cao, Huan Cui, Li-Xing Lin, Haoyu Zhang, Yingqiang Zhang, Qun Hao
Date: 2022-05-05 00:00:00

Conventional computational ghost imaging (CGI) uses light carrying a sequence of patterns with uniform-resolution to illuminate the object, then performs correlation calculation based on the light intensity value reflected by the target and the preset patterns to obtain object image. It requires a large number of measurements to obtain high-quality images, especially if high-resolution images are to be obtained. To solve this problem, we developed temporally variable-resolution illumination patterns, replacing the conventional uniform-resolution illumination patterns with a sequence of patterns of different imaging resolutions. In addition, we propose to combine temporally variable-resolution illumination patterns and spatially variable-resolution structure to develop temporally and spatially variable-resolution (TSV) illumination patterns, which not only improve the imaging quality of the region of interest (ROI) but also improve the robustness to noise. The methods using proposed illumination patterns are verified by simulations and experiments compared with CGI. For the same number of measurements, the method using temporally variable-resolution illumination patterns has better imaging quality than CGI, but it is less robust to noise. The method using TSV illumination patterns has better imaging quality in ROI than the method using temporally variable-resolution illumination patterns and CGI under the same number of measurements. We also experimentally verify that the method using TSV patterns have better imaging performance when applied to higher resolution imaging. The proposed methods are expected to solve the current computational ghost imaging that is difficult to achieve high-resolution and high-quality imaging.
Choosing Exogeneity Assumptions in Potential Outcome Models
Authors: Matthew A. Masten, Alexandre Poirier
Date: 2022-05-04 00:00:00

There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen based on the kind of selection on unobservables it allows. Consequently, researchers can assess the plausibility of any exogeneity assumption by studying the distributions of treatment given the unobservables that are consistent with that assumption. We use this approach to study two common exogeneity assumptions: quantile and mean independence. We show that both assumptions require a kind of non-monotonic relationship between treatment and the potential outcomes. We discuss how to assess the plausibility of this kind of treatment selection. We also show how to define a new and weaker version of quantile independence that allows for monotonic treatment selection. We then show the implications of the choice of exogeneity assumption for identification. We apply these results in an empirical illustration of the effect of child soldiering on wages.
Measures of physical mixing evaluate the economic mobility of the typical individual
Authors: Viktor Stojkoski
Date: 2022-05-05 00:00:00

Measures of economic mobility represent aggregate values for how individual wealth changes over time. As such, these measures may not describe the feasibility of a typical individual to change their wealth. To address this limitation, we introduce mixing, a concept from statistical physics, as a relevant phenomenon for quantifying how individuals move across the wealth distribution. We display the relationship between mixing and mobility both theoretically and using data. By studying the properties of an established model of wealth dynamics, we show that some individuals can move across the distribution when wealth is a non-mixing observable. Only in the mixing case every individual is able to move across the whole wealth distribution. There is also a direct equivalence between measures of mixing and the magnitude of the standard measures of economic mobility, but the opposite is not true. We then describe an empirical method for estimating the mixing properties of wealth dynamics in practice. We use this method to present a pedagogical application using the USA longitudinal data. This, approach, even though limited in data availability, leads to results suggesting that wealth in the USA is either non-mixing or that it takes a very long time for the individuals to mix within the distribution. These results showcase how mixing can be used in tandem with measures of mobility for drawing conclusions about the extent of mobility across the whole distribution.