pwc-archive/papers-with-abstracts huggingface dataset:
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks
Authors: Jan Aalmoes, Vasisht Duddu, Antoine Boutet
Date: 2022-02-04 00:00:00
Tasks: Attribute, Fairness, Inference Attack

Machine learning (ML) models have been deployed for high-stakes applications. Due to class imbalance in the sensitive attribute observed in the datasets, ML models are unfair on minority subgroups identified by a sensitive attribute, such as race and sex. In-processing fairness algorithms ensure model predictions are independent of sensitive attribute. Furthermore, ML models are vulnerable to attribute inference attacks where an adversary can identify the values of sensitive attribute by exploiting their distinguishable model predictions. Despite privacy and fairness being important pillars of trustworthy ML, the privacy risk introduced by fairness algorithms with respect to attribute leakage has not been studied. We identify attribute inference attacks as an effective measure for auditing blackbox fairness algorithms to enable model builder to account for privacy and fairness in the model design. We proposed Dikaios, a privacy auditing tool for fairness algorithms for model builders which leveraged a new effective attribute inference attack that account for the class imbalance in sensitive attributes through an adaptive prediction threshold. We evaluated Dikaios to perform a privacy audit of two in-processing fairness algorithms over five datasets. We show that our attribute inference attacks with adaptive prediction threshold significantly outperform prior attacks. We highlighted the limitations of in-processing fairness algorithms to ensure indistinguishable predictions across different values of sensitive attributes. Indeed, the attribute privacy risk of these in-processing fairness schemes is highly variable according to the proportion of the sensitive attributes in the dataset. This unpredictable effect of fairness mechanisms on the attribute privacy risk is an important limitation on their utilization which has to be accounted by the model builder.
Polynomial convergence of iterations of certain random operators in Hilbert space
Authors: Soumyadip Ghosh, Yingdong Lu, Tomasz J. Nowicki
Date: 2022-02-04 00:00:00
Tasks: regression

We study the convergence of a random iterative sequence of a family of operators on infinite dimensional Hilbert spaces, inspired by the Stochastic Gradient Descent (SGD) algorithm in the case of the noiseless regression, as studied in [1]. We identify conditions that are strictly broader than previously known for polynomial convergence rate in various norms, and characterize the roles the randomness plays in determining the best multiplicative constants. Additionally, we prove almost sure convergence of the sequence.
Rediscovering orbital mechanics with machine learning
Authors: Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
Date: 2022-02-04 00:00:00
Tasks: BIG-bench Machine Learning, Graph Neural Network, scientific discovery, Symbolic Regression

We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery.
A Bibliometric Perspective of Social Science Scientific Communities of Pakistan and India
Authors: Sami Ul-Haq, Saeed-Ul Hassan
Date: 2022-02-03 00:00:00

In this study, we use research publication data from the field of social science to identify collaboration networks among social science research communities of India and Pakistan. We have used Scopus database to extract information of social science journals for both countries India and Pakistan. Study of this data is significant as both countries have common social issues and many of common social values. Keywords analysis has been done to see common research areas in both communities like poverty, education, the issue of gender etc. Despite having many of the common social issues, collaboration among social science research communities of both countries is not strong.
mTrust: Discerning Multi-Faceted Trust in a Connected World
Authors: Jiliang Tang, Huiji Gao, Huan Liu
Date: 2012-02-08 00:00:00
Proceeding: WSDM 2012 2

Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images
Authors: Zhang, Jing; Caroline Petitjean; and Samia Ainouz
Date: 2022-01-25 00:00:00
Proceeding: Journal of Imaging 2022 1
Tasks: 2D Semantic Segmentation, Computational Efficiency, regression, Segmentation

The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest. Another possibility is to estimate directly the HC with a regression network. Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate. This observation motivates the present study, where we propose a fair, quantitative comparison of segmentation-based and segmentation-free (i.e., regression) approaches to estimate how far regression-based approaches stand from segmentation approaches. We experiment various convolutional neural networks (CNN) architectures and backbones for both segmentation and regression models and provide estimation results on the HC18 dataset, as well agreement analysis, to support our findings. We also investigate memory usage and computational efficiency to compare both types of approaches. The experimental results demonstrate that even if segmentation-based approaches deliver the most accurate results, regression CNN approaches are actually learning to find prominent features, leading to promising yet improvable HC estimation results.
Combining deep and unsupervised features for multilingual speech emotion recognition
Authors: Vincenzo Scotti, Federico Galati, Licia Sbattella, Roberto Tedesco
Date: 2021-01-10 00:00:00
Proceeding: International Workshop on pattern recognition for positive teChnology And eldeRly wEllbeing (CARE) 2021 1
Tasks: Emotion Recognition, Multimodal Emotion Recognition

In this paper we present a Convolutional Neural Network for multilingual emotion recognition from spoken sentences. The purpose of this work was to build a model capable of recognising emotions combining textual and acoustic information compatible with multiple languages. The model we derive has an end-to-end deep architecture, hence it takes raw text and audio data and uses convolutional layers to extract a hierarchy of classification features. Moreover, we show how the trained model achieves good performances in different languages thanks to the usage of multilingual unsupervised textual features. As an additional remark, it is worth to mention that our solution does not require text and audio to be word- or phoneme-aligned. The proposed model, PATHOSnet, was trained and evaluated on multiple corpora with different spoken languages (IEMOCAP, EmoFilm, SES and AESI). Before training, we tuned the hyper-parameters solely on the IEMOCAP corpus, which offers realistic audio recording and transcription of sentences with emotional content in English. The final model turned out to provide state-of-the-art performances on some of the selected data sets on the four considered emotions.
Drivers and impacts of changes in China’s drylands
Authors: Changjia Li, Bojie Fu, Shuai Wang, Lindsay C. Stringer, Yaping Wang, Zidong Li, Yanxu Liu, WenXin Zhou
Date: 2021-11-09 00:00:00
Proceeding: Nature Reviews Earth & Environment 2021 11

China has 6.6 million km2 of drylands that support approximately 580 million people. These drylands are at risk of desertification. In this Review, the changes observed in China’s drylands are synthesized, with a focus on their drivers and the effects of 13 large-scale land conservation and restoration programmes aimed at mitigating them, including the Three-North Shelterbelt Development Program and Grain for Green Program. After the implementation of the first large-scale restoration programme in 1978, 45.76% of China’s drylands experienced statistically significant land improvement or vegetation greenness, as identified by the Normalized Difference Vegetation Index. However, activities associated with restoration and conservation projects, such as afforestation, also impose substantial water pressure. Desertification thus remained prevalent during 1980–2015, with 11.43% drylands (especially in north-eastern and north-western drylands) experiencing land degradation or vegetation brownness. Drylands remain at risk of expansion owing to increasing aridity, particularly in semi-arid areas. Future trade-offs between the effects of CO2 fertilization and increased aridity on dryland vegetation cover are still poorly understood. Long-term experiments on the interactions between physical–chemical–biological processes across spatial and temporal scales, such as large-scale field surveys using standardized protocols, are needed to better manage drylands in China and globally.
An Overview of Ultra-WideBand (UWB) Standards and Organizations (IEEE 802.15.4, FiRa, Apple): Interoperability Aspects and Future Research Directions
Authors: Dieter Coppens, Adnan Shahid, Sam Lemey, Ben Van Herbruggen Chris Marshall, Eli de Poorter
Date: 2022-02-04 00:00:00

The increasing popularity of ultra-wideband (UWB) technology for location-based services, such as access control and real-time indoor track&tracing, as well as UWB support in new consumer devices such as smartphones, has resulted in the availability of multiple new UWB radio chips. However, due to this increase in UWB device availability, the question of which (industry) standards and configuration factors impact UWB interoperability and compatibility becomes increasingly important. In this paper, the fundamentals of UWB compatibility are investigated by first giving an overview of different UWB radio chips on the market. After that, an overview of UWB standards and organizations is given. Next, this overview is used to discuss the focus of these different standards and to identify the differences between them. We describe compatibility issues and associated interoperability aspects related to physical (PHY), medium-access-control (MAC) and upper layers. For the PHY layer, compatibility is possible for all UWB radio chips if the correct settings are configured. For the MAC layer, the implementation of the multiple access scheme as well as the localization technique is mostly proprietary. For the device discovery, several standards are currently being drafted. Finally, future challenges related to UWB interoperability are discussed.
Proofs and Supplementary Material: Unified Characterization and Precoding for Non-Stationary Channels
Authors: Zhibin Zou, Maqsood Careem, Aveek Dutta, Ngwe Thawdar
Date: 2022-02-03 00:00:00
Tasks: LEMMA

This document provides the supplementary material including a comprehensive related work, the complete proofs and extended evaluation results that support the manuscript, "Unified Characterization and Precoding for Non-Stationary Channels", that was accepted for publication at IEEE International Conference on Communications (ICC) 2022. Equations (1)--(34) refer to the equations from the main manuscript, and the Theorem, Lemma and Corollaries correspond to those from the manuscript.
On the velocity-stress formulation for geometrically nonlinear elastodynamics and its structure-preserving discretization
Authors: Tobias Thoma, Paul Kotyczka, Herbert Egger
Date: 2022-02-04 00:00:00

We consider the dynamics of an elastic continuum under large deformation but small strain. Such systems can be described by the equations of geometrically nonlinear elastodynamics in combination with the St. Venant-Kirchhoff material law. The velocity-stress formulation of the problem turns out to have a formal port-Hamiltonian structure. In contrast to the linear case, the operators of the problem are modulated by the displacement field which can be handled as a passive variable and integrated along with the velocities. A weak formulation of the problem is derived and essential boundary conditions are incorporated via Lagrange multipliers. This variational formulation explicitly encodes the transfer between kinetic and potential energy in the interior as well as across the boundary, thus leading to a global power balance and ensuring passivity of the system. The particular geometric structure of the weak formulation can be preserved under Galerkin approximation via appropriate mixed finite elements. In addition, a fully discrete power balance can be obtained by appropriate time discretization. The main properties of the system and its discretization are shown theoretically and demonstrated by numerical tests.
Uncertainty in fMRI Functional Networks of Autism Brain Imaging Data
Authors: Amin Kaveh, Matteo Magnani, Christian Rohner
Date: 2022-02-04 00:00:00

In this paper we review the preprocessing pipeline through which fMRI data is transformed into a network. We discuss three parameters that mostly affect our understanding of the existence of functional correlations between the brain regions. In the end, we conclude that the existence of functional correlations between pairs of the brain's regions can be modeled with probabilistic edges, not to lose the uncertainty that is inherent in the network generation process.
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
Authors: Naijun Zheng, Na Li, Xixin Wu, Lingwei Meng, Jiawen Kang, Haibin Wu, Chao Weng, Dan Su, Helen Meng
Date: 2022-02-04 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

This paper describes our speaker diarization system submitted to the Multi-channel Multi-party Meeting Transcription (M2MeT) challenge, where Mandarin meeting data were recorded in multi-channel format for diarization and automatic speech recognition (ASR) tasks. In these meeting scenarios, the uncertainty of the speaker number and the high ratio of overlapped speech present great challenges for diarization. Based on the assumption that there is valuable complementary information between acoustic features, spatial-related and speaker-related features, we propose a multi-level feature fusion mechanism based target-speaker voice activity detection (FFM-TS-VAD) system to improve the performance of the conventional TS-VAD system. Furthermore, we propose a data augmentation method during training to improve the system robustness when the angular difference between two speakers is relatively small. We provide comparisons for different sub-systems we used in M2MeT challenge. Our submission is a fusion of several sub-systems and ranks second in the diarization task.
From Semi-Infinite Constraints to Structured Robust Policies: Optimal Gain Selection for Financial Systems
Authors: Chung-Han Hsieh
Date: 2022-02-04 00:00:00

This paper studies the robust optimal gain selection problem for financial trading systems, formulated within a \emph{double linear policy} framework, which allocates capital across long and short positions. The key objective is to guarantee \emph{robust positive expected} (RPE) profits uniformly across a range of uncertain market conditions while ensuring risk control. This problem leads to a robust optimization formulation with \emph{semi-infinite} constraints, where the uncertainty is modeled by a bounded set of possible return parameters. We address this by transforming semi-infinite constraints into structured policies -- the \emph{balanced} policy and the \emph{complementary} policy -- which enable explicit characterization of the optimal solution. Additionally, we propose a novel graphical approach to efficiently solve the robust gain selection problem, drastically reducing computational complexity. Empirical validation on historical stock price data demonstrates superior performance in terms of risk-adjusted returns and downside risk compared to conventional strategies. This framework generalizes classical mean-variance optimization by incorporating robustness considerations, offering a systematic and efficient solution for robust trading under uncertainty.
Sparse Polynomial Optimisation for Neural Network Verification
Authors: Matthew Newton, Antonis Papachristodoulou
Date: 2022-02-04 00:00:00

The prevalence of neural networks in society is expanding at an increasing rate. It is becoming clear that providing robust guarantees on systems that use neural networks is very important, especially in safety-critical applications. A trained neural network's sensitivity to adversarial attacks is one of its greatest shortcomings. To provide robust guarantees, one popular method that has seen success is to bound the activation functions using equality and inequality constraints. However, there are numerous ways to form these bounds, providing a trade-off between conservativeness and complexity. Depending on the complexity of these bounds, the computational time of the optimisation problem varies, with longer solve times often leading to tighter bounds. We approach the problem from a different perspective, using sparse polynomial optimisation theory and the Positivstellensatz, which derives from the field of real algebraic geometry. The former exploits the natural cascading structure of the neural network using ideas from chordal sparsity while the later asserts the emptiness of a semi-algebraic set with a nested family of tests of non-decreasing accuracy to provide tight bounds. We show that bounds can be tightened significantly, whilst the computational time remains reasonable. We compare the solve times of different solvers and show how the accuracy can be improved at the expense of increased computation time. We show that by using this sparse polynomial framework the solve time and accuracy can be improved over other methods for neural network verification with ReLU, sigmoid and tanh activation functions.
Grid-Forming and Spatially Distributed Control Design of Dynamic Virtual Power Plants
Authors: Verena Häberle, Ali Tayyebi, Xiuqiang He, Eduardo Prieto-Araujo, Florian Dörfler
Date: 2022-02-04 00:00:00

We present a novel grid-forming control design approach for dynamic virtual power plants (DVPP). We consider a group of heterogeneous grid-forming distributed energy resources (DER) which collectively provide desired dynamic ancillary services, such as fast frequency and voltage control. To achieve that, we study the nontrivial aggregation of grid-forming DERs to establish the DVPP, and employ an adaptive divide-and-conquer strategy that disaggregates the desired control specifications of the aggregate DVPP via adaptive dynamic participation factors to obtain local desired behaviors of each DER. We then design local controllers at the DER level to realize these local desired behaviors. In the process, physical and engineered limits of each DER are taken into account. We extend the proposed approach to make it also compatible with grid-following DER controls, thereby establishing the concept of so-called hybrid DVPPs. Furthermore, we generalize the DVPP design to spatially dispersed DER locations in power grids with different voltage levels and R/X ratios. Finally, the DVPP control performance is verified via numerical case studies in the IEEE nine-bus transmission grid with an interconnected medium voltage distribution grid.
A Nano-Architecture for Fertility Monitoring via Intra-body Communication
Authors: Shama Siddiqui, Anwar Ahmed Khan, Qammer H. Abbasi, Muhammad Ali Imran, Indrakshi Dey
Date: 2022-02-04 00:00:00

Fertility monitoring in humans for either natural conception or artificial insemination and fertilization has been a critical challenge both for the treating physician and the treated patients. Eggs in human female can be fertilized when they reach the Fallopian tube from the upper parts of the reproductive system. However, no technology, till date, on its own could detect the presence of eggs in the Fallopian tube and communicate their presence to the consulting physician or nurse and the patient, so that the next step can be initiated in a timely fashion. In this paper, we propose a conceptual architecture from a communications engineering point of view. The architecture combines intra-body nano-sensor network for detecting Fallopian tube activity, with body-area network for receiving information from the intra-body network and communicating the same over-the-air to the involved personnel (physician/nurse/patient). Preliminary simulations have been conducted using particle based stochastic simulator to investigate the relationship between achievable information rates, signal to noise ratio (SNR) and distance. It has been found that data rate as high as 300 Mbps is achievable at SNR 45. Hence, the proposed architecture ensures transfer of information with near-zero latency and minimum energy along with high throughput, so that necessary action can be taken within the short time-window of the Fallopian tube activity.