pwc-archive/papers-with-abstracts huggingface dataset:
UniCT DMI Solution for 3rd COV19D Competition on COVID-19 Detection through attention-based CNN for CT Scan
Authors: Alessia Rondinella, Francesco Guarnera, Oliver Giudice, Alessandro Ortis, Francesco Rundo, Sebastiano Battiato
Date: 2023-03-15 00:00:00
Tasks: Management, Medical Image Analysis

This paper presents our solution for the first challenge of the 3rd Covid-19 competition, which is part of the "AI-enabled Medical Image Analysis Workshop" organized by IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 2023. Our proposed solution is based on a Resnet as a backbone network with the addition of attention mechanisms. The Resnet provides an effective feature extractor for the classification task, while the attention mechanisms improve the model's ability to focus on important regions of interest within the images. We conducted extensive experiments on the provided dataset and achieved promising results. Our proposed approach has the potential to assist in the accurate diagnosis of Covid-19 from chest computed tomography images, which can aid in the early detection and management of the disease.
Artificial Intelligence based drone for early disease detection and precision pesticide management in cashew farming
Authors: Manoj Kumar Rajagopal, Bala Murugan MS
Date: 2023-03-15 00:00:00
Tasks: Edge-computing, Management

The use of unmanned aerial vehicles (UAV) is revolutionizing the agricultural industry. Cashews are grown by approximately 70% of small and marginal farmers, and the cashew industry plays a critical role in their economic development. To take timely counter measures against plant diseases and infections, it is imperative to monitor and detect diseases as early as possible and take suitable measures. Using UAVs, such as those that are equipped with artificial intelligence, can assist farmers by providing early detection of crop diseases and precision pesticide application. An edge computing paradigm of Artificial Intelligence is employed to process this image in order to make decisions with the least amount of latency possible. As a result of these decisions, the stage of infestation, the crops affected, the method of prevention of spreading the disease, and what type and amount of pesticides need to be applied can be determined. UAVs equipped with sensors detect disease patterns quickly and accurately over large areas. Combined with AI algorithms, these machines can analyse data from a variety of sources such as temperature, humidity, CO2 levels and soil composition. This allows them to recognize disease symptoms before they become visible. Early detection allows for more effective control strategies that can reduce costs caused by lost production due to infestations or crop failure. Using an end-to-end training architecture, mobileNetV2 determines how to classify anthracnose disease in cashew leaves. A standard PlantVillage dataset is used for performance evaluation and for standardization. Additionally, samples captured with a drone present a variety of image samples captured in a variety of conditions, which complicates the analysis. According to our analysis, we were able to identify the anthracnose with 95% accuracy and the healthy leaves with 99% accuracy.
Observation of Periodic Systems: Bridge Centralized Kalman Filtering and Consensus-Based Distributed Filtering
Authors: Jiachen Qian, Zhisheng Duan, Peihu Duan, Zhongkui Li
Date: 2023-03-15 00:00:00

Compared with linear time invariant systems, linear periodic system can describe the periodic processes arising from nature and engineering more precisely. However, the time-varying system parameters increase the difficulty of the research on periodic system, such as stabilization and observation. This paper aims to consider the observation problem of periodic systems by bridging two fundamental filtering algorithms for periodic systems with a sensor network: consensus-on-measurement-based distributed filtering (CMDF) and centralized Kalman filtering (CKF). Firstly, one mild convergence condition based on uniformly collective observability is established for CMDF, under which the filtering performance of CMDF can be formulated as a symmetric periodic positive semidefinite (SPPS) solution to a discrete-time periodic Lyapunov equation. Then, the closed form of the performance gap between CMDF and CKF is presented in terms of the information fusion steps and the consensus weights of the network. Moreover, it is pointed out that the estimation error covariance of CMDF exponentially converges to the centralized one with the fusion steps tending to infinity. Altogether, these new results establish a concise and specific relationship between distributed and centralized filterings, and formulate the trade-off between the communication cost and distributed filtering performance on periodic systems. Finally, the theoretical results are verified with numerical experiments.
Low Complexity Iterative 2D DOA Estimation in MIMO Systems
Authors: Md Imrul Hasan, Mohammad Saquib
Date: 2023-03-15 00:00:00

Multiple-input multiple-output (MIMO) systems play an essential role in direction-of-arrival (DOA) estimation. A large number of antennas used in a MIMO system imposes a huge complexity burden on the popular DOA estimation algorithms, such as MUSIC and ESPRIT due to the implementation of eigenvalue decomposition. This renders those algorithms impractical in applications requiring quick DOA estimation. Consequently, we theoretically derive several useful noise subspace vectors when the number of signal sources is less than the number of elements in both the transmitter and receiver sides. Those noise subspace vectors are then utilized to formulate a 2D-constrained minimization problem, solved iteratively to obtain the DOAs of all the sources in a scene. The convergence of the proposed iterative algorithm has been mathematically as well as numerically demonstrated. Depending on the number of iterations, our algorithm can provide significant complexity gain over the existing high-resolution 2D DOA estimation algorithms in MIMO systems, such as MUSIC, while exhibiting comparable performance for a moderate to high signal-to-noise ratio (SNR).
Stress Detection using Context-Aware Sensor Fusion from Wearable Devices
Authors: Nafiul Rashid, Trier Mortlock, Mohammad Abdullah Al Faruque
Date: 2023-03-14 00:00:00
Tasks: Sensor Fusion

Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine the mental state of patients. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: (i) robustness -- wearable health sensors contain high levels of measurement noise that degrades performance, and (ii) adaptation -- static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. SELF-CARE demonstrates state-of-the-art performance on the WESAD dataset. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2-class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications.
EGC: a format for expressing prokaryotic genomes content expectations
Authors: Giorgio Gonnella
Date: 2023-03-15 00:00:00

The number of available genomes of prokaryotic organisms is rapidly growing enabling comparative genomics studies. The comparison of genomes of organisms with a common phenotype, habitat or phylogeny often shows that these genomes share some common contents. Collecting rules expressing common genome traits depending on given factors is useful, as such rules could be used for quality control or for identifying interesting exceptions and formulating hypothesis. Automatizing the rules verification using computation tools requires the definition of a representation schema. In this study, we present EGC (Expected Genome Contents), a flat-text file format for the representation of expectation rules about the content of prokaryotic genomes. A parser for the EGC format has been implemented using the TextFormats software library, accompanied by a set of related Python packages.
Psychophysics of Artificial Neural Networks Questions Classical Hue Cancellation Experiments
Authors: Jorge Vila-Tomás, Pablo Hernández-Cámara, Jesús Malo
Date: 2023-03-15 00:00:00

We show that classical hue cancellation experiments lead to human-like opponent curves even if the task is done by trivial (identity) artificial networks. Specifically, human-like opponent spectral sensitivities always emerge in artificial networks as long as (i) the retina converts the input radiation into any tristimulus-like representation, and (ii) the post-retinal network solves the standard hue cancellation task, e.g. the network looks for the weights of the cancelling lights so that every monochromatic stimulus plus the weighted cancelling lights match a grey reference in the (arbitrary) color representation used by the network. In fact, the specific cancellation lights (and not the network architecture) are key to obtain human-like curves: results show that the classical choice of the lights is the one that leads to the best (more human-like) result, and any other choices lead to progressively different spectral sensitivities. We show this in two ways: through artificial psychophysics using a range of networks with different architectures and a range of cancellation lights, and through a change-of-basis theoretical analogy of the experiments. This suggests that the opponent curves of the classical experiment are just a by-product of the front-end photoreceptors and of a very specific experimental choice but they do not inform about the downstream color representation. In fact, the architecture of the post-retinal network (signal recombination or internal color space) seems irrelevant for the emergence of the curves in the classical experiment. This result in artificial networks questions the conventional interpretation of the classical result in humans by Jameson and Hurvich.
Pesticide Mediated Critical Transition in Plant-Pollinator Networks
Authors: Arnab Chattopadhyay, Amit Samadder, Sabyasachi Bhattacharya
Date: 2023-03-15 00:00:00

Mutually beneficial interactions between plant and pollinators play an essential role in the biodiversity, stability of the ecosystem and crop production. Despite their immense importance, rapid decline events of pollinators are common worldwide in past decades. Excessive use of chemical pesticides is one of the most important threat to pollination in the current era of anthropogenic changes. Pesticides are applied to the plants to increase their growth by killing harmful pests and pollinators accumulates toxic pesticides from the interacting plants directly from the nectar and pollen. This has a significant adverse effect on the pollinator growth and the mutualism which in turn can cause an abrupt collapse of the community however predicting the fate of such community dynamics remains a blur under the alarming rise in the dependency of chemical pesticides. We mathematically modeled the influence of pesticides in a multispecies mutualistic community and used 105 real plant-pollinator networks sampled worldwide as well as simulated networks, to assess its detrimental effect on the plant-pollinator mutualistic networks. Our results indicate that the persistence of the community is strongly influenced by the level of pesticide and catastrophic and irreversible community collapse may occur due to pesticide. Furthermore, a species rich, highly nested community with low connectance and modularity has greater potential to function under the influence of pesticide. We finally proposed a realistic intervention strategy which involves the management of the pesticide level of one targeted plant from the community. We show that our intervention strategy can significantly delay the collapse of the community. Overall our study can be considered as the first attempt to understand the consequences of the chemical pesticide on a plant-pollinator mutualistic community.
Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data
Authors: C. Wu, E. B. Gunnarsson, E. M. Myklebust, A. Köhn-Luque, D. S. Tadele, J. M. Enserink, A. Frigessi, J. Foo, K. Leder
Date: 2023-03-14 00:00:00

Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages.
DeFi Lending During The Merge
Authors: Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer
Date: 2023-01-23 00:00:00
Tasks: POS

Lending protocols in decentralized finance enable the permissionless exchange of capital from lenders to borrowers without relying on a trusted third party for clearing or market-making. Interest rates are set by the supply and demand of capital according to a pre-defined function. In the lead-up to The Merge: Ethereum blockchain's transition from proof-of-work (PoW) to proof-of-stake (PoS), a fraction of the Ethereum ecosystem announced plans of continuing with a PoW-chain. Owners of ETH - whether their ETH was borrowed or not - would hold the native tokens on each chain. This development alarmed lending protocols. They feared spiking ETH borrowing rates would lead to mass liquidations which could undermine their viability. Thus, the decentralized autonomous organization running the protocols saw no alternative to intervention - restricting users' ability to borrow. We investigate the effects of the merge and the aforementioned intervention on the two biggest lending protocols on Ethereum: AAVE and Compound. Our analysis finds that borrowing rates were extremely volatile, jumping by two orders of magnitude, and borrowing at times reached 100% of the available funds. Despite this, no spike in mass liquidations or irretrievable loans materialized. Further, we are the first to quantify and analyze hard-fork-arbitrage, profiting from holding debt in the native blockchain token during a hard fork. We find that arbitrageurs transferred tokens to centralized exchanges which at the time were worth more than 13 Mio US$, money that was effectively extracted from the platforms' lenders.
An axiomatic approach to default risk and model uncertainty in rating systems
Authors: Max Nendel, Jan Streicher
Date: 2023-03-14 00:00:00

In this paper, we deal with an axiomatic approach to default risk. We introduce the notion of a default risk measure, which generalizes the classical probability of default (PD), and allows to incorporate model risk in various forms. We discuss different properties and representations of default risk measures via monetary risk measures, families of related tail risk measures, and Choquet capacities. In a second step, we turn our focus on default risk measures, which are given as worst-case PDs and distorted PDs. The latter are frequently used in order to take into account model risk for the computation of capital requirements through risk-weighted assets (RWAs), as demanded by the Capital Requirement Regulation (CRR). In this context, we discuss the impact of different default risk measures and margins of conservatism on the amount of risk-weighted assets.
Optimal investment in ambiguous financial markets with learning
Authors: Nicole Bäuerle, Antje Mahayni
Date: 2023-03-15 00:00:00

We consider the classical multi-asset Merton investment problem under drift uncertainty, i.e. the asset price dynamics are given by geometric Brownian motions with constant but unknown drift coefficients. The investor assumes a prior drift distribution and is able to learn by observing the asset prize realizations during the investment horizon. While the solution of an expected utility maximizing investor with constant relative risk aversion (CRRA) is well known, we consider the optimization problem under risk and ambiguity preferences by means of the KMM (Klibanoff et al. (2005)) approach. Here, the investor maximizes a double certainty equivalent. The inner certainty equivalent is for given drift coefficient, the outer is based on a drift distribution. Assuming also a CRRA type ambiguity function, it turns out that the optimal strategy can be stated in terms of the solution without ambiguity preferences but an adjusted drift distribution. To the best of our knowledge an explicit solution method in this setting is new. We rely on some duality theorems to prove our statements. Based on our theoretical results, we are able to shed light on the impact of the prior drift distribution as well as the consequences of ambiguity preferences via the transfer to an adjusted drift distribution, i.e. we are able to explain the interaction of risk and ambiguity preferences. We compare our results with the ones in a pre-commitment setup where the investor is restricted to deterministic strategies. It turns out that (under risk and ambiguity aversion) an infinite investment horizon implies in both cases a maximin decision rule, i.e. the investor follows the worst (best) Merton fraction (over all realizations of it) if she is more (less) risk averse than a log-investor. We illustrate our findings with an extensive numerical study.
Optimal Investment in Defined Contribution Pension Schemes with Forward Utility Preferences
Authors: Kenneth Tsz Hin Ng, Wing Fung Chong
Date: 2023-03-15 00:00:00

Optimal investment strategies of an individual worker during the accumulation phase in the defined contribution pension scheme have been well studied in the literature. Most of them adopted the classical backward model and approach, but any pre-specifications of retirement time, preferences, and market environment models do not often hold in such a prolonged horizon of the pension scheme. Pre-commitment to ensure the time-consistency of an optimal investment strategy derived from the backward model and approach leads the supposedly optimal strategy to be sub-optimal in the actual realizations. This paper revisits the optimal investment problem for the worker during the accumulation phase in the defined contribution pension scheme, via the forward preferences, in which an environment-adapting strategy is able to hold optimality and time-consistency together. Stochastic partial differential equation representation for the worker's forward preferences is illustrated. This paper constructs two of the forward utility preferences and solves the corresponding optimal investment strategies, in the cases of initial power and exponential utility functions.
Systematic design space exploration by learning the explored space using Machine Learning
Authors: Avinash Kumar, Anish Kumar, Sumit Sharma, Surjeet Singh, Kumar Vardhan
Date: 2023-03-14 00:00:00

Current practice in parameter space exploration in euclidean space is dominated by randomized sampling or design of experiment methods. The biggest issue with these methods is not keeping track of what part of parameter space has been explored and what has not. In this context, we utilize the geometric learning of explored data space using modern machine learning methods to keep track of already explored regions and samples from the regions that are unexplored. For this purpose, we use a modified version of a robust random-cut forest along with other heuristic-based approaches. We demonstrate our method and its progression in two-dimensional Euclidean space but it can be extended to any dimension since the underlying method is generic.
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Authors: Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris, Mehdi B. Tahoori, Jörg Henkel
Date: 2023-03-14 00:00:00

Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. Nevertheless, large feature sizes in PE prohibit the realization of complex ML models in PE, even with bespoke architectures. In this work, we present an automated, cross-layer approximation framework tailored to bespoke architectures that enable complex ML models, such as Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), in PE. Our framework adopts cooperatively a hardware-driven coefficient approximation of the ML model at algorithmic level, a netlist pruning at logic level, and a voltage over-scaling at the circuit level. Extensive experimental evaluation on 12 MLPs and 12 SVMs and more than 6000 approximate and exact designs demonstrates that our model-to-circuit cross-approximation delivers power and area optimal designs that, compared to the state-of-the-art exact designs, feature on average 51% and 66% area and power reduction, respectively, for less than 5% accuracy loss. Finally, we demonstrate that our framework enables 80% of the examined classifiers to be battery-powered with almost identical accuracy with the exact designs, paving thus the way towards smart complex printed applications.