pwc-archive/papers-with-abstracts huggingface dataset:
Smart Mat Used for Prevention of Hospital-Acquired Pressure Injuries
Authors: Lanhao Gao, Zihuai Lin
Date: 2022-07-08 00:00:00
Tasks: Data Visualization

This work develops a smart mat for monitoring body positions. We use Velostat as a force sensor resistance (FSR) to construct a sensor matrix over the mat to receive the pressure distribution of the patient's body, and then upload the processed distribution information to the PC for data visualization through Arduino. Data visualization on the PC side is compiled through Python language to realize the functions of patient body pressure distribution monitoring, long-term pressure alarm and posture prediction. The purpose of this work is to relieve the work stress on medical staff caused by pressure injuries during the treatment and care of patients during the pandemic. This paper includes the literature review on similar previous works and combines the test results to design the structure and circuit of the smart mat.
Winning the lottery with neural connectivity constraints: faster learning across cognitive tasks with spatially constrained sparse RNNs
Authors: Mikail Khona, Sarthak Chandra, Joy J. Ma, Ila Fiete
Date: 2022-07-07 00:00:00

Recurrent neural networks (RNNs) are often used to model circuits in the brain, and can solve a variety of difficult computational problems requiring memory, error-correction, or selection [Hopfield, 1982, Maass et al., 2002, Maass, 2011]. However, fully-connected RNNs contrast structurally with their biological counterparts, which are extremely sparse (~0.1%). Motivated by the neocortex, where neural connectivity is constrained by physical distance along cortical sheets and other synaptic wiring costs, we introduce locality masked RNNs (LM-RNNs) that utilize task-agnostic predetermined graphs with sparsity as low as 4%. We study LM-RNNs in a multitask learning setting relevant to cognitive systems neuroscience with a commonly used set of tasks, 20-Cog-tasks [Yang et al., 2019]. We show through reductio ad absurdum that 20-Cog-tasks can be solved by a small pool of separated autapses that we can mechanistically analyze and understand. Thus, these tasks fall short of the goal of inducing complex recurrent dynamics and modular structure in RNNs. We next contribute a new cognitive multi-task battery, Mod-Cog, consisting of upto 132 tasks that expands by 7-fold the number of tasks and task-complexity of 20-Cog-tasks. Importantly, while autapses can solve the simple 20-Cog-tasks, the expanded task-set requires richer neural architectures and continuous attractor dynamics. On these tasks, we show that LM-RNNs with an optimal sparsity result in faster training and better data-efficiency than fully connected networks.
Data integration in systems genetics and aging research
Authors: Alexis Rapin, Maroun Bou Sleiman, Johan Auwerx
Date: 2022-07-07 00:00:00
Tasks: Data Integration, Explainable artificial intelligence, Management

Human life expectancy has dramatically improved over the course of the last century. Although this reflects a global improvement in sanitation and medical care, this also implies that more people suffer from diseases that typically manifest later in life, like Alzheimer and atherosclerosis. Increasing healthspan by delaying or reverting the development of these age-related diseases has therefore become an urgent challenge in biomedical research. Research in this field is complicated by the multi-factorial nature of age-related diseases. They are rooted in complex physiological mechanisms impacted by heritable, environment and life-style factors that can be unique to each individual. Although technological advances in high-throughput biomolecular assays have enabled researchers to investigate individual physiology at the molecular level, integrating information about its different components, and accounting for individual variations remains a challenge. We are using a large collection of omics and phenotype data derived from the BXD mouse genetic diversity panel to explore how good data management practices, as fostered by the FAIR principles, paired with an explainable artificial intelligence framework, can provide solutions to decipher the complex roots of age-related diseases. These developments will help to propose innovative approaches to extend healthspan in the aging global population.
Habitat connectivity in agricultural landscapes improving multi-functionality of constructed wetlands as nature-based solutions
Authors: Clémentine Préau, Julien Tournebize, Maxime Lenormand, Samuel Alleaume, Véronique Gouy Boussada, Sandra Luque
Date: 2022-07-08 00:00:00

The prevention of biodiversity loss in agricultural landscapes to protect ecosystem stability and functions is of major importance in itself and for the maintenance of associated ecosystem services. Intense agriculture leads to a loss in species richness and homogenization of species pools as well as the fragmentation of natural habitats and groundwater pollution. Constructed wetlands stand as nature-based solutions (NBS) to buffer the degradation of water quality by intercepting the transfer of particles, nutrients and pesticides between crops and surface waters. In karstic watersheds where sinkholes short-cut surface water directly to groundwater increasing water resource vulnerability, constructed wetlands are recommended to mitigate agricultural pollutants. Constructed wetlands also have the potential to improve landscape connectivity by providing refuge and breeding sites for wildlife, especially for amphibians. We propose here a methodology to identify optimal locations for water pollution mitigation using constructed wetlands from the perspective of habitat connectivity. We use ecological niche modelling at the regional scale to model the potential of habitat suitability for nine amphibian species, and to infer how the landscape impedes species movements. We combine those results to graph theory to identify connectivity priorities at the operational scale of an agricultural catchment area. Our framework allowed us to identify optimal areas from the point of view of the species, to analyze the effect of multifunctional constructed wetlands aiming to both reduce water pollution and to improve amphibian species habitat overall connectivity. More generally, we show the potential of habitat connectivity assessment to improve multifunctionality of NBS for pollution mitigation.
An introduction to rating triggers for collateral-inclusive XVA in an ICTMC framework
Authors: Kevin Kamm
Date: 2022-07-08 00:00:00

In this paper, we model the rating process of an entity as a piecewise homogeneous continuous time Markov chain. We focus specifically on calibrating the model to both historical data (rating transition matrices) and market data (CDS quotes), relying on a simple change of measure to switch from the historical probability to the risk-neutral one. We overcome some of the imperfections of the data by proposing a novel calibration procedure, which leads to an improvement of the entire scheme. We apply our model to compute bilateral credit and debit valuation adjustments of a netting set under a CSA with thresholds depending on ratings of the two parties.
Pairwise and high-order dependencies in the cryptocurrency trading network
Authors: Tomas Scagliarini, Giuseppe Pappalardo, Alessio Emanuele Biondo, Alessandro Pluchino, Andrea Rapisarda, Sebastiano Stramaglia
Date: 2022-07-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.
Demystifying the Adversarial Robustness of Random Transformation Defenses
Authors: Chawin Sitawarin, Zachary Golan-Strieb, David Wagner
Date: 2022-06-18 00:00:00
Proceeding: AAAI Workshop AdvML 2022 2
Tasks: Adversarial Robustness, Autonomous Vehicles, Bayesian Optimization

Neural networks' lack of robustness against attacks raises concerns in security-sensitive settings such as autonomous vehicles. While many countermeasures may look promising, only a few withstand rigorous evaluation. Defenses using random transformations (RT) have shown impressive results, particularly BaRT (Raff et al., 2019) on ImageNet. However, this type of defense has not been rigorously evaluated, leaving its robustness properties poorly understood. Their stochastic properties make evaluation more challenging and render many proposed attacks on deterministic models inapplicable. First, we show that the BPDA attack (Athalye et al., 2018a) used in BaRT's evaluation is ineffective and likely overestimates its robustness. We then attempt to construct the strongest possible RT defense through the informed selection of transformations and Bayesian optimization for tuning their parameters. Furthermore, we create the strongest possible attack to evaluate our RT defense. Our new attack vastly outperforms the baseline, reducing the accuracy by 83% compared to the 19% reduction by the commonly used EoT attack ($4.3\times$ improvement). Our result indicates that the RT defense on the Imagenette dataset (a ten-class subset of ImageNet) is not robust against adversarial examples. Extending the study further, we use our new attack to adversarially train RT defense (called AdvRT), resulting in a large robustness gain. Code is available at https://github.com/wagner-group/demystify-random-transform.
Optimization of Temperature and Relative Humidity in an Automatic Egg Incubator Using Mamdani Interference System
Authors: Pramit Dutta, Nafisa Anjum
Date: 2022-06-17 00:00:00

Temperature and humidity are two of the rudimentary factors that must be controlled during egg incubation. Improper temperature and humidity levels during the incubation period often result in unwanted conditions. This paper proposes the design of an efficient Mamdani fuzzy interference system instead of the widely used Takagi-Sugeno system in this field for controlling the temperature and humidity levels of an egg incubator. Though the optimum incubation temperature and humidity levels used here are that of chicken egg, the proposed methodology is applicable to other avian species as well. Theinput functions have been used here as per estimated values forsafe hatching using Mamdani whereas defuzzification method, COA, has been applied for output. From the model output,a stabilized heat from temperature level and fan speed to control the humidity level of an egg incubator can be obtained. This maximizes the hatching rate of healthy chicks under any conditions in the field.
Zero Initialised Unsupervised Active Learning by Optimally Balanced Entropy-Based Sampling for Imbalanced Problems
Authors: Gábor Szűcs, Dávid Papp
Date: 2021-05-24 00:00:00
Proceeding: Journal of Experimental & Theoretical Artificial Intelligence 2021 5
Tasks: Active Learning, BIG-bench Machine Learning, imbalanced classification

Given the challenge of gathering labelled training data for machine learning tasks, active learning has become popular. This paper focuses on the beginning of unsupervised active learning, where there are no labelled data at all. The aim of this zero initialised  unsupervised active learning is to select the most informative examples – even from an imbalanced dataset – to be labelled manually. Our solution with proposed selection strategy, called Optimally Balanced Entropy-Based Sampling (OBEBS) reaches a balanced training set at each step to avoid imbalanced problems. Two theorems of the optimal solution for selection strategy are also presented and proved in the paper. At the beginning of the active learning, there is not enough information for supervised machine learning method, thus our selection strategy is based on unsupervised learning (clustering). The cluster membership likelihoods of the items are essential for the algorithm to connect the clusters and the classes, i.e., to find assignment between them. For the best assignment, the Hungarian algorithm is used, and single, multi, and adaptive assignment variants of OBEBS method are developed. Based on generated and real images datasets of handwritten digits, the experimental results show that our method surpasses the state-of-the-art methods.
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
Authors: Tung Nguyen, Aditya Grover
Date: 2022-07-09 00:00:00
Tasks: Bayesian Optimization, Decision Making, Gaussian Processes, Meta-Learning, Multi-Armed Bandits, Sequential Decision Making

Neural Processes (NPs) are a popular class of approaches for meta-learning. Similar to Gaussian Processes (GPs), NPs define distributions over functions and can estimate uncertainty in their predictions. However, unlike GPs, NPs and their variants suffer from underfitting and often have intractable likelihoods, which limit their applications in sequential decision making. We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem. We learn TNPs via an autoregressive likelihood-based objective and instantiate it with a novel transformer-based architecture. The model architecture respects the inductive biases inherent to the problem structure, such as invariance to the observed data points and equivariance to the unobserved points. We further investigate knobs within the TNP framework that tradeoff expressivity of the decoding distribution with extra computation. Empirically, we show that TNPs achieve state-of-the-art performance on various benchmark problems, outperforming all previous NP variants on meta regression, image completion, contextual multi-armed bandits, and Bayesian optimization.
Optimal Storage and Solar Capacity of a Residential Household under Net Metering and Time-of-Use Pricing
Authors: Victor Sam Moses Babu K., Pratyush Chakraborty, Enrique Baeyens, Pramod P. Khargonekar
Date: 2022-07-11 00:00:00

Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this paper, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provide benefit for a period of one year. Results show significant profit when using storage devices and solar panels optimally for the system. It is important to note that though our approach can help significantly to take investment decisions, the solution will still be sub-optimal for somebody who needs optimal investment jointly on both storage and solar systems.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Authors: Edward Small, Wei Shao, Zeliang Zhang, Peihan Liu, Jeffrey Chan, Kacper Sokol, Flora Salim
Date: 2022-07-11 00:00:00
Tasks: Decision Making, Fairness, Unfairness Detection

With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a defined notion of fairness. However, fair solutions are reliant on the quality of the training data, and can be highly sensitive to noise. Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem. In this work, we therefore propose a new criterion to measure the robustness of various fairness optimisation strategies - the robustness ratio. We conduct multiple extensive experiments on five bench mark fairness data sets using three of the most popular fairness strategies with respect to four of the most popular definitions of fairness. Our experiments empirically show that fairness methods that rely on threshold optimisation are very sensitive to noise in all the evaluated data sets, despite mostly outperforming other methods. This is in contrast to the other two methods, which are less fair for low noise scenarios but fairer for high noise ones. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies. This can potentially can serve as a guideline in choosing the most suitable fairness strategy for various data sets.
Stochastic Gradient Descent and Anomaly of Variance-flatness Relation in Artificial Neural Networks
Authors: Xia Xiong, Yong-Cong Chen, Chunxiao Shi, Ping Ao
Date: 2022-07-11 00:00:00
Tasks: Relation

Stochastic gradient descent (SGD), a widely used algorithm in deep-learning neural networks has attracted continuing studies for the theoretical principles behind its success. A recent work reports an anomaly (inverse) relation between the variance of neural weights and the landscape flatness of the loss function driven under SGD [Feng & Tu, PNAS 118, 0027 (2021)]. To investigate this seemingly violation of statistical physics principle, the properties of SGD near fixed points are analysed via a dynamic decomposition method. Our approach recovers the true "energy" function under which the universal Boltzmann distribution holds. It differs from the cost function in general and resolves the paradox raised by the the anomaly. The study bridges the gap between the classical statistical mechanics and the emerging discipline of artificial intelligence, with potential for better algorithms to the latter.
CoMER: Modeling Coverage for Transformer-based Handwritten Mathematical Expression Recognition
Authors: Wenqi Zhao, Liangcai Gao
Date: 2022-07-10 00:00:00
Tasks: Decoder, Handwritten Mathmatical Expression Recognition

The Transformer-based encoder-decoder architecture has recently made significant advances in recognizing handwritten mathematical expressions. However, the transformer model still suffers from the lack of coverage problem, making its expression recognition rate (ExpRate) inferior to its RNN counterpart. Coverage information, which records the alignment information of the past steps, has proven effective in the RNN models. In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to refine the attention weights with past alignment information without hurting its parallelism. Furthermore, we take coverage information to the extreme by proposing self-coverage and cross-coverage, which utilize the past alignment information from the current and previous layers. Experiments show that CoMER improves the ExpRate by 0.61%/2.09%/1.59% compared to the current state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME 2014/2016/2019 test sets.
L$_0$onie: Compressing COINs with L$_0$-constraints
Authors: Juan Ramirez, Jose Gallego-Posada
Date: 2022-07-08 00:00:00

Advances in Implicit Neural Representations (INR) have motivated research on domain-agnostic compression techniques. These methods train a neural network to approximate an object, and then store the weights of the trained model. For example, given an image, a network is trained to learn the mapping from pixel locations to RGB values. In this paper, we propose L$_0$onie, a sparsity-constrained extension of the COIN compression method. Sparsity allows to leverage the faster learning of overparameterized networks, while retaining the desirable compression rate of smaller models. Moreover, our constrained formulation ensures that the final model respects a pre-determined compression rate, dispensing of the need for expensive architecture search.
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
Authors: Zijie Wu, Zhen Zhu, Junping Du, Xiang Bai
Date: 2022-07-11 00:00:00
Tasks: Image-to-Image Translation, Style Transfer, Video Style Transfer

In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL.