pwc-archive/papers-with-abstracts huggingface dataset:
Denoising of MR images with Rician noise using a wider neural network and noise range division
Authors: Xuexiao Youa, B, Ning Caoa, ⁎, Hao Lua, Minghe Maoa, Wei Wanga, C
Date: 2019-06-17 00:00:00
Proceeding: Magnetic Resonance Imaging 2019 6
Tasks: Deep Learning, Denoising, Medical Image Analysis

Magnetic resonance (MR) images denoising is important in medical image analysis. Denoising methods based on deep learning have shown great promise and outperform all of the other conventional methods. However, deep- learning methods are limited by the number of training samples. In this article, using a small sample size, we applied a wider denoising neural network to MR images with Rician noise and trained several denoising models. The first model is specific to a certain noise, while the other applies to a wide range of noise levels. We con- sidered the noise range as one interval, two sub-intervals, three sub-intervals, or even more sub-intervals to train the corresponding models. Experimental results demonstrate that for MR images, the proposed deep-learning models are efficient in terms of peak-signal-to-noise ratio, structure-similarity-index metrics and normalized mutual information. In addition, for blind noise, the effect of the three sub-intervals is better than that of the other sub-intervals.
The Formation and Survival of Discs in a Lambda-CDM Universe
Authors: Cecilia Scannapieco, Simon D. M. White, Volker Springel, Patricia B. Tissera
Date: 2008-12-04 00:00:00

We study the formation of galaxies in a Lambda-CDM Universe using high resolution hydrodynamical simulations with a multiphase treatment of gas, cooling and feedback, focusing on the formation of discs. Our simulations follow eight haloes similar in mass to the Milky Way and extracted from a large cosmological simulation without restriction on spin parameter or merger history. This allows us to investigate how the final properties of the simulated galaxies correlate with the formation histories of their haloes. We find that, at z = 0, none of our galaxies contain a disc with more than 20 per cent of its total stellar mass. Four of the eight galaxies nevertheless have well-formed disc components, three have dominant spheroids and very small discs, and one is a spheroidal galaxy with no disc at all. The z = 0 spheroids are made of old stars, while discs are younger and formed from the inside-out. Neither the existence of a disc at z = 0 nor the final disc-to-total mass ratio seems to depend on the spin parameter of the halo. Discs are formed in haloes with spin parameters as low as 0.01 and as high as 0.05; galaxies with little or no disc component span the same range in spin parameter. Except for one of the simulated galaxies, all have significant discs at z > ~2, regardless of their z = 0 morphologies. Major mergers and instabilities which arise when accreting cold gas is misaligned with the stellar disc trigger a transfer of mass from the discs to the spheroids. In some cases, discs are destroyed, while in others, they survive or reform. This suggests that the survival probability of discs depends on the particular formation history of each galaxy. A realistic Lambda-CDM model will clearly require weaker star formation at high redshift and later disc assembly than occurs in our models.
PrognosEase: A data generator for health deterioration prognosis
Authors: Tarek Berghout, Mohamed Benbouzid
Date: 2023-07-05 00:00:00
Proceeding: Elsevier SoftwareX 2023 7
Tasks: Prognosis

This paper presents PrognosEase; a software that provides an easier way to produce different types of run-to-failure data mimicking real-world conditions to simplify prognosis studies in terms of data collection and improvement in machine learning degradation modeling process. Different types of degradation types made available to meet different types of applications. Besides, some primary machine learning experiments were performed to ensure that complexity patterns of real systems could be observed in the training/testing predictions attitude. This paper also presents the impacts, limitations and potential improvements of the data generator.
Attention Wave-U-Net for Acoustic Echo Cancellation
Authors: Jung-Hee Kim, Joon-Hyuk Chang
Date: 2020-10-25 00:00:00
Proceeding: Interspeech 2020 10
Tasks: Acoustic echo cancellation

In this paper, a Wave-U-Net based acoustic echo cancellation
(AEC) with an attention mechanism is proposed to jointly sup-
press acoustic echo and background noise. The proposed ap-
proach consists of the Wave-U-Net, an auxiliary encoder, and an
attention network. In the proposed approach, the Wave-U-Net
yields the estimated near-end speech from the mixture, the aux-
iliary encoder extracts the latent features of the far-end speech,
among which the relevant features are provided to the Wave-U-
Net by using the attention mechanism. With the attention net-
work, the echo can be effectively suppressed from the mixture.
Experimental results on TIMIT dataset show that the proposed
approach outperforms the existing methods in terms of the echo
return loss enhancement (ERLE) for the single-talk period and
the perceptual evaluation of speech quality (PESQ) score for the
double-talk period. Furthermore, the robustness of the proposed
approach against unseen noise condition is also validated from
the experimental results.
Real-Time Detection of False Readings in Smart Grid AMI Using Deep and Ensemble Learning research model code github
Authors: MOHAMMED J. ABDULAAL, Mohamed I. Ibrahem, MOHAMED M. E. A. MAHMOUD, JUNAID KHALID, ABDULAH JEZA ALJOHANI, AHMAD H. MILYANI, AND ABDULLAH M. ABUSORRAH
Date: 2022-04-29 00:00:00
Proceeding: IEEE Access 2022 4
Tasks: energy management, Ensemble Learning, Management

In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to
regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and
real-time load monitoring and energy management. However, fraudulent consumers may compromise their
meters to launch electricity-theft cyberattacks by reporting low-consumption readings to reduce their bills.
These false readings not only cause financial losses but also degrade the grid’s performance because they are
used for energy management and load estimate. The existing solutions in the literature focus only on securing
the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings
for a long period of time in load monitoring and energy management until they are identified. In this paper,
we propose a general ensemble-based deep-learning detector that enables the SO to detect false readings in
real time. To do that, we first train several deep learning models on samples generated from a sliding window
of the readings. Then, we use the best-performing model to train several models on different ratios of false
readings and use them in our ensemble-based detector. Extensive experiments are conducted, and the results
indicate that comparing to the literature, our detector can detect the false readings after sending a few false
readings (around 15) comparing to the existing daily and weekly detection approaches that need 144 and
1,008 readings, respectively.
Sequential Inference for Latent Force Models
Authors: Jouni Hartikainen, Simo Sarkka
Date: 2012-02-14 00:00:00

Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated as a linear statespace model driven by a white noise process and how inference on the resulting model can be efficiently implemented using Kalman filter and smoother. Then we shall show how the recently proposed switching LFM can be reformulated using the state variable approach, and how we can construct a probabilistic model for the switches by formulating a similar switching LFM as a switching linear dynamic system (SLDS). We illustrate the performance of the proposed methodology in simulated scenarios and apply it to inferring the switching points in GPS data collected from car movement data in urban environment.
Single cortical neurons as deep artificial neural networks
Authors: David Beniaguev, Idan Segev, Michael London
Date: 2021-08-10 00:00:00
Proceeding: Neuron 2021 8
Tasks: Time Series

Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons’ input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical models of cortical neurons at millisecond (spiking) resolution. A temporally convolutional DNN with five to eight layers was required to capture the I/O mapping of a realistic model of a layer 5 cortical pyramidal cell (L5PC). This DNN generalized well when presented with inputs widely outside the training distribution. When NMDA receptors were removed, a much simpler network (fully connected neural network with one hidden layer) was sufficient to fit the model. Analysis of the DNNs’ weight matrices revealed that synaptic integration in dendritic branches could be conceptualized as pattern matching from a set of spatiotemporal templates. This study provides a unified characterization of the computational complexity of single neurons and suggests that cortical networks therefore have a unique architecture, potentially supporting their computational power.
Improved-salp swarm optimized type-II fuzzy controller in load frequency control of multi area islanded AC microgrid
Authors: Prakash Chandra Sahu, Sonalika Mishra, Ramesh Chandra Prusty, Sidhartha Panda
Date: 2018-11-05 00:00:00
Proceeding: journal 2018 11

The present article deals with load frequency control (LFC) in an islanded two area AC microgrid (MC)
system. In this study the proposed two area MG system comprises different micro sources including
Micro-turbine (MT), Diesel engine generator (DEG) and Fuel Cells (FC) which are primarily responsible
for balancing load and power generation in an interconnected system. MG in grid-connected mode has
lower possibility of frequency control problem due to active presence of utility grid. Whereas MG in
islanded mode faces huge frequency control problem due to dynamic nature of different renewable energy
sources (RES) and different uncertainties like wind power fluctuations, disturbances in solar irradiation
power, dynamics in applied load and system parameters (damping coefficient & Inertia constant). In
regard to this the present article proposes a robust type-II fuzzy PID controller to create secondary
frequency control loop for maintaining both frequency and tie-line power to their nominal values under
different uncertainties. For performance study, the proposed type-II fuzzy PID controller performances
are compared with type-I fuzzy controller, PID and PI controllers. To obtain optimal gain values of above
controllers, a meta-heuristic improved-salp swarm optimization (I-SSO) algorithm has been implemented
and proposed I-SSO technique performances are compared with original SSO, Particle swarm optimization
(PSO) and Genetic Algorithm (GA) techniques. Finally it is observed from different performance analysis
that proposed I-SSO tuned type-II fuzzy controller exhibits superior performances for load frequency
control in multi-area islanded AC microgrid system under different uncertainty conditions.
A template-independent approach for information extraction in real estate documents
Authors: Nicola Landro, Gabriele Destro, Stefano Taverni, Ignazio Gallo
Date: 2023-05-30 00:00:00
Proceeding: Ital-IA 2023 5
Tasks: Information Retrieval, Natural Language Understanding, Optical Character Recognition (OCR), Question Answering, Retrieval

Business corporations manage tons of unstructured data daily, such as PDFs and websites. Recent advances in the deep
learning field help find insight from this unstructured information. New models leverage the power of the Transformer
architecture to accomplish natural language understanding tasks on these data, jointly using the raw image and its text
content or directly the image without OCR. We propose an extraction pipeline that employs question-answering models
to get insight from unstructured data, allowing fast and efficient information retrieval from different sources. We show an
application of this technique to a specific set of documents and how we can scale this infrastructure to different types of
records. Our solution can effectively handle large document corpora robustly, helping corporations exploit all the power
coming from their data.
Applications of the discrete-time Fourier transform to data analysis
Authors: dayne sorvisto
Date: 2023-07-08 00:00:00
Proceeding: International Journal of Data Science and Analytics 2023 7
Tasks: Computing Characateristic Functions

We define a discrete analogue of the characteristic function for discrete random variable and develop numerical procedures for computing the discrete characteristic function for several well-known discrete random variables. We rigorously define what is meant by the Fourier transform of a probability mass function and also show how to reverse the process to recover the probability mass function of a discrete random variable, given a procedure for computing the characteristic function. Unlike previous work on the subject, our approach is novel in that we are not computing closed-form solutions of a continuous random variable but are focused on applying the methods to real-world data sets.
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks
Authors: Aysha Thahsin Zahir Ismail, Raj Mani Shukla
Date: 2023-07-04 00:00:00
Tasks: Data Poisoning, Federated Learning, Handwritten Digit Recognition

Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid of FL and SL known as the SplitFed Learning (SFL). This research is the earliest attempt to study, analyze and present the impact of data poisoning attacks in SFL. We propose three kinds of novel attack strategies namely untargeted, targeted and distance-based attacks for SFL. All the attacks strategies aim to degrade the performance of the DCML-based classifier. We test the proposed attack strategies for two different case studies on Electrocardiogram signal classification and automatic handwritten digit recognition. A series of attack experiments were conducted by varying the percentage of malicious clients and the choice of the model split layer between the clients and the server. The results after the comprehensive analysis of attack strategies clearly convey that untargeted and distance-based poisoning attacks have greater impacts in evading the classifier outcomes compared to targeted attacks in SFL
Scaling Laws Do Not Scale
Authors: Fernando Diaz, Michael Madaio
Date: 2023-07-05 00:00:00

Recent work has advocated for training AI models on ever-larger datasets, arguing that as the size of a dataset increases, the performance of a model trained on that dataset will correspondingly increase (referred to as "scaling laws"). In this paper, we draw on literature from the social sciences and machine learning to critically interrogate these claims. We argue that this scaling law relationship depends on metrics used to measure performance that may not correspond with how different groups of people perceive the quality of models' output. As the size of datasets used to train large AI models grows and AI systems impact ever larger groups of people, the number of distinct communities represented in training or evaluation datasets grows. It is thus even more likely that communities represented in datasets may have values or preferences not reflected in (or at odds with) the metrics used to evaluate model performance in scaling laws. Different communities may also have values in tension with each other, leading to difficult, potentially irreconcilable choices about metrics used for model evaluations -- threatening the validity of claims that model performance is improving at scale. We end the paper with implications for AI development: that the motivation for scraping ever-larger datasets may be based on fundamentally flawed assumptions about model performance. That is, models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models. We suggest opportunities for the field to rethink norms and values in AI development, resisting claims for universality of large models, fostering more local, small-scale designs, and other ways to resist the impetus towards scale in AI.
Optimal Bandwidth Selection for DENCLUE Algorithm
Authors: Hao Wang
Date: 2023-07-06 00:00:00
Tasks: Clustering

In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named DENCLUE was invented to solve clustering problem for nonlinear data structures. However, its parameter selection problem was largely neglected until 2011. In this paper, we propose a new approach to compute the optimal parameters for the DENCLUE algorithm, and discuss its performance in the experiment section.
Sparse Graphical Linear Dynamical Systems
Authors: Emilie Chouzenoux, Victor Elvira
Date: 2023-07-06 00:00:00
Tasks: Earth Observation, State Space Models, Time Series

Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis. Extensive research exists on state-space models (SSMs), which are powerful mathematical tools that allow for probabilistic and interpretable learning on time series. Learning the model parameters in SSMs is arguably one of the most complicated tasks, and the inclusion of prior knowledge is known to both ease the interpretation but also to complicate the inferential tasks. Very recent works have attempted to incorporate a graphical perspective on some of those model parameters, but they present notable limitations that this work addresses. More generally, existing graphical modeling tools are designed to incorporate either static information, focusing on statistical dependencies among independent random variables (e.g., graphical Lasso approach), or dynamic information, emphasizing causal relationships among time series samples (e.g., graphical Granger approaches). However, there are no joint approaches combining static and dynamic graphical modeling within the context of SSMs. This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM. We present DGLASSO (Dynamic Graphical Lasso), a new inference method within this framework that implements an efficient block alternating majorization-minimization algorithm. The algorithm's convergence is established by departing from modern tools from nonlinear analysis. Experimental validation on various synthetic data showcases the effectiveness of the proposed model and inference algorithm.
Optimal Scalarizations for Sublinear Hypervolume Regret
Authors: Qiuyi Zhang
Date: 2023-07-06 00:00:00
Tasks: Bayesian Optimization

Scalarization is a general, parallizable technique that can be deployed in any multiobjective setting to reduce multiple objectives into one, yet some have dismissed this versatile approach because linear scalarizations cannot explore concave regions of the Pareto frontier. To that end, we aim to find simple non-linear scalarizations that provably explore a diverse set of $k$ objectives on the Pareto frontier, as measured by the dominated hypervolume. We show that hypervolume scalarizations with uniformly random weights achieves an optimal sublinear hypervolume regret bound of $O(T^{-1/k})$, with matching lower bounds that preclude any algorithm from doing better asymptotically. For the setting of multiobjective stochastic linear bandits, we utilize properties of hypervolume scalarizations to derive a novel non-Euclidean analysis to get regret bounds of $\tilde{O}( d T^{-1/2} + T^{-1/k})$, removing unnecessary $\text{poly}(k)$ dependencies. We support our theory with strong empirical performance of using non-linear scalarizations that outperforms both their linear counterparts and other standard multiobjective algorithms in a variety of natural settings.
OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload
Authors: Andreas Karatzas, Iraklis Anagnostopoulos
Date: 2023-07-06 00:00:00

Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN applications, raising new challenges regarding workload distribution. Equipped with a diverse set of accelerators, newer embedded system present architectural heterogeneity, which current run-time controllers are unable to fully utilize. To enable high throughput in multi-DNN workloads, such a controller is ought to explore hundreds of thousands of possible solutions to exploit the underlying heterogeneity. In this paper, we propose OmniBoost, a lightweight and extensible multi-DNN manager for heterogeneous embedded devices. We leverage stochastic space exploration and we combine it with a highly accurate performance estimator to observe a x4.6 average throughput boost compared to other state-of-the-art methods. The evaluation was performed on the HiKey970 development board.
A Vulnerability of Attribution Methods Using Pre-Softmax Scores
Authors: Miguel Lerma, Mirtha Lucas
Date: 2023-07-06 00:00:00

We discuss a vulnerability involving a category of attribution methods used to provide explanations for the outputs of convolutional neural networks working as classifiers. It is known that this type of networks are vulnerable to adversarial attacks, in which imperceptible perturbations of the input may alter the outputs of the model. In contrast, here we focus on effects that small modifications in the model may cause on the attribution method without altering the model outputs.