pwc-archive/papers-with-abstracts huggingface dataset:
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems
Authors: Tarek Berghout, Mohamed Benbouzid, S. M. MUYEEN, Toufik Bentrcia, Leïla-Hayet Mouss
Date: 2021-11-15 00:00:00
Proceeding: IEEE Access 2021 11
Tasks: Dimensionality Reduction

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.
Binary Image Skeletonization Using 2-Stage U-Net
Authors: Mohamed A. Ghanem, Alaa A. Anani
Date: 2021-12-22 00:00:00
Tasks: Action Detection, Activity Detection

Object Skeletonization is the process of extracting skeletal, line-like representations of shapes. It provides a very useful tool for geometric shape understanding and minimal shape representation. It also has a wide variety of applications, most notably in anatomical research and activity detection. Several mathematical algorithmic approaches have been developed to solve this problem, and some of them have been proven quite robust. However, a lesser amount of attention has been invested into deep learning solutions for it. In this paper, we use a 2-stage variant of the famous U-Net architecture to split the problem space into two sub-problems: shape minimization and corrective skeleton thinning. Our model produces results that are visually much better than the baseline SkelNetOn model. We propose a new metric, M-CCORR, based on normalized correlation coefficients as an alternative to F1 for this challenge as it solves the problem of class imbalance, managing to recognize skeleton similarity without suffering from F1's over-sensitivity to pixel-shifts.
Scalable Memory Protection in the PENGLAI Enclave
Authors: Erhu Feng, Xu Lu, Dong Du, Bicheng Yang, Xueqiang Jiang, Yubin Xia, Binyu Zang, Haibo Chen
Date: 2021-07-14 00:00:00
Proceeding: OSDI 2021 7
Tasks: CPU

Secure hardware enclaves have been widely used for protecting security-critical applications in the cloud. However, existing enclave designs fail to meet the requirements of scalability demanded by new scenarios like serverless computing, mainly due to the limitations in their secure memory protection mechanisms, including static allocation, restricted capacity and high-cost initialization. In this paper, we propose a software-hardware co-design to support dynamic, fine-grained, large-scale secure memory as well as fast-initialization. We first introduce two new hardware primitives: 1) Guarded Page Table (GPT), which protects page table pages to support page-level secure memory isolation; 2) Mountable Merkle Tree (MMT), which supports scalable integrity protection for secure memory. Upon these two primitives, our system can scale to thousands of concurrent enclaves with high resource utilization and eliminate the high-cost initialization of secure memory using fork-style enclave creation without weakening the security guarantees.

We have implemented a prototype of our design based on Penglai, an open-sourced enclave system for RISC-V. The experimental results show that Penglai can support 1,000s enclave instances running concurrently and scale up to 512GB secure memory with both encryption and integrity protection. The overhead of GPT is 5% for memory-intensive workloads (e.g., Redis) and negligible for CPU-intensive workloads (e.g., RV8 and Coremarks). Penglai also reduces the latency of secure memory initialization by three orders of magnitude and gains 3.6x speedup for real-world applications (e.g., MapReduce).
Characterizing serverless platforms with serverlessbench
Authors: Tianyi Yu, Qingyuan Liu, Dong Du, Yubin Xia, Bingyu Zang, Haibo Chen
Date: 2020-10-12 00:00:00
Proceeding: SOCC 2020 10

Serverless computing promises auto-scalability and cost-efficiency (in "pay-as-you-go" manner) for high-productive software development. Because of its virtue, serverless computing has motivated increasingly new applications and services in the cloud. This, however, also presents new challenges including how to efficiently design high-performance serverless platforms and how to efficiently program on the platforms.

This paper proposes ServerlessBench, an open-source benchmark suite for characterizing serverless platforms. It includes test cases exploring characteristic metrics of serverless computing, e.g., communication efficiency, startup latency, stateless overhead, and performance isolation. We have applied the benchmark suite to evaluate the most popular serverless computing platforms, including AWS Lambda, Open-Whisk, and Fn, and present new serverless implications from the study. For example, we show scenarios where decoupling an application into a composition of serverless functions can be beneficial in cost-saving and performance, and that the "stateless" property in serverless computing can hurt the execution performance of serverless functions. These implications form several design guidelines, which may help platform designers to optimize serverless platforms and application developers to design their functions best fit to the platforms.
Deep-neural-network solution of the electronic Schrödinger equation
Authors: Jan Hermann, Zeno Schätzle, Frank Noé
Date: 2020-09-23 00:00:00
Proceeding: Nature Chemistry 2020 9
Tasks: valid

The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient.
Scalable Sequential Spectral Clustering
Authors: Yeqing Li, Junzhou Huang, Wei Liu
Date: 2016-02-12 00:00:00
Proceeding: AAAI 2016 2
Tasks: Clustering, graph construction, Image Clustering

In the past decades, Spectral Clustering (SC) has become one
of the most effective clustering approaches. Although it has
been widely used, one significant drawback of SC is its expensive computation cost. Many efforts have been devoted to
accelerating SC algorithms and promising results have been
achieved. However, most of the existing algorithms rely on
the assumption that data can be stored in the computer memory. When data cannot fit in the memory, these algorithms
will suffer severe performance degradations. In order to overcome this issue, we propose a novel sequential SC algorithm
for tackling large-scale clustering with limited computational
resources, e.g., memory. We begin with investigating an effective way of approximating the graph affinity matrix via
leveraging a bipartite graph. Then we choose a smart graph
construction and optimization strategy to avoid random access to data. These efforts lead to an efficient SC algorithm
whose memory usage is independent of the number of input data points. Extensive experiments carried out on large
datasets demonstrate that the proposed sequential SC algorithm is up to a thousand times faster than the state-of-thearts.
A Family of Centrality Measures for Graph Data Based on Subgraphs
Authors: Cristian Riveros, Jorge Salas
Date: 2020-05-31 00:00:00
Proceeding: ICDT 2020 5
Tasks: All

We present the theoretical foundations of a new approach in centrality measures for graph data. The
main principle of our approach is very simple: the more relevant subgraphs around a vertex, the more
central it is in the network. We formalize the notion of “relevant subgraphs” by choosing a family of
subgraphs that, give a graph G and a vertex v in G, it assigns a subset of connected subgraphs of
G that contains v. Any of such families defines a measure of centrality by counting the number of
subgraphs assigned to the vertex, i.e., a vertex will be more important for the network if it belongs
to more subgraphs in the family. We show many examples of this approach and, in particular, we
propose the all-subgraphs centrality, a centrality measure that takes every subgraph into account.
We study fundamental properties over families of subgraphs that guarantee desirable properties
over the corresponding centrality measure. Interestingly, all-subgraphs centrality satisfies all these
properties, showing its robustness as a notion for centrality. Finally, we study the computational
complexity of counting certain families of subgraphs and show a polynomial time algorithm to
compute the all-subgraphs centrality for graphs with bounded tree width.
Integrating Material Selection with Design Optimization via Neural Networks
Authors: Aaditya Chandrasekhar, Saketh Sridhara, Krishnan Suresh
Date: 2021-12-23 00:00:00

The engineering design process often entails optimizing the underlying geometry while simultaneously selecting a suitable material. For a certain class of simple problems, the two are separable where, for example, one can first select an optimal material, and then optimize the geometry. However, in general, the two are not separable. Furthermore, the discrete nature of material selection is not compatible with gradient-based geometry optimization, making simultaneous optimization challenging. In this paper, we propose the use of variational autoencoders (VAE) for simultaneous optimization. First, a data-driven VAE is used to project the discrete material database onto a continuous and differentiable latent space. This is then coupled with a fully-connected neural network, embedded with a finite-element solver, to simultaneously optimize the material and geometry. The neural-network's built-in gradient optimizer and back-propagation are exploited during optimization. The proposed framework is demonstrated using trusses, where an optimal material needs to be chosen from a database, while simultaneously optimizing the cross-sectional areas of the truss members. Several numerical examples illustrate the efficacy of the proposed framework. The Python code used in these experiments is available at github.com/UW-ERSL/MaTruss
Cost-effective Outbreak Detection in Networks
Authors: Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, Natalie Glance
Date: 2007-08-12 00:00:00
Proceeding: SIGKDD 2007 8

Given a water distribution network, where should we place
sensors to quickly detect contaminants? Or, which blogs
should we read to avoid missing important stories?
These seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes
(sensor locations, blogs) in a network, in order to detect the
spreading of a virus or information as quickly as possible.
We present a general methodology for near optimal sensor
placement in these and related problems. We demonstrate
that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of “submodularity”. We exploit submodularity to develop an efficient algorithm that scales to large problems,
achieving near optimal placements, while being 700 times
faster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by
any algorithm. Our algorithms and bounds also handle cases
where nodes (sensor locations, blogs) have different costs.
We evaluate our approach on several large real-world problems, including a model of a water distribution network from
the EPA, and real blog data. The obtained sensor placements are provably near optimal, providing a constant fraction of the optimal solution. We show that the approach
scales, achieving speedups and savings in storage of several
orders of magnitude. We also show how the approach leads
to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions
Optimal learning of high-dimensional classification problems using deep neural networks
Authors: Philipp Petersen, Felix Voigtlaender
Date: 2021-12-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the problem of learning classification functions from noiseless training samples, under the assumption that the decision boundary is of a certain regularity. We establish universal lower bounds for this estimation problem, for general classes of continuous decision boundaries. For the class of locally Barron-regular decision boundaries, we find that the optimal estimation rates are essentially independent of the underlying dimension and can be realized by empirical risk minimization methods over a suitable class of deep neural networks. These results are based on novel estimates of the $L^1$ and $L^\infty$ entropies of the class of Barron-regular functions.
Comparison of emergency department trauma triage performance of clinicians and clinical prediction models: a cohort study in India
Authors: Ludvig Wärnberg Gerdin, Monty Khajanchi, Vineet Kumar, Nobhojit Roy, Makhan Lal Saha, Kapil Dev Soni, Anurag Mishra, Jyoti Kamble, Nitin Borle, Chandrika Prasad Verma, Martin Gerdin Wärnberg
Date: 2020-02-18 00:00:00
Proceeding: BMJ Open 2020 2

Objective: The aim of this study was to evaluate and compare the abilities of clinicians and clinical prediction models to accurately triage emergency department (ED) trauma patients. We compared the decisions made by clinicians with the Revised Trauma Score (RTS), the Glasgow Coma Scale, Age and Systolic Blood Pressure (GAP) score, the Kampala Trauma Score (KTS) and the Gerdin et al model.

Design: Prospective cohort study.

Setting: Three hospitals in urban India.

Participants: In total, 7697 adult patients who presented to participating hospitals with a history of trauma were approached for enrolment. The final study sample included 5155 patients. The majority (4023, 78.0%) were male.

Main outcome measure: The patient outcome was mortality within 30 days of arrival at the participating hospital. A grid search was used to identify model cut-off values. Clinicians and categorised models were evaluated and compared using the area under the receiver operating characteristics curve (AUROCC) and net reclassification improvement in non-survivors (NRI+) and survivors (NRI-) separately.

Results: The differences in AUROCC between each categorised model and the clinicians were 0.016 (95% CI -0.014 to 0.045) for RTS, 0.019 (95% CI -0.007 to 0.058) for GAP, 0.054 (95% CI 0.033 to 0.077) for KTS and -0.007 (95% CI -0.035 to 0.03) for Gerdin et al. The NRI+ for each model were -0.235 (-0.37 to -0.116), 0.17 (-0.042 to 0.405), 0.55 (0.47 to 0.65) and 0.22 (0.11 to 0.717), respectively. The NRI- were 0.385 (0.348 to 0.4), -0.059 (-0.476 to -0.005), -0.162 (-0.18 to -0.146) and 0.039 (-0.229 to 0.06), respectively.

Conclusion: The findings of this study suggest that there are no substantial differences in discrimination and net reclassification improvement between clinicians and all four clinical prediction models when using 30-day mortality as the outcome of ED trauma triage in adult patients.

Trial registration number: ClinicalTrials.gov Registry (NCT02838459).
Equivariance and generalization in neural networks
Authors: Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh
Date: 2021-12-23 00:00:00

The crucial role played by the underlying symmetries of high energy physics and lattice field theories calls for the implementation of such symmetries in the neural network architectures that are applied to the physical system under consideration. In these proceedings, we focus on the consequences of incorporating translational equivariance among the network properties, particularly in terms of performance and generalization. The benefits of equivariant networks are exemplified by studying a complex scalar field theory, on which various regression and classification tasks are examined. For a meaningful comparison, promising equivariant and non-equivariant architectures are identified by means of a systematic search. The results indicate that in most of the tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes.
Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks
Authors: Abdalla Elmokhtar Ahmed Elesawi, Kyeong Soo Kim
Date: 2021-12-23 00:00:00
Tasks: Indoor Localization

There has been an increasing tendency to move from outdoor to indoor lifestyle in modern cities. The emergence of big shopping malls, indoor sports complexes, factories, and warehouses is accelerating this tendency. In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those indoor facilities. One of the most economical and practical approaches to indoor localization is Wi-Fi fingerprinting, which exploits the widely-deployed Wi-Fi networks using mobile devices (e.g., smartphones) without any modification of the existing infrastructure. Traditional Wi-Fi fingerprinting schemes rely on complicated data pre/post-processing and time-consuming manual parameter tuning. In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data pre/post-processing and with less parameter tuning. The RNN in the proposed scheme estimates locations in a sequential manner from a general to a specific one (e.g., building->floor->location) in order to exploit the hierarchical nature of the localization in multi-building and multi-floor environments. The experimental results with the UJIIndoorLoc dataset demonstrate that the proposed scheme estimates building and floor with 100% and 95.24% accuracy, respectively, and provides three-dimensional positioning error of 8.62 m, which outperforms existing deep neural network-based schemes.
Generalization capabilities of neural networks in lattice applications
Authors: Srinath Bulusu, Matteo Favoni, Andreas Ipp, David I. Müller, Daniel Schuh
Date: 2021-12-23 00:00:00

In recent years, the use of machine learning has become increasingly popular in the context of lattice field theories. An essential element of such theories is represented by symmetries, whose inclusion in the neural network properties can lead to high reward in terms of performance and generalizability. A fundamental symmetry that usually characterizes physical systems on a lattice with periodic boundary conditions is equivariance under spacetime translations. Here we investigate the advantages of adopting translationally equivariant neural networks in favor of non-equivariant ones. The system we consider is a complex scalar field with quartic interaction on a two-dimensional lattice in the flux representation, on which the networks carry out various regression and classification tasks. Promising equivariant and non-equivariant architectures are identified with a systematic search. We demonstrate that in most of these tasks our best equivariant architectures can perform and generalize significantly better than their non-equivariant counterparts, which applies not only to physical parameters beyond those represented in the training set, but also to different lattice sizes.
Spawning rings of exceptional points out of Dirac cones
Authors: Bo Zhen, Chia Wei Hsu, Yuichi Igarashi, Ling Lu, Ido Kaminer, Adi Pick, Song-Liang Chua, John D. Joannopoulos & Marin Soljačić
Date: 2015-09-01 00:00:00
Proceeding: journal 2015 9

The Dirac cone underlies many unique electronic properties of graphene1 and topological insulators, and its band structure—two conical bands touching at a single point—has also been realized for photons in waveguide arrays2, atoms in optical lattices3, and through accidental degeneracy4,5. Deformation of the Dirac cone often reveals intriguing properties; an example is the quantum Hall effect, where a constant magnetic field breaks the Dirac cone into isolated Landau levels. A seemingly unrelated phenomenon is the exceptional point6,7, also known as the parity–time symmetry breaking point8,9,10,11, where two resonances coincide in both their positions and widths. Exceptional points lead to counter-intuitive phenomena such as loss-induced transparency12, unidirectional transmission or reflection11,13,14, and lasers with reversed pump dependence15 or single-mode operation16,17. Dirac cones and exceptional points are connected: it was theoretically suggested that certain non-Hermitian perturbations can deform a Dirac cone and spawn a ring of exceptional points18,19,20. Here we experimentally demonstrate such an ‘exceptional ring’ in a photonic crystal slab. Angle-resolved reflection measurements of the photonic crystal slab reveal that the peaks of reflectivity follow the conical band structure of a Dirac cone resulting from accidental degeneracy, whereas the complex eigenvalues of the system are deformed into a two-dimensional flat band enclosed by an exceptional ring. This deformation arises from the dissimilar radiation rates of dipole and quadrupole resonances, which play a role analogous to the loss and gain in parity–time symmetric systems. Our results indicate that the radiation existing in any open system can fundamentally alter its physical properties in ways previously expected only in the presence of material loss and gain.