pwc-archive/papers-with-abstracts huggingface dataset:
Quantum Self-Supervised Learning
Authors: Ben Jaderberg, Lewis W. Anderson, Weidi Xie, Samuel Albanie, Martin Kiffner, Dieter Jaksch
Date: 2021-03-26 00:00:00
Tasks: Self-Supervised Learning

The resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation. However, the staggering computational complexity of these methods is such that for state-of-the-art performance, classical hardware requirements represent a significant bottleneck to further progress. Here we take the first steps to understanding whether quantum neural networks could meet the demand for more powerful architectures and test its effectiveness in proof-of-principle hybrid experiments. Interestingly, we observe a numerical advantage for the learning of visual representations using small-scale quantum neural networks over equivalently structured classical networks, even when the quantum circuits are sampled with only 100 shots. Furthermore, we apply our best quantum model to classify unseen images on the ibmq\_paris quantum computer and find that current noisy devices can already achieve equal accuracy to the equivalent classical model on downstream tasks.
Backpropagation Through Time For Networks With Long-Term Dependencies
Authors: George Bird, Maxim E. Polivoda
Date: 2021-03-26 00:00:00

Backpropagation through time (BPTT) is a technique of updating tuned parameters within recurrent neural networks (RNNs). Several attempts at creating such an algorithm have been made including: Nth Ordered Approximations and Truncated-BPTT. These methods approximate the backpropagation gradients under the assumption that the RNN only utilises short-term dependencies. This is an acceptable assumption to make for the current state of artificial neural networks. As RNNs become more advanced, a shift towards influence by long-term dependencies is likely. Thus, a new method for backpropagation is required. We propose using the 'discrete forward sensitivity equation' and a variant of it for single and multiple interacting recurrent loops respectively. This solution is exact and also allows the network's parameters to vary between each subsequent step, however it does require the computation of a Jacobian.
Contrastive Domain Adaptation
Authors: Mamatha Thota, Georgios Leontidis
Date: 2021-03-26 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Self-Supervised Learning

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.
Explore the Knowledge contained in Network Weights to Obtain Sparse Neural Networks
Authors: Mengqiao Han, Xiabi Liu, Zhaoyang Hai, Zhengwen Li
Date: 2021-03-26 00:00:00
Tasks: image-classification, Image Classification

Sparse neural networks are important for achieving better generalization and enhancing computation efficiency. This paper proposes a novel learning approach to obtain sparse fully connected layers in neural networks (NNs) automatically. We design a switcher neural network (SNN) to optimize the structure of the task neural network (TNN). The SNN takes the weights of the TNN as the inputs and its outputs are used to switch the connections of TNN. In this way, the knowledge contained in the weights of TNN is explored to determine the importance of each connection and the structure of TNN consequently. The SNN and TNN are learned alternately with stochastic gradient descent (SGD) optimization, targeting at a common objective. After learning, we achieve the optimal structure and the optimal parameters of the TNN simultaneously. In order to evaluate the proposed approach, we conduct image classification experiments on various network structures and datasets. The network structures include LeNet, ResNet18, ResNet34, VggNet16 and MobileNet. The datasets include MNIST, CIFAR10 and CIFAR100. The experimental results show that our approach can stably lead to sparse and well-performing fully connected layers in NNs.
Current Trends and Applications of Dempster-Shafer Theory (Review)
Authors: V. K. Ivanov, N . V. Vinogradova, B. V. Palyukh, A. N. Sotnikov
Date: 2021-03-26 00:00:00

The article provides a review of the publications on the current trends and developments in Dempster-Shafer theory and its different applications in science, engineering, and technologies. The review took account of the following provisions with a focus on some specific aspects of the theory. Firstly, the article considers the research directions whose results are known not only in scientific and academic community but understood by a wide circle of potential designers and developers of advanced engineering solutions and technologies. Secondly, the article shows the theory applications in some important areas of human activity such as manufacturing systems, diagnostics of technological processes, materials and products, building and construction, product quality control, economic and social systems. The particular attention is paid to the current state of research in the domains under consideration and, thus, the papers published, as a rule, in recent years and presenting the achievements of modern research on Dempster-Shafer theory and its application are selected and analyzed.
Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting
Authors: Qi-Qiao He, Patrick Cheong-Iao Pang, Yain-Whar Si
Date: 2021-03-26 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Transfer Learning

Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and single-source transfer learning models are less effective. Therefore, in this paper, we investigate multi-source deep transfer learning for financial time series. We propose two multi-source transfer learning methods namely Weighted Average Ensemble for Transfer Learning (WAETL) and Tree-structured Parzen Estimator Ensemble Selection (TPEES). The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results reveal that TPEES outperforms other baseline methods on majority of multi-source transfer tasks.
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Authors: Cong Hao, Jordan Dotzel, JinJun Xiong, Luca Benini, Zhiru Zhang, Deming Chen
Date: 2021-03-25 00:00:00
Tasks: Benchmarking, Edge-computing

Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives. Empowered by edge computing, AI workloads are migrating from centralized cloud architectures to distributed edge systems, introducing a new paradigm called edge AI. While edge AI has the promise of bringing significant increases in autonomy and intelligence into everyday lives through common edge devices, it also raises new challenges, especially for the development of its algorithms and the deployment of its services, which call for novel design methodologies catered to these unique challenges. In this paper, we provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design. We discuss representative methodologies in each category in detail, including on-device training methods, specialized software design, dedicated hardware design, benchmarking and design automation, software/hardware co-design, software/compiler co-design, and compiler/hardware co-design. Moreover, we attempt to reveal hidden cross-layer design opportunities that can further boost the solution quality of future edge AI and provide insights into future directions and emerging areas that require increased research focus.
Adaptive edge detection algorithm for multi-focus application
Authors: Trong-An Bui; Pei-Jun Lee
Date: 2018-07-12 00:00:00
Tasks: Clustering, Edge Detection

This paper proposes a method of solving the problem of multi-focus through edge detection based on adaptive thresholding. Under conditions such as different light or different regions, the methods of solving the problem of multi-focus based on conventional edge detection methods will not work well because they are based on a global value of thresholding. In this paper, we will focus on processing and be applying the adaptive threshold method in determining the regions in the multi-focus problem. We will combine the clustering method as k-mean in an image to combine with the area defined by edge detection through adaptive thresholding to define the best result. The Fusion algorithm is applied to handle focused regions defined by the above method. The results and deployment environment we apply to mobile devices.
Adaptive multi-focus regions defining and implementation on mobile phone
Authors: Trong-An Bui
Date: 2018-06-30 00:00:00
Tasks: Contour Detection

Multi–focus application is not an unknown new thread in image processing. At the present, there are have many methods and solutions to solve this problem, but each solution has its own advantages and disadvantages.  
In addition, in recent times, the rapid development of mobile multi-focus processing has been a trend for new technology development, so multi-focus processing is being improved efficiency and quality.
This thesis proposes an object based multi-focus method which is implemented on mobile device. 
To achieve low computation complexity in the same object determination for multi input image, this thesis adapts Oriented FAST and Rotated BRIEF algorithm to detect feature points. The most important is the precise detection of the regions through Edge and Contour detection.  Then apply the Fusion algorithm to process the region recognition results.
 To perform the identification of focused regions and the objects within the image, this thesis proposes the method of aggregating information from the recognition of the edge on image. In this thesis, we will focus on building edge and contour detection based on Adaptive Threshold. This method, the maximum efficiency compared to the method through Global Threshold. Plus, that's the information of the image through partitioning and incorporating algorithms in the image processing to determine the focus areas of the image.
By all algorithms and implementing methods, we will carry out entirely on mobile platform. We intend to take advantage of smartphone’s enhanced flexibility and high performance to make the selection of areas via the touch screen of the mobile device.
Star-shaped Risk Measures
Authors: Erio Castagnoli, Giacomo Cattelan, Fabio Maccheroni, Claudio Tebaldi, Ruodu Wang
Date: 2021-03-29 00:00:00

In this paper monetary risk measures that are positively superhomogeneous, called star-shaped risk measures, are characterized and their properties studied. The measures in this class, which arise when the controversial subadditivity property of coherent risk measures is dispensed with and positive homogeneity is weakened, include all practically used risk measures, in particular, both convex risk measures and Value-at-Risk. From a financial viewpoint, our relaxation of convexity is necessary to quantify the capital requirements for risk exposure in the presence of liquidity risk, competitive delegation, or robust aggregation mechanisms. From a decision theoretical perspective, star-shaped risk measures emerge from variational preferences when risk mitigation strategies can be adopted by a rational decision maker.
A quantitative assessment of epidemiological parameters to model COVID- 19 burden
Authors: Agnese Zardini, Margherita Galli, Marcello Tirani, Danilo Cereda, Mattia Manica, Filippo Trentini, Giorgio Guzzetta, Valentina Marziano, Raffaella Piccarreta, Alessia Melegaro, Marco Ajelli, Piero Poletti, Stefano Merler
Date: 2021-03-29 00:00:00

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1,965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever >= 37.5 {\deg}C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95% CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR 3-21) days; the length of stay in ICU was 11 (IQR 6-19) days. The obtained estimates could be instrumental to refine mathematical modeling work supporting public health decisions.
Modelling the Role of Inter-cellular Communication in Modulating Photosynthesis in Plants
Authors: Hamdan Awan, Raviraj S. Adve, Nigel Wallbridge, Carrol Plummer, Andrew W. Eckford
Date: 2021-03-29 00:00:00

In this paper we show how inter-cellular molecular communication may change the overall levels of photosynthesis in plants. Individual plant cells respond to external stimuli, such as illumination levels, to regulate their photosynthetic output. Here, we present a mathematical model which shows that by sharing information internally using molecular communication, plants may increase overall photosynthate production. Numerical results show that higher mutual information between cells corresponds to an increase in overall photosynthesis by as much as 25 per cent. This suggests that molecular communication plays a vital role in maximising the photosynthesis in plants and therefore suggests new routes to influence plant development in agriculture and elsewhere.
Prototype-based Personalized Pruning
Authors: Jangho Kim, Simyung Chang, Sungrack Yun, Nojun Kwak
Date: 2021-03-25 00:00:00
Tasks: Keyword Spotting, Model Compression

Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitable for edge devices because retraining or finetuning is needed with limited personal data. Also, a full model might be too heavy for edge devices with limited resources. Unfortunately, model compression methods which can handle the model complexity issue also require the retraining phase. These multiple training phases generally need huge computational cost during on-device learning which can be a burden to edge devices. In this work, we propose a dynamic personalization method called prototype-based personalized pruning (PPP). PPP considers both ends of personalization and model efficiency. After training a network, PPP can easily prune the network with a prototype representing the characteristics of personal data and it performs well without retraining or finetuning. We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
Heterogeneous Objectives: State-of-the-Art and Future Research
Authors: Richard Allmendinger, Joshua Knowles
Date: 2021-02-26 00:00:00
Tasks: Multiobjective Optimization

Multiobjective optimization problems with heterogeneous objectives are defined as those that possess significantly different types of objective function components (not just incommensurable in units or scale). For example, in a heterogeneous problem the objective function components may differ in formal computational complexity, practical evaluation effort (time, costs, or resources), determinism (stochastic vs deterministic), or some combination of all three. A particularly challenging variety of heterogeneity may occur by the combination of a time-consuming laboratory-based objective with other objectives that are evaluated using faster computer-based calculations. Perhaps more commonly, all objectives may be evaluated computationally, but some may require a lengthy simulation process while others are computed from a relatively simple closed-form calculation. In this chapter, we motivate the need for more work on the topic of heterogeneous objectives (with reference to real-world examples), expand on a basic taxonomy of heterogeneity types, and review the state of the art in tackling these problems. We give special attention to heterogeneity in evaluation time (latency) as this requires sophisticated approaches. We also present original experimental work on estimating the amount of heterogeneity in evaluation time expected in many-objective problems, given reasonable assumptions, and survey related research threads that could contribute to this area in future.
Pyfectious: An individual-level simulator to discover optimal containment polices for epidemic diseases
Authors: Arash Mehrjou, Ashkan Soleymani, Amin Abyaneh, Samir Bhatt, Bernhard Schölkopf, Stefan Bauer
Date: 2021-03-24 00:00:00

Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease's propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator's adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.
Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy
Authors: Paolo Scarabaggio, Massimo La Scala, Raffaele Carli, Mariagrazia Dotoli
Date: 2020-11-09 00:00:00

The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility.
GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS
Authors: Baohua Sun, Tao Zhang, Jiapeng Su, Hao Sha
Date: 2021-02-19 00:00:00
Tasks: CPU, Object, object-detection, Object Detection

Object detection is widely used on embedded devices. With the wide availability of CNN (Convolutional Neural Networks) accelerator chips, the object detection applications are expected to run with low power consumption, and high inference speed. In addition, the CPU load is expected to be as low as possible for a CNN accelerator chip working as a co-processor with a host CPU. In this paper, we optimize the object detection model on the CNN accelerator chip by minimizing the CPU load. The resulting model is called GnetDet. The experimental result shows that the GnetDet model running on a 224mW chip achieves the speed of 106FPS with excellent accuracy.