Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Infectious Disease Risk Perception on Perceived Retail Crowding: With Special Reference to Retail Industry in Sri Lanka

The global pandemic of COVID 19 has changed consumer behaviour to reduce the risk. This is common for all interpersonal interactions of individuals especially in maintaining the recommended interpersonal distance based on the recommendations from the health experts. Sri Lanka as a developing country affected by COVID 19, observed changes in individuals' day today’ consumption decision making due to pandemic. Importantly the retailing sector is highly influenced by the conditions since the frequency of interpersonal interactions and degree of interaction is higher in the retailing sector. Interestingly the retailing sector with its largest contributor, the SMEs need to identify the unique changes that happen to the consumer behaviour in responding to them. Accordingly, the objective of the study is to examine the impact of infectious disease risk on the perceived retail crowding in two perspectives of human density and the spatial density. The study has used a quantitative survey in collecting data with the online self-administrated questionnaire with a sample of 100 consumers. Data analysis has been done by using a PLS-SEM with the support of SmartPLS version 3. The key findings of the study emphasize the significant positive influence of infectious disease risk on human density as well as on the spatial Sri Lankan Journal of Entrepreneurship Volume 02 | Issue 01 | September 2020
Connection among Stochastic Hamilton–Jacobi–Bellman Equation, Path-Integral, and Koopman Operator on Nonlinear Stochastic Optimal Control

This paper gives a new insight into nonlinear stochastic optimal control problems from the perspective of Koopman operators. The Koopman operator is a linear map from functions to functions, which stems from the original system dynamics. Although the Koopman operator is infinite-dimensional, only a specific type of observable is enough to be focused on in the control problem. This fact becomes easier to understand via the path-integral control. Furthermore, the focus on the specific observable leads to a natural power-series expansion; coupled ordinary differential equations for discrete-state space systems are derived. The connection among the stochastic Hamilton-Jacobi-Bellman equation, the path-integral control, and the Koopman operator is clarified, and a demonstration for nonlinear stochastic optimal control shows the derived equations work well.
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU

Data sparsity is one of the key challenges associated with model development in Natural Language Understanding (NLU) for conversational agents. The challenge is made more complex by the demand for high quality annotated utterances commonly required for supervised learning, usually resulting in weeks of manual labor and high cost. In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN). We explore data generation in the context of two tasks, the bootstrapping of a new language and the handling of low resource features. For both tasks we explore three sequential GAN architectures, one with a token-level reward function, another with our own implementation of a token-level Monte Carlo rollout reward, and a third with sentence-level reward. We evaluate the performance of these feedback models across several sampling methodologies and compare our results to upsampling the original data to the same scale. We further improve the GAN model performance through the transfer learning of the pretrained embeddings. Our experiments reveal synthetic data generated using the sequential generative adversarial network provides significant performance boosts across multiple metrics and can be a major benefit to the NLU tasks.
SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification

The classification of electrocardiogram (ECG) signals, which takes much time and suffers from a high rate of misjudgment, is recognized as an extremely challenging task for cardiologists. The major difficulty of the ECG signals classification is caused by the long-term sequence dependencies. Most existing approaches for ECG signal classification use Recurrent Neural Network models, e.g., LSTM and GRU, which are unable to extract accurate features for such long sequences. Other approaches utilize 1-Dimensional Convolutional Neural Network (CNN), such as ResNet or its variant, and they can not make good use of the multi-lead information from ECG signals.Based on the above observations, we develop a multi-scale deep residual network for the ECG signal classification task. We are the first to propose to treat the multi-lead signal as a 2-dimensional matrix and combines multi-scale 2-D convolution blocks with 1-D convolution blocks for feature extraction. Our proposed model achieves 99.2% F1-score in the MIT-BIH dataset and 89.4% F1-score in Alibaba dataset and outperforms the state-of-the-art performance by 2% and 3%, respectively, view related code and data at https://github.com/Amadeuszhao/SE-ECGNet
Large Deviation Principles for Lacunary Sums.

Let $(a_k)_{k\in\mathbb N}$ be a sequence of integers satisfying the Hadamard gap condition $a_{k+1}/a_k>q>1$ for all $k\in\mathbb N$, and let $$ S_n(\omega) = \sum_{k=1}^n\cos(2\pi a_k \omega),\qquad n\in\mathbb N,\;\omega\in [0,1]. $$ The lacunary trigonometric sum $S_n$ is known to exhibit several properties typical for sums of independent random variables. In this paper we initiate the investigation of large deviation principles (LDPs) for $S_n$. Under the large gap condition $a_{k+1}/a_k\to\infty$, we prove that $(S_n/n)_{n\in\mathbb N}$ satisfies an LDP with speed $n$ and the same rate function $\tilde{I}$ as for sums of independent random variables with the arcsine distribution, but show that the LDP may fail to hold when we only assume the Hadamard gap condition. However, we prove that in the special case $a_k=q^k$ for some $q\in \{2,3,\ldots\}$, $(S_n/n)_{n\in\mathbb N}$ satisfies an LDP with speed $n$ and a rate function $I_q$ different from $\tilde{I}$. We also show that $I_q$ converges pointwise to $\tilde I$ as $q\to\infty$ and construct a random perturbation $(a_k)_{k\in\mathbb N}$ of the sequence $(2^k)_{k\in\mathbb N}$ for which $a_{k+1}/a_k\to 2$ as $k\to\infty$, but for which $(S_n/n)_{n\in\mathbb N}$ satisfies an LDP with the rate function $\tilde{I}$ as in the independent case and not, as one might na{\"i}vely expect, with rate function $I_2$. We relate this fact to the number of solutions of certain Diophantine equations. Our results show that LDPs for lacunary trigonometric sums are sensitive to the arithmetic properties of $(a_k)_{k\in\mathbb N}$. This is particularly noteworthy since no such arithmetic effects are visible in the central limit theorem by Salem and Zygmund or in the law of the iterated logarithm by Erd\"os and G\'al. Our proofs use a combination of tools from probability theory, harmonic analysis, and dynamical systems.
Lookahead optimizer improves the performance of Convolutional Autoencoders for reconstruction of natural images

Autoencoders are a class of artificial neural networks which have gained a lot of attention in the recent past. Using the encoder block of an autoencoder the input image can be compressed into a meaningful representation. Then a decoder is employed to reconstruct the compressed representation back to a version which looks like the input image. It has plenty of applications in the field of data compression and denoising. Another version of Autoencoders (AE) exist, called Variational AE (VAE) which acts as a generative model like GAN. Recently, an optimizer was introduced which is known as lookahead optimizer which significantly enhances the performances of Adam as well as SGD. In this paper, we implement Convolutional Autoencoders (CAE) and Convolutional Variational Autoencoders (CVAE) with lookahead optimizer (with Adam) and compare them with the Adam (only) optimizer counterparts. For this purpose, we have used a movie dataset comprising of natural images for the former case and CIFAR100 for the latter case. We show that lookahead optimizer (with Adam) improves the performance of CAEs for reconstruction of natural images.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems

Plug-and-Play (PnP) is a non-convex framework that combines proximal algorithms, for example alternating direction method of multipliers (ADMM), with advanced denoiser priors. Over the past few years, great empirical success has been obtained by PnP algorithms, especially for the ones integrated with deep learning-based denoisers. However, a crucial issue of PnP approaches is the need of manual parameter tweaking. As it is essential to obtain high-quality results across the high discrepancy in terms of imaging conditions and varying scene content. In this work, we present a tuning-free PnP proximal algorithm, which can automatically determine the internal parameters including the penalty parameter, the denoising strength and the termination time. A core part of our approach is to develop a policy network for automatic search of parameters, which can be effectively learned via mixed model-free and model-based deep reinforcement learning. We demonstrate, through a set of numerical and visual experiments, that the learned policy can customize different parameters for different states, and often more efficient and effective than existing handcrafted criteria. Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield to state-of-the-art results. This is prevalent on both linear and nonlinear exemplary inverse imaging problems, and in particular, we show promising results on compressed sensing MRI, sparse-view CT and phase retrieval.
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in face morphing attack detection is developing rapidly, however very few datasets with several forms of attacks are publicly available. This paper bridges this gap by providing a new dataset with four different types of morphing attacks, based on OpenCV, FaceMorpher, WebMorph and a generative adversarial network (StyleGAN), generated with original face images from three public face datasets. We also conduct extensive experiments to assess the vulnerability of the state-of-the-art face recognition systems, notably FaceNet, VGG-Face, and ArcFace. The experiments demonstrate that VGG-Face, while being less accurate face recognition system compared to FaceNet, is also less vulnerable to morphing attacks. Also, we observed that naive morphs generated with a StyleGAN do not pose a significant threat.
Revisiting the Integrated Star Formation Law. II. Starbursts and the Combined Global Schmidt Law

We compile observations of molecular gas contents and infrared-based star formation rates (SFRs) for 112 circumnuclear star-forming regions, in order to reinvestigate the form of the disk-averaged Schmidt surface density star-formation law in starbursts. We then combine these results with total gas and SFR surface densities for 153 nearby nonstarbursting disk galaxies from de los Reyes & Kennicutt (2019), to investigate the properties of the combined star formation law, following Kennicutt (1998). We confirm that the combined Schmidt law can be fitted with a single power law with slope n = 1.5 ± 0.05 (including fitting method uncertainties), somewhat steeper than the value n = 1.4 ± 0.15 found by Kennicutt. Fitting separate power laws to the nonstarbursting and starburst galaxies, however, produces very different slopes (n = 1.34 ± 0.07 and 0.98 ± 0.07, respectively), with a pronounced offset in the zero-point (∼0.6 dex) of the starburst relation to higher SFR surface densities. This offset is seen even when a common conversion factor between CO intensity and molecular hydrogen surface density is applied, and it is confirmed when disk surface densities of interstellar dust are used as proxies for gas measurements. Tests for possible systematic biases in the starburst data fail to uncover any spurious sources for such a large offset. We tentatively conclude that the global Schmidt law in galaxies, at least as it is conventionally measured, is bimodal or possibly multimodal. Possible causes may include changes in the small-scale structure of the molecular interstellar medium or the stellar initial mass function. A single n ∼ 1.5 power law still remains as a credible approximation or “recipe” for analytical or numerical models of galaxy formation and evolution.
Automatic Generation of Interpretable Lung Cancer Scoring Models from Chest X-Ray Images

Lung cancer is the leading cause of cancer death worldwide with early detection being the key to a positive patient prognosis. Although a multitude of studies have demonstrated that machine learning, and particularly deep learning, techniques are effective at automatically diagnosing lung cancer, these techniques have yet to be clinically approved and adopted by the medical community. Most research in this field is focused on the narrow task of nodule detection to provide an artificial radiological second reading. We instead focus on extracting, from chest X-ray images, a wider range of pathologies associated with lung cancer using a computer vision model trained on a large dataset. We then find the set of best fit decision trees against an independent, smaller dataset for which lung cancer malignancy metadata is provided. For this small inferencing dataset, our best model achieves sensitivity and specificity of 85% and 75% respectively with a positive predictive value of 85% which is comparable to the performance of human radiologists. Furthermore, the decision trees created by this method may be considered as a starting point for refinement by medical experts into clinically usable multi-variate lung cancer scoring and diagnostic models.
Automatic Diagnosis of Malaria from Thin Blood Smear Images using Deep Convolutional Neural Network with Multi-Resolution Feature Fusion

Malaria, a life-threatening disease, infects millions of people every year throughout the world demanding faster diagnosis for proper treatment before any damages occur. In this paper, an end-to-end deep learning-based approach is proposed for faster diagnosis of malaria from thin blood smear images by making efficient optimizations of features extracted from diversified receptive fields. Firstly, an efficient, highly scalable deep neural network, named as DilationNet, is proposed that incorporates features from a large spectrum by varying dilation rates of convolutions to extract features from different receptive areas. Next, the raw images are resampled to various resolutions to introduce variations in the receptive fields that are used for independently optimizing different forms of DilationNet scaled for different resolutions of images. Afterward, a feature fusion scheme is introduced with the proposed DeepFusionNet architecture for jointly optimizing the feature space of these individually trained networks operating on different levels of observations. All the convolutional layers of various forms of DilationNets that are optimized to extract spatial features from different resolutions of images are directly transferred to provide a variegated feature space from any image. Later, joint optimization of these spatial features is carried out in the DeepFusionNet to extract the most relevant representation of the sample image. This scheme offers the opportunity to explore the feature space extensively by varying the observation level to accurately diagnose the abnormality. Intense experimentations on a publicly available dataset show outstanding performance with accuracy over 99.5% outperforming other state-of-the-art approaches.
Explainable Link Prediction for Privacy-Preserving Contact Tracing

Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.
Entanglement-equivalences of covariant channels

We define two channels N and N' to be entanglement-equivalent if there exists an entanglement-assisted source channel coding scheme for N from N' and vice versa. For example, the noiseless channel on a d^2-level classical system and the noiseless channel on a d-dimensional quantum system are entanglement-equivalent by teleportation and dense coding. We provide a construction of entanglement-equivalences of channels based on the principle of covariance. Let G be a compact quantum group, let F': Rep(G) -> Hilb be a fibre functor accessible from the canonical fibre functor F: Rep(G) -> Hilb by a unitary pseudonatural transformation, and let G' be the compact quantum group obtained from the fibre functor F' by Tannaka duality. Then for any G-covariant channel N we construct a G'-covariant channel N' and entanglement-assisted source channel coding schemes witnessing an entanglement-equivalence of N and N'. This construction induces an equivalence of categories E: Chan(G) -> Chan(G'), where Chan(G) is the category of G-C*-algebras and covariant channels. We show that all tight teleportation and dense coding schemes are a special case of our construction, using covariance for the action of the quantum permutation group of the algebra M_d(\mathbb{C}). Where G is a finite abelian group, we explicitly compute the effect of the equivalence E on algebras and channels. We thereby produce concrete new examples of entanglement-equivalences of channels in the case where G=Z_2 \times Z_2 and the noncanonical fibre functor is specified by the Pauli matrices.
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks

Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-modal clinical and imaging data in the presence of competing risks based on neural networks and an extension of the multi-task logistic regression framework. We demonstrate improved prognostic performance of the multi-modal approach over single modality predictors in a cohort of 2552 head and neck cancer patients, particularly for cancer specific survival, where our approach achieves 2-year AUROC of 0.774 and $C$-index of 0.788.
OneNet: Towards End-to-End One-Stage Object Detection

End-to-end one-stage object detection trailed thus far. This paper discovers that the lack of classification cost between sample and ground-truth in label assignment is the main obstacle for one-stage detectors to remove Non-maximum Suppression(NMS) and reach end-to-end. Existing one-stage object detectors assign labels by only location cost, e.g. box IoU or point distance. Without classification cost, sole location cost leads to redundant boxes of high confidence scores in inference, making NMS necessary post-processing. To design an end-to-end one-stage object detector, we propose Minimum Cost Assignment. The cost is the summation of classification cost and location cost between sample and ground-truth. For each object ground-truth, only one sample of minimum cost is assigned as the positive sample; others are all negative samples. To evaluate the effectiveness of our method, we design an extremely simple one-stage detector named OneNet. Our results show that when trained with Minimum Cost Assignment, OneNet avoids producing duplicated boxes and achieves to end-to-end detector. On COCO dataset, OneNet achieves 35.0 AP/80 FPS and 37.7 AP/50 FPS with image size of 512 pixels. We hope OneNet could serve as an effective baseline for end-to-end one-stage object detection. The code is available at: \url{this https URL}.
Geometric Algorithms for Sampling the Flux Space of Metabolic Networks

Systems Biology is a fundamental field and paradigm that introduces a new era in Biology. The crux of its functionality and usefulness relies on metabolic networks that model the reactions occurring inside an organism and provide the means to understand the underlying mechanisms that govern biological systems. Even more, metabolic networks have a broader impact that ranges from resolution of ecosystems to personalized medicine.The analysis of metabolic networks is a computational geometry oriented field as one of the main operations they depend on is sampling uniformly points from polytopes; the latter provides a representation of the steady states of the metabolic networks. However, the polytopes that result from biological data are of very high dimension (to the order of thousands) and in most, if not all, the cases are considerably skinny. Therefore, to perform uniform random sampling efficiently in this setting, we need a novel algorithmic and computational framework specially tailored for the properties of metabolic networks.We present a complete software framework to handle sampling in metabolic networks. Its backbone is a Multiphase Monte Carlo Sampling (MMCS) algorithm that unifies rounding and sampling in one pass, obtaining both upon termination. It exploits an improved variant of the Billiard Walk that enjoys faster arithmetic complexity per step. We demonstrate the efficiency of our approach by performing extensive experiments on various metabolic networks. Notably, sampling on the most complicated human metabolic network accessible today, Recon3D, corresponding to a polytope of dimension 5 335 took less than 30 hours. To our knowledge, that is out of reach for existing software.
Edge Computing Assisted Autonomous Flight for UAV: Synergies between Vision and Communications

Autonomous flight for UAVs relies on visual information for avoiding obstacles and ensuring safe collision-free flight. In addition to visual clues, safe UAVs often need connectivity with the ground station. In this article, we study the synergies between vision and communications for edge-computing-enabled UAV flight. By proposing a framework of edge computing assisted autonomous flight (ECAAF), we illustrate that vision and communications can interact with and assist each other with the aid of edge computing and offloading, and further speed up UAV mission completion. ECAAF consists of three functionalities that are discussed in detail: edge computing for 3D map acquisition, radio map construction from the 3D map, and online trajectory planning. During ECAAF, the interactions of communication capacity, video offloading, 3D map quality, and channel state of the trajectory form a positive feedback loop. Simulation results verify that the proposed method can improve mission performance by enhancing connectivity. Finally, we conclude with some future research directions.