Reviewerly/exHarmony huggingface dataset:
Explicitly represent the boundary of ReLU NN as a classifier and measure its robustness

Adversarial examples pose a great threat to the application of neural network as a classifier in areas with high security requirements. Intuitively, the adversarial property of neural network is closely related to the boundary. However, works barely give a rigorous description of it. This paper for the first time studies the boundary of ReLU NN, which separates the sample space into distinct cells, each of which belongs to the same class. During the demonstration, we discuss the algebraic property of border points upon the separating boundary and the topological difference between border points and odd ones. We derive Theorem III.7 to give a rigorous, comprehensive and concise description on the boundary of ReLU NN. Furthermore, an algorithm, based on the description, is proposed to give a standard and exact metric for the local robustness of ReLU NN. This metric gives the local adversarial property of NN and reflects some aspects of its security performance, i.e. the nimimum distortion needed to craft an adversarial example. To the best of our knowledge, this is the first work proposed to give a rigorous and example-specific metric for the local robustness of ReLU NN.
A-Z Database List review: Getting started and keeping the momentum

Libraries have multiple portals to access their electronic resources.At Queens College, the two access portals are the catalog, Ex-Libris' Primo, and Springshare's A-Z Database List. 1 The most recent comprehensive review of the A-Z Database List was conducted five years ago by the previous electronic resources librarian.As such, the list had multiple formats for displaying information and lacked consistency.The supporting information for the overall layout of the list was not helpful to users nor library staff.Users should be able to search for the resources they want without getting bogged down by unnecessary jargon, inconsistencies, and fluff.It was not difficult to get buy in for a comprehensive review.Librarians agreed that the list lacked consistency and a review would be beneficial.Beyond that there was little consensus.Where do we start?How do we approach this? Literature reviewA-Z Database Lists are popular tools.While there are articles, conference reports, and posters on the subject, each library employs what best suits their needs.
Controversy of quantum congestion in two-particle quantum walks

The article deals with one- and two-particle quantum walks on a graph with Braess-like topology and analyzes the issue of network congestion in the quantum world. Our approach to the study of congestion in quantum networks is based on the comparison of the evolution of bosonic and fermionic many-particle states. We consider a simple example of non-interacting particles, where one can expect the appearance of congestion in the fermion case due to the Pauli principle. It is shown that dependence of the transport efficiency on the parameters of quantum graph is similar in the bosonic and fermionic cases.
THE EFFECT OF MOTIVATION AND PERCEPTION OF COMPENSATION FAIRNESS ON PERFORMANCE MEDIATED BY JOB SATISFACTION ON HOTEL EMPLOYEES IN BANDA ACEH

The purpose of this study was to examine the effect of motivation and compensation fairness on employee performance mediated by job satisfaction on hotel employees in city Banda Aceh.The population used in this study were all employees of 82 hotels in Banda Aceh, amounting to people.Because the population was relatively small, the entire population was sampled.The sampling method used the census method.So the number of samples in this study was respondents.Processing the data and analysis of research results used structural equation modeling (SEM).The results of the study prove that motivation affects job satisfaction, fairness of compensation does not affect job satisfaction, motivation affects employee performance, fairness of compensation affects employee performance, and job satisfaction affects employee performance.The variable with the greatest impact on this hotel employee performance model is employee satisfaction because it has the largest coefficient value compared to other variables.For the indirect hypotheses, the result shows that employee motivation affects employee performance mediated by hotel employees' job satisfaction and the fairness of compensation doesn't affect employee performance even though it is mediated by hotel employees' job satisfaction.
SalFBNet: Learning Pseudo-Saliency Distribution via Feedback  Convolutional Networks

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic relationships and potential benefits of feedback connections in vision tasks such as saliency detection, despite their significant representation capabilities. In this work, we propose a feedback-recursive convolutional framework (SalFBNet) for saliency detection. The proposed feedback model can learn abundant contextual representations by bridging a recursive pathway from higher-level feature blocks to low-level layer. Moreover, we create a large-scale Pseudo-Saliency dataset to alleviate the problem of data deficiency in saliency detection. We first use the proposed feedback model to learn saliency distribution from pseudo-ground-truth. Afterwards, we fine-tune the feedback model on existing eye-fixation datasets. Furthermore, we present a novel Selective Fixation and Non-Fixation Error (sFNE) loss to make proposed feedback model better learn distinguishable eye-fixation-based features. Extensive experimental results show that our SalFBNet with fewer parameters achieves competitive results on the public saliency detection benchmarks, which demonstrate the effectiveness of proposed feedback model and Pseudo-Saliency data. Source codes and Pseudo-Saliency dataset can be found at https://github.com/gqding/SalFBNet
Sequence-to-Sequence Resources for Catalan

In this work, we introduce sequence-to-sequence language resources for Catalan, a moderately under-resourced language, towards two tasks, namely: Summarization and Machine Translation (MT). We present two new abstractive summarization datasets in the domain of newswire. We also introduce a parallel Catalan-English corpus, paired with three different brand new test sets. Finally, we evaluate the data presented with competing state of the art models, and we develop baselines for these tasks using a newly created Catalan BART. We release the resulting resources of this work under open license to encourage the development of language technology in Catalan.
Incoherent control of two-photon induced optical measurements in open quantum systems: quantum heat engine perspective

We present a consistent optimization procedure for the optical measurements in open quantum systems using recently developed incoherent control protocol. Assigning an effective hot bath for the two-entangled-photon pump we recast the transmission of classical probe as a work in a quantum heat engine framework. We demonstrate that maximum work in such a heat engine can exceed that for the classical two-photon and one-photon pumps, while efficiency at maximum power can be attributed to conventional boundaries obtained for three-level maser heat engine. Our results pave the way for incoherent control and optimization of optical measurements in open quantum systems that involve two-photon processes with quantum light.
Cross-Domain Few-Shot Graph Classification

We study the problem of few-shot graph classification across domains with nonequivalent feature spaces by introducing three new cross-domain benchmarks constructed from publicly available datasets. We also propose an attention-based graph encoder that uses three congruent views of graphs, one contextual and two topological views, to learn representations of task-specific information for fast adaptation, and task-agnostic information for knowledge transfer. We run exhaustive experiments to evaluate the performance of contrastive and meta-learning strategies. We show that when coupled with metric-based meta-learning frameworks, the proposed encoder achieves the best average meta-test classification accuracy across all benchmarks. The source code and data will be released here: https://github.com/kavehhassani/metagrl
Understanding AdamW through Proximal Methods and Scale-Freeness

Adam has been widely adopted for training deep neural networks due to less hyperparameter tuning and remarkable performance. To improve generalization, Adam is typically used in tandem with a squared $\ell_2$ regularizer (referred to as Adam-$\ell_2$). However, even better performance can be obtained with AdamW, which decouples the gradient of the regularizer from the update rule of Adam-$\ell_2$. Yet, we are still lacking a complete explanation of the advantages of AdamW. In this paper, we tackle this question from both an optimization and an empirical point of view. First, we show how to re-interpret AdamW as an approximation of a proximal gradient method, which takes advantage of the closed-form proximal mapping of the regularizer instead of only utilizing its gradient information as in Adam-$\ell_2$. Next, we consider the property of "scale-freeness" enjoyed by AdamW and by its proximal counterpart: their updates are invariant to component-wise rescaling of the gradients. We provide empirical evidence across a wide range of deep learning experiments showing a correlation between the problems in which AdamW exhibits an advantage over Adam-$\ell_2$ and the degree to which we expect the gradients of the network to exhibit multiple scales, thus motivating the hypothesis that the advantage of AdamW could be due to the scale-free updates.
Automatic Language Identification for Celtic Texts

Language identification is an important Natural Language Processing task. It has been thoroughly researched in the literature. However, some issues are still open. This work addresses the identification of the related low-resource languages on the example of the Celtic language family. This work's main goals were: (1) to collect the dataset of three Celtic languages; (2) to prepare a method to identify the languages from the Celtic family, i.e. to train a successful classification model; (3) to evaluate the influence of different feature extraction methods, and explore the applicability of the unsupervised models as a feature extraction technique; (4) to experiment with the unsupervised feature extraction on a reduced annotated set. We collected a new dataset including Irish, Scottish, Welsh and English records. We tested supervised models such as SVM and neural networks with traditional statistical features alongside the output of clustering, autoencoder, and topic modelling methods. The analysis showed that the unsupervised features could serve as a valuable extension to the n-gram feature vectors. It led to an improvement in performance for more entangled classes. The best model achieved a 98\% F1 score and 97\% MCC. The dense neural network consistently outperformed the SVM model. The low-resource languages are also challenging due to the scarcity of available annotated training data. This work evaluated the performance of the classifiers using the unsupervised feature extraction on the reduced labelled dataset to handle this issue. The results uncovered that the unsupervised feature vectors are more robust to the labelled set reduction. Therefore, they proved to help achieve comparable classification performance with much less labelled data.
Sparse Markov Models for High-dimensional Inference

Finite order Markov models are theoretically well-studied models for dependent discrete data. Despite their generality, application in empirical work when the order is large is rare. Practitioners avoid using higher order Markov models because (1) the number of parameters grow exponentially with the order and (2) the interpretation is often difficult. Mixture of transition distribution models (MTD) were introduced to overcome both limitations. MTD represent higher order Markov models as a convex mixture of single step Markov chains, reducing the number of parameters and increasing the interpretability. Nevertheless, in practice, estimation of MTD models with large orders are still limited because of curse of dimensionality and high algorithm complexity. Here, we prove that if only few lags are relevant we can consistently and efficiently recover the lags and estimate the transition probabilities of high-dimensional MTD models. The key innovation is a recursive procedure for the selection of the relevant lags of the model. Our results are based on (1) a new structural result of the MTD and (2) an improved martingale concentration inequality. We illustrate our method using simulations and a weather data.
CRISPnet: Color Rendition ISP Net

Image signal processors (ISPs) are historically grown legacy software systems for reconstructing color images from noisy raw sensor measurements. They are usually composited of many heuristic blocks for denoising, demosaicking, and color restoration. Color reproduction in this context is of particular importance, since the raw colors are often severely distorted, and each smart phone manufacturer has developed their own characteristic heuristics for improving the color rendition, for example of skin tones and other visually important colors. In recent years there has been strong interest in replacing the historically grown ISP systems with deep learned pipelines. Much progress has been made in approximating legacy ISPs with such learned models. However, so far the focus of these efforts has been on reproducing the structural features of the images, with less attention paid to color rendition. Here we present CRISPnet, the first learned ISP model to specifically target color rendition accuracy relative to a complex, legacy smart phone ISP. We achieve this by utilizing both image metadata (like a legacy ISP would), as well as by learning simple global semantics based on image classification -- similar to what a legacy ISP does to determine the scene type. We also contribute a new ISP image dataset consisting of both high dynamic range monitor data, as well as real-world data, both captured with an actual cell phone ISP pipeline under a variety of lighting conditions, exposure times, and gain settings.
3-D PET Image Generation with tumour masks using TGAN

Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult due to the lack of training data, labeled samples, and privacy concerns. For this reason, a robust generative method to create synthetic data is highly sought after. However, most three-dimensional image generators require additional image input or are extremely memory intensive. To address these issues we propose adapting video generation techniques for 3-D image generation. Using the temporal GAN (TGAN) architecture, we show we are able to generate realistic head and neck PET images. We also show that by conditioning the generator on tumour masks, we are able to control the geometry and location of the tumour in the generated images. To test the utility of the synthetic images, we train a segmentation model using the synthetic images. Synthetic images conditioned on real tumour masks are automatically segmented, and the corresponding real images are also segmented. We evaluate the segmentations using the Dice score and find the segmentation algorithm performs similarly on both datasets (0.65 synthetic data, 0.70 real data). Various radionomic features are then calculated over the segmented tumour volumes for each data set. A comparison of the real and synthetic feature distributions show that seven of eight feature distributions had statistically insignificant differences (p>0.05). Correlation coefficients were also calculated between all radionomic features and it is shown that all of the strong statistical correlations in the real data set are preserved in the synthetic data set.
Sensor Data Validation and Driving Safety in Autonomous Driving Systems

Autonomous driving technology has drawn a lot of attention due to its fast development and extremely high commercial values. The recent technological leap of autonomous driving can be primarily attributed to the progress in the environment perception. Good environment perception provides accurate high-level environment information which is essential for autonomous vehicles to make safe and precise driving decisions and strategies. Moreover, such progress in accurate environment perception would not be possible without deep learning models and advanced onboard sensors, such as optical sensors (LiDARs and cameras), radars, GPS. However, the advanced sensors and deep learning models are prone to recently invented attack methods. For example, LiDARs and cameras can be compromised by optical attacks, and deep learning models can be attacked by adversarial examples. The attacks on advanced sensors and deep learning models can largely impact the accuracy of the environment perception, posing great threats to the safety and security of autonomous vehicles. In this thesis, we study the detection methods against the attacks on onboard sensors and the linkage between attacked deep learning models and driving safety for autonomous vehicles. To detect the attacks, redundant data sources can be exploited, since information distortions caused by attacks in victim sensor data result in inconsistency with the information from other redundant sources. To study the linkage between attacked deep learning models and driving safety...
On some algorithms for estimation in Gaussian graphical models

In Gaussian graphical models, the likelihood equations must typically be solved iteratively. We investigate two algorithms: A version of iterative proportional scaling which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by neighbourhood coordinate descent, corresponding to the graphical lasso with zero penalty. For large, sparse graphs, the iterative proportional scaling algorithm appears feasible and has simple convergence properties. The algorithm based on neighbourhood coordinate descent is extremely fast and less dependent on sparsity, but needs a positive definite starting value to converge. We give an algorithm for finding such a starting value for graphs with low colouring number. As a consequence, we also obtain a simplified proof for existence of the maximum likelihood estimator in such cases.
Feedabck Analysis: A Process Definition

Customers Feedbacks take an important role for any business. It gives information to the new customers about their interactions with product or service. The objectives of this research work are to find out the degree of happiness of the customers and assist marketing teams in determining the gap between customers’ acceptance and product or service quality. From this gap marketers can improve their product and service quality. Customers share their reviews, ideas, experiences about the product and service quality. In this research work, authors have chosen an automobile service station named Mohan Automobile Service Station, West Bengal. In this research work authors have collected the customers feedback through questioners created in Google form. The collected feedbacks are validated and analyzed to know the insight sentiment using Python Monkey Learn tools. The result of the collected feedback is shown as positive, negative, or neutral based on the polarity of the feedback. The result will be helpful for the service station to know their weak point and they can update their service quality.
A Polyhedral Study of Lifted Multicuts

Fundamental to many applications in data analysis are the decompositions of a graph, i.e. partitions of the node set into component-inducing subsets. One way of encoding decompositions is by multicuts, the subsets of those edges that straddle distinct components. Recently, a lifting of multicuts from a graph $G = (V, E)$ to an augmented graph $\hat G = (V, E \cup F)$ has been proposed in the field of image analysis, with the goal of obtaining a more expressive characterization of graph decompositions in which it is made explicit also for pairs $F \subseteq \tbinom{V}{2} \setminus E$ of non-neighboring nodes whether these are in the same or distinct components. In this work, we study in detail the polytope in $\mathbb{R}^{E \cup F}$ whose vertices are precisely the characteristic vectors of multicuts of $\hat G$ lifted from $G$, connecting it, in particular, to the rich body of prior work on the clique partitioning and multilinear polytope.
Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users' mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.
InsetGAN for Full-Body Image Generation

While GANs can produce photo-realistic images in ideal conditions for certain domains, the generation of full-body human images remains difficult due to the diversity of identities, hairstyles, clothing, and the variance in pose. Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e.g., human body) and a set of specialized GANs, or insets, focus on different parts (e.g., faces, shoes) that can be seamlessly inserted onto the global canvas. We model the problem as jointly exploring the respective latent spaces such that the generated images can be combined, by inserting the parts from the specialized generators onto the global canvas, without introducing seams. We demonstrate the setup by combining a full body GAN with a dedicated high-quality face GAN to produce plausible-looking humans. We evaluate our results with quantitative metrics and user studies.
Mammograms Classification: A Review

An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been utilized in developing computer-aided diagnosis systems that will potentially help in clinical diagnosis. Researchers have proved that artificial intelligence with its emerging technologies can be used in the early detection of the disease and improve radiologists' performance in assessing breast cancer. In this paper, we review the methods developed for mammogram mass classification in two categories. The first one is classifying manually provided cropped region of interests (ROI) as either malignant or benign, and the second one is the classification of automatically segmented ROIs as either malignant or benign. We also provide an overview of datasets and evaluation metrics used in the classification task. Finally, we compare and discuss the deep learning approach to classical image processing and learning approach in this domain.