Reviewerly/exHarmony huggingface dataset:
Quantum walks do not like bridges

We consider graphs with two cut vertices joined by a path with one or two edges, and prove that there can be no quantum perfect state transfer between these vertices, unless the graph has no other vertex. We achieve this result by applying the 1-sum lemma for the characteristic polynomial of graphs, the neutrino identities that relate entries of eigenprojectors and eigenvalues, and variational principles for eigenvalues (Cauchy interlacing theorem, Weyl inequalities and Wielandt minimax principle). We see our result as an intermediate step to broaden the understanding of how connectivity plays a key role in quantum walks, and as further evidence of the conjecture that no tree on four or more vertices admits state transfer. We conclude with some open problems.
Flexible infrared images destriping algorithm with L1-based sparse regularization for wide-field astronomical images

A novel infrared image destriping method is proposed from a single image. The proposed method can estimate the clean image and stripe noise image simultaneously. Firstly, we analyze the common challenges in current destriping algorithms, and then integrate an unidirectional total variation constraint to remove stripe noise effectively. Wavelet transform is introduced to constrain the spatial mixture Gaussian noise in the degraded images. We employ the total variation regularization to smooth the distribution of Wavelet coefficients. Namely, the distribution of Wavelet coefficients of clean image is sparser than that of degraded stripe image. Finally, an efficient optimization strategy is described to alternately estimate the clean image and stripe image until convergence. Compared experiments are carried out on the simulated and real infrared images with the state-of-the-art models. The proposed method is able to recover high resolution infrared image in low computation cost.
Stochastic First-Order Methods Over Distributed Data

In this paper, we study the problem of learning from data available over a network of geographically distributed nodes. Each node possess a private local cost function and the goal is to minimize the global cost defined as the average of all local costs. Assuming that the cost functions are smooth and strongly-convex, and that the information exchange among the nodes can be asymmetric, we propose two first-order stochastic optimization methods PushSVRG and AB-SVRG converging to the global minimum. Both methods use net-work level gradient tracking to eliminate the dissimilarity among heterogeneous data distribution and node level vari-ance reduction to mitigate the variance caused by imper-fect (local) gradient information. To eliminate the asym-metry of information exchange caused by the communication, PushSVRG uses column-stochastic weights and push-sum consensus while AB-SVRG uses both row and column stochastic weights and does not require the extra push-sum iterations. We compare the proposed methods with related work on first-order stochastic optimization using extensive numerical experiments and highlight the practical aspects of different variance reduction techniques.
Lightweight Fingerprint Attack and Encrypted Traffic Analysis on News Articles

The news articles we read online can reveal a lot about us. Privacy aware groups have therefore long pushed for the use of HTTPS (encrypted end-to-end communication). In this paper, we present the design and evaluation of a lightweight framework that can (1) successfully identify individual news articles even when the articles are delivered over encrypted connections, and (2) separate between articles associated with different news websites even when the websites are delivered over the same infrastructure. Our results demonstrate that naive use of HTTPS is not enough to prevent attackers monitoring a user's connections from identifying articles that the user reads on the most popular news website. We also provide insights into what makes some websites more/less resilient to our attack, and we use Twitter data to evaluate the effectiveness of an example attack that in addition incorporates the popularity of individual news articles. We are the first to demonstrate and evaluate the practical effectiveness of this type of attack when applied on modern news websites, and our multi-website-based evaluation provides valuable insights into how websites can best protect themselves against this type of attacks. These insights are important for websites that want to help protect the privacy of their users.
gPHI: Lightweight Anonymity Protocol for Anonymity at Host and AS Levels

Lightweight anonymity protocols are expected to provide anonymity service at the networking layer. Among them, PHI and its successor dPHI are promising due to the fact that anonymous path selection according to IP routing enables easy deployment at the Internet infrastructure. However, the path selection based on IP routing incurs vulnerabilities against attacks which leverage underlying Internet topology information. This paper discovers such new attacks against PHI/dPHI and designs gPHI to mitigate such attacks by extending dPHI.
Homomorphic encryption enables private data sharing for digital health: winning entry to the Vinnova innovation competition Vinter 2021–22

People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021–22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE) - a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.
A 40-nm 646.6TOPS/W Sparsity-Scaling DNN Processor for On-Device Training

This work presents the first deep-neural-network (DNN) processor that supports sparsity-scaling training (SST). SST enables a sparsity of 92.4-to-97.8% with a <1.84% accuracy loss on commonly used neural networks (including ResNet and VGG). Compact 8-bit block floating-point (BFP8) is employed and external memory access (EMA) is minimized by bidirectional data compression. The chip delivers the maximum energy efficiency of 646.6TOPS/W, achieving 3.7× and 4.9× improvements in energy and area efficiencies, respectively, when compared to the state-of-the-art designs.
The Impact of Covid-19 Pandemic on Undergraduate Students: the Role of an Adaptive Situation-Aware Learning System

In the context of the emergency of COVID-19, students have experienced moments of strong emotional stress, with the risk of generating a state of frustration and discouraging them from studying. The proposed adaptive e-learning system, based on situational awareness, and remodeled teaching were essential in limiting this phenomenon. The system has been designed and developed according to the design principles of Situation Awareness. The feedback selection process, updated through the years to face the emergency situation, is driven by a Fuzzy Cognitive Map, implemented to identify the learners' situation, defined through their levels of engagement, motivation, and participation. The experimentation was conducted using the SAGAT methodology, involving students participating in classes during the courses held over the academic years 2018/2019, 2019/2020, and 2020/2021. The results show that the system is capable of increasing the level of situation awareness of the students even in a context of emergency.
Navigating Diverse Salient Features for Vehicle Re-Identification

Mining sufficient discriminative information is vital for effective feature representation in vehicle re-identification. Traditional methods mainly focus on the most salient features and neglect whether the explored information is sufficient. This paper tackles the above limitation by proposing a novel Salience-Navigated Vehicle Re-identification Network (SVRN) which explores diverse salient features at multi-scales. For mining sufficient salient features, we design SVRN from two aspects: 1) network architecture: we propose a novel salience-navigated vehicle re-identification network, which mines diverse features under a cascaded suppress-and-explore mode. 2) feature space: cross-space constraint enables the diversity from feature space, which restrains the cross-space features by vehicle and image identifications (IDs). Extensive experiments demonstrate our method’s effectiveness, and the overall results surpass all previous state-of-the-arts in three widely-used Vehicle ReID benchmarks (VeRi-776, VehicleID, and VERI-WILD), i.e., we achieve an 84.5% mAP on VeRi-776 benchmark that outperforms the second-best method by a large margin (3.5% mAP).
A Cascade Model with Prior Knowledge for Bone Age Assessment

Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and continuity of hand bone development simultaneously. In addition, most existing BAA models are based on datasets from European and American children and may not be applicable to the developmental characteristics of Chinese children. Thus, this work proposes a cascade model that fuses prior knowledge. Specifically, a novel bone age representation is defined, which incorporates nonlinear and continuous features of skeletal development and is implemented by a cascade model. Moreover, corresponding regions of interest (RoIs) based on RUS-CHN were extracted by YOLO v5 as prior knowledge inputs to the model. In addition, based on MobileNet v2, an improved feature extractor was proposed by introducing the Convolutional Block Attention Module and increasing the receptive field to improve the accuracy of the evaluation. The experimental results show that the mean absolute error (MAE) is 4.44 months and significant correlations with the reference bone age is (r = 0.994, p &lt; 0.01); accuracy is 94.04% for ground truth within ±1 year. Overall, the model design adequately considers hand bone development features and has high accuracy and consistency, and it also has some applicability on public datasets, showing potential for practical and clinical applications.
Variational quantum algorithm for Gaussian discrete solitons and their boson sampling

In the context of quantum information, highly nonlinear regimes, such as those supporting solitons, are marginally investigated. We miss general methods for quantum solitons, although they can act as entanglement generators or as self-organized quantum processors. We develop a computational approach that uses a neural network as a variational ansatz for quantum solitons in an array of waveguides. By training the resulting phase-space quantum machine learning model, we find different soliton solutions varying the number of particles and interaction strength. We consider Gaussian states that enable measuring the degree of entanglement and sampling the probability distribution of many-particle events. We also determine the probability of generating particle pairs and unveil that soliton bound states emit correlated pairs. These results may have a role in boson sampling with nonlinear systems and in quantum processors for entangled nonlinear waves.
Social Big Data Mining

Social media is used to share the data or information among the large group of people. Numerous forums, blogs, social networks, news reports, e-commerce websites, and many more online media play a role in sharing individual opinions. The data generated from these sources is huge and in unstructured format. Big data is a term used for data sets that are large or complex and that cannot be processed by traditional processing system. Sentimental analysis is one of the major data analytics applied on big data. It is a task of natural language processing to determine whether a text contains subjective information and what information it expresses. It helps in achieving various goals like the measurement of customer satisfaction, observing public mood on political movement, movie sales prediction, market intelligence, and many more. In this chapter, the authors present various techniques used for sentimental analysis and related work using these techniques. The chapter also presents open issues and challenges in sentimental analysis landscape.
Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets

Selecting the optimal set of features to determine sentiment in online textual content is imperative for superior classification results. Optimal feature selection is computationally hard task and fosters the need for devising novel techniques to improve the classifier performance. In this work, the binary adaptation of cuckoo search (nature inspired, meta-heuristic algorithm) known as the Binary Cuckoo Search is proposed for the optimum feature selection for a sentiment analysis of textual online content. The baseline supervised learning techniques such as SVM, etc., have been firstly implemented with the traditional tf-idf model and then with the novel feature optimization model. Benchmark Kaggle dataset, which includes a collection of tweets is considered to report the results. The results are assessed on the basis of performance accuracy. Empirical analysis validates that the proposed implementation of a binary cuckoo search for feature selection optimization in a sentiment analysis task outperforms the elementary supervised algorithms based on the conventional tf-idf score.
Sentiment Analysis on Social Media

Due to the advent of Web 2.0, the size of social media content (SMC) is growing rapidly and likely to increase faster in the near future. Social media applications such as Instagram, Twitter, Facebook, etc. have become an integral part of our lives, as they prompt the people to give their opinions and share information around the world. Identifying emotions in SMC is important for many aspects of sentiment analysis (SA) and is a top-level agenda of many firms today. SA on social media (SASM) extends an organization's ability to capture and study public sentiments toward social events and activities in real time. This chapter studies recent advances in machine learning (ML) used for SMC analysis and its applications. The framework of SASM consists of several phases, such as data collection, pre-processing, feature representation, model building, and evaluation. This survey presents the basic elements of SASM and its utility. Furthermore, the study reports that ML has a significant contribution to SMC mining. Finally, the research highlights certain issues related to ML used for SMC.
Quantum error correction fundamentals

The concept of this chapter is to gradually introduce the reader to quantum error correction coding principles, starting from intuitive descriptions and advancing to rigorous mathematical discussions. The chapter starts with Pauli operators, basic definitions, and the representation of quantum errors. Although Pauli operators were introduced in Chapter 3, they are discussed here in the context of quantum errors and quantum error correction. Next, basic quantum codes, such as three-qubit flip, three-qubit phase flip, and Shor's nine-qubit codes are presented. Projection measurements are used to determine error syndrome and perform corresponding error correction action. Stabilizer formalism and stabilizer groups are also introduced, and basic stabilizer codes are described. The entire next chapter is devoted to stabilizer codes; only the basic concepts are introduced here. An important class of codes, Calderbank–Shor–Steane (CSS) codes, is described next. The connection between classical and quantum codes is established, and two classes of CSS codes are described—dual-containing and quantum codes derived from classical codes over GF(4). The quantum error correction concept is formally introduced, followed by the necessary and sufficient conditions for quantum code to correct a given set of errors. Then, the minimum distance for a quantum code is defined and used to relate it to the error correction capability of a quantum code. CSS codes are then revisited under this mathematical framework. The section after that is devoted to important quantum coding bounds: Hamming quantum, quantum Gilbert–Varshamov, and quantum Singleton (also known as Knill–Laflamme). Next, the concept of operator-sum representation is introduced and used to provide a physical interpretation and describe measurement of the environment. Finally, several important quantum channel models are introduced, such as depolarizing, amplitude damping, and generalized amplitude damping.
A Video Key Frame Extraction Method Based on Multiview Fusion

A massive amount of video data is stored in the real-time road monitoring system, especially in high-speed scenes. Traditional methods of video key frame extraction have the problems of large computation and long-time consumption. Thus, it is imperative to decrease the massive video data generated by monitoring and help researchers to study key frames. Aiming at the above problems, we propose an efficient key frame extraction method based on multiview fusion, where the autoencoder is used to compress the video data. Specifically, all the video frames of the video data are subjected to feature dimensionality reduction, and the features after dimensionality reduction are subjected to multiview fusion. Finally, dynamic programming and clustering are used to extract key frames. The experimental results show that the proposed method has lower computational complexity in extracting key frames, while the mutual information in the extracted key frames is large. It illustrates the reliability and efficiency of the proposed method, which provides technical support for subsequent video research.
Rewriting geometric rules of a GAN

Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However, the current machine learning approaches miss a key element of the creative process - the ability to synthesize things that go far beyond the data distribution and everyday experience. To begin to address this issue, we enable a user to "warp" a given model by editing just a handful of original model outputs with desired geometric changes. Our method applies a low-rank update to a single model layer to reconstruct edited examples. Furthermore, to combat overfitting, we propose a latent space augmentation method based on style-mixing. Our method allows a user to create a model that synthesizes endless objects with defined geometric changes, enabling the creation of a new generative model without the burden of curating a large-scale dataset. We also demonstrate that edited models can be composed to achieve aggregated effects, and we present an interactive interface to enable users to create new models through composition. Empirical measurements on multiple test cases suggest the advantage of our method against recent GAN fine-tuning methods. Finally, we showcase several applications using the edited models, including latent space interpolation and image editing.
Successive learned image compression: Comprehensive analysis of instability

Recently, learned image compression relying on deep learning has shown remarkable improvement compared to conventional image compression. While the existing studies on learned image compression focus only on single-step compression, this paper addresses the problem of successive image compression (SIC), which refers to repeated application of encoding and decoding an image. It can be commonly involved in the processes of image sharing, editing, and re-distribution, but the performance of learned image compression during SIC has not been studied in literature. Thus, we provide comprehensive analysis of successive learned image compression in three aspects. First, we newly introduce the issue of instability of learned compression methods during SIC, and analyze its causes and affecting factors. Second, we provide SIC benchmarking studies for the state-of-the-art learned compression methods, based on which we compare different methods and investigate the components affecting the instability in detail. Finally, we propose two methods to mitigate the issue of instability of learned methods during SIC.
ASSET

We present ASSET, a neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map. Our architecture is based on a transformer with a novel attention mechanism. Our key idea is to sparsify the transformer's attention matrix at high resolutions, guided by dense attention extracted at lower image resolutions. While previous attention mechanisms are computationally too expensive for handling high-resolution images or are overly constrained within specific image regions hampering long-range interactions, our novel attention mechanism is both computationally efficient and effective. Our sparsified attention mechanism is able to capture long-range interactions and context, leading to synthesizing interesting phenomena in scenes, such as reflections of landscapes onto water or fora consistent with the rest of the landscape, that were not possible to generate reliably with previous convnets and transformer approaches. We present qualitative and quantitative results, along with user studies, demonstrating the effectiveness of our method. Our code and dataset are available at our project page: https://github.com/DifanLiu/ASSET