Reviewerly/exHarmony huggingface dataset:
All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly; 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to understand an aerial scene. In this paper, we propose a novel all grains, one scheme (AGOS) framework to tackle these challenges. To the best of our knowledge, it is the first work to extend the classic multiple instance learning into multi-grain formulation. Specially, it consists of a multi-grain perception module (MGP), a multi-branch multi-instance representation module (MBMIR) and a self-aligned semantic fusion (SSF) module. Firstly, our MGP preserves the differential dilated convolutional features from the backbone, which magnifies the discriminative information from multi-grains. Then, our MBMIR highlights the key instances in the multi-grain representation under the MIL formulation. Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole. Notably, our AGOS is flexible and can be easily adapted to existing CNNs in a plug-and-play manner. Extensive experiments on UCM, AID and NWPU benchmarks demonstrate that our AGOS achieves a comparable performance against the state-of-the-art methods.
Siamese-SR: A Siamese Super-Resolution Model for Boosting Resolution of Digital Rock Images for Improved Petrophysical Property Estimation

Digital Rock Physics leverages advances in digital image acquisition and analysis techniques to create 3D digital images of rock samples, which are used for computational modeling and simulations to predict petrophysical properties of interest. However, the accuracy of the predictions is crucially dependent on the quality of the digital images, which is currently limited by the resolution of the micro-CT scanning technology. We have proposed a novel Deep Learning based Super-Resolution model called Siamese-SR to digitally boost the resolution of Digital Rock images whilst retaining the texture and providing optimal de-noising. The Siamese-SR model consists of a generator which is adversarially trained with a relativistic and a siamese discriminator utilizing Materials In Context (MINC) loss estimator. This model has been demonstrated to improve the resolution of sandstone rock images acquired using micro-CT scanning by a factor of 2. Another key highlight of our work is that for the evaluation of the super-resolution performance, we propose to move away from image-based metrics such as Structural Similarity (SSIM) and Peak Signal to Noise Ratio (PSNR) because they do not correlate well with expert geological and petrophysical evaluations. Instead, we propose to subject the super-resolved images to the next step in the Digital Rock workflow to calculate a crucial petrophysical property of interest, viz. porosity and use it as a metric for evaluation of our proposed Siamese-SR model against several other existing super-resolution methods like SRGAN, ESRGAN, EDSR and SPSR. Furthermore, we also use Local Attribution Maps to show how our proposed Siamese-SR model focuses optimally on edge-semantics, which is what leads to improvement in the image-based porosity prediction, the permeability prediction from Multiple Relaxation Time Lattice Boltzmann Method (MRTLBM) flow simulations as well as the prediction of other petrophysical properties of interest derived from Mercury Injection Capillary Pressure (MICP) simulations.
Metaverse - Death to Offline Communication or Empowerment of Interaction?

The article is dedicated to the phenomenon of metaverse which is the next step in the modern virtual environment. The topic of creating metaverses is discussed a lot nowadays due to the fact that several world-wide corporations expressed their interest in developing the mentioned spaces. The elaboration of metaverses will provide people access to online interaction which practically does not differ from the real-life experience. This brings up a problem of metaverses’ development effect on the process of communication and its effectiveness. Besides, it is very important to mention the main perspectives of integrating and applying the metaverse in the communication sphere. Metaverses could become a factor that will cause an adverse effect on face-to-face communication replacing it with online experience. Besides, they could catalyze the process of integration between online and offline dimensions and their further co-development starting a new era in communications. It is being reviewed and analyzed by authors.
Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on SSAE-KELM

Anaerobic digestion technology is the most environmentally friendly approach to treat kitchen waste. Volatile fatty acid (VFA) is an essential quality monitoring indicator in the anaerobic digestion process of treating kitchen waste. In this paper, a soft measurement method of volatile fatty acid (VFA) concentration in the anaerobic digestion process is established based on stacked supervised auto-encoder combine kernel extreme learning machine algorithm (SSAE-KELM) to improve the real-time monitoring level and resource conversion efficiency of the anaerobic digestion process. Given the problems of poor feature extraction and low accuracy and efficiency of the model, a stack supervised autoencoder is proposed to realize nonlinear and deep feature extraction of process data. Simultaneously, using the idea of the extreme learning machine to train the network significantly improves the efficiency of the model. Then, the kernel extreme learning machine is used to realize regression modelling. Besides, a combined feature selection algorithm is presented to select auxiliary variables more accurately. The simulation results demonstrate that the soft sensor model can predict the concentration of volatile fatty acids (VFA) more efficiently and accurately.
The “Human Factor” in Cybersecurity

A great deal of research has been devoted to the exploration and categorization of threats posed from malicious attacks from current employees who are disgruntled with the organisation, or are motivated by financial gain. These so-called “insider threats” pose a growing menace to information security, but given the right mechanisms, they have the potential to be detected and caught. In contrast, human factors related to aspects of poor planning, lack of attention to detail, and ignorance are linked to the rise of the accidental or unintentional insider. In this instance there is no malicious intent and no prior planning for their “attack,” but their actions can be equally as damaging and disruptive to the organisation. This chapter presents an exploration of fundamental human factors that could contribute to an individual becoming an unintentional threat. Furthermore, key frameworks for designing mitigations for such threats are also presented, alongside suggestions for future research in this area.
Multilayer Perceptrons

This article briefly introduces Artificial Neural Networks for beginners. In its first part, it describes the functioning of a single artificial neuron for simple binary classification tasks. The method is later generalized to single-layer networks, whose functioning is shown on simple multi-class classification tasks. Than, the fact that single-layer Neural Networks may fail to correctly classify non-linearly separable problems is shown by means of a simple example. Finally, multi-layer Neural Networks are presented, as more general methods, able to obtain satisfactory results potentially on any kind of classification problem, including non-linearly separable ones.
Blockchain Architecture

Recently, blockchain technology has received much attention. This is because of the rise of cryptocurrencies such as Bitcoin and Eternium. The cryptocurrencies manage the transactions of the users as a set of blocks in a ledger using cryptography techniques. In these systems, peer-to-peer networks are used to manage communications among users. These networks are also used to update the ledger in a fully distributed fashion. In other words, the technology of peer-to-peer networks plays a key role in the architecture of the blockchain. Therefore, the first issue in designing blockchain architecture is to determine the required technologies and applications of peer-to-peer networks. The second issue is to organize a well-defined application development process for blockchain architecture. The last issue is to identify the key participants in the blockchain environments. In this section, we discuss the mentioned issues.
Neural Networks and Deep Learning

This chapter deals with neural networks, starting from the early days of the perceptron and perceptron rule, and then moves on to the multilayer feed-forward neural networks and their training via the backpropagation algorithmic concept. A number of algorithms are discussed, starting from the basic gradient descent scheme up to very recently proposed popular variants. The palette of possible nonlinearities, including the ReLU, is presented and the effects of the choice of the nonlinearity on the convergence of the training algorithm are discussed, also in relation to the cost function that is selected. Regularization techniques, including the dropout method, are presented. In the sequel, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are introduced in some depth, alongside some very popular deep networks that are currently in use. In the sequel, generative networks are presented, starting from the more classical models based on Boltzmann machines and deep belief networks and moving on to the more recent advances including variational autoencoders and generative adversarial networks (GANs). Finally, capsule networks are introduced and discussed. A case study from the field of natural language processing (NLP) completes the chapter.
Quantum regression beyond the Born-Markov approximation for generalized spin-boson models

The quantum regression formula for an open quantum system consists in an infinite hierarchy of conditions for its multitime correlation functions, thus requiring full access to the total ``$\mathrm{system}+\mathrm{environment}$'' evolution, and providing a stronger requirement than completely positive (CP) divisibility. Here, we analyze CP divisibility and check the validity of quantum regression beyond the Born-Markov approximation (e.g., the weak-coupling limit) for a class of generalized spin-boson models giving rise to a multilevel amplitude-damping evolution; in all cases, it is possible to engineer the system-bath coupling in such a way that quantum regression is exactly satisfied.
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data

Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications. Hence, sequential recommender systems have been developed to learn the dynamic user interests from the historical interactions for suggesting items. However, the interaction pattern encoding functions in most existing sequential recommender systems have focused on single type of user-item interactions. In many real-life online platforms, user-item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type behavior inter-dependencies. Learning from informative representations of users and items based on their multi-typed interaction data, is of great importance to accurately characterize the time-evolving user preference. In this work, we tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns. Towards this end, we propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving correlations across different types of behaviors. The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies. Experiments on the real-world datasets indicate that our method TGT consistently outperforms various state-of-the-art recommendation methods. Our model implementation codes are available at https://github.com/akaxlh/TGT.
Consensus clustering for Bayesian mixture models

Abstract Background Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from multiple runs of a non-deterministic clustering algorithm. Here we consider the application of consensus clustering to a broad class of heuristic clustering algorithms that can be derived from Bayesian mixture models (and extensions thereof) by adopting an early stopping criterion when performing sampling-based inference for these models. While the resulting approach is non-Bayesian, it inherits the usual benefits of consensus clustering, particularly in terms of computational scalability and providing assessments of clustering stability/robustness. Results In simulation studies, we show that our approach can successfully uncover the target clustering structure, while also exploring different plausible clusterings of the data. We show that, when a parallel computation environment is available, our approach offers significant reductions in runtime compared to performing sampling-based Bayesian inference for the underlying model, while retaining many of the practical benefits of the Bayesian approach, such as exploring different numbers of clusters. We propose a heuristic to decide upon ensemble size and the early stopping criterion, and then apply consensus clustering to a clustering algorithm derived from a Bayesian integrative clustering method. We use the resulting approach to perform an integrative analysis of three ’omics datasets for budding yeast and find clusters of co-expressed genes with shared regulatory proteins. We validate these clusters using data external to the analysis. Conclustions Our approach can be used as a wrapper for essentially any existing sampling-based Bayesian clustering implementation, and enables meaningful clustering analyses to be performed using such implementations, even when computational Bayesian inference is not feasible, e.g. due to poor exploration of the target density (often as a result of increasing numbers of features) or a limited computational budget that does not along sufficient samples to drawn from a single chain. This enables researchers to straightforwardly extend the applicability of existing software to much larger datasets, including implementations of sophisticated models such as those that jointly model multiple datasets.
Text-Free Prosody-Aware Generative Spoken Language Modeling

Speech pre-training has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT-2 can generate coherent paragraphs, has barely been explored.Generative Spoken Language Modeling (GSLM) (Lakhotia et al., 2021) is the only prior work addressing the generative aspects of speech pretraining, which replaces text with discovered phone-like units for language modeling and shows the ability to generate meaningful novel sentences.Unfortunately, despite eliminating the need of text, the units used in GSLM discard most of the prosodic information.Hence, GSLM fails to leverage prosody for better comprehension, and does not generate expressive speech.In this work, we present a prosody-aware generative spoken language model (pGSLM).It is composed of a multi-stream transformer language model (MS-TLM) of speech, represented as discovered unit and prosodic feature streams, and an adapted HiFi-GAN model converting MS-TLM outputs to waveforms.We devise a series of metrics for prosody modeling and generation, and re-use metrics from GSLM for content modeling.Experimental results show that the pGSLM can utilize prosody to improve both prosody and content modeling, and also generate natural, meaningful, and coherent speech given a spoken prompt. 1
The effective noise of stochastic gradient descent

Stochastic Gradient Descent (SGD) is the workhorse algorithm of deep learning technology. At each step of the training phase, a mini batch of samples is drawn from the training dataset and the weights of the neural network are adjusted according to the performance on this specific subset of examples. The mini-batch sampling procedure introduces a stochastic dynamics to the gradient descent, with a non-trivial state-dependent noise. We characterize the stochasticity of SGD and a recently-introduced variant, \emph{persistent} SGD, in a prototypical neural network model. In the under-parametrized regime, where the final training error is positive, the SGD dynamics reaches a stationary state and we define an effective temperature from the fluctuation-dissipation theorem, computed from dynamical mean-field theory. We use the effective temperature to quantify the magnitude of the SGD noise as a function of the problem parameters. In the over-parametrized regime, where the training error vanishes, we measure the noise magnitude of SGD by computing the average distance between two replicas of the system with the same initialization and two different realizations of SGD noise. We find that the two noise measures behave similarly as a function of the problem parameters. Moreover, we observe that noisier algorithms lead to wider decision boundaries of the corresponding constraint satisfaction problem.
Diffractive interconnects: all-optical permutation operation using diffractive networks

Abstract Permutation matrices form an important computational building block frequently used in various fields including, e.g., communications, information security, and data processing. Optical implementation of permutation operators with relatively large number of input–output interconnections based on power-efficient, fast, and compact platforms is highly desirable. Here, we present diffractive optical networks engineered through deep learning to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using passive transmissive layers that are individually structured at the wavelength scale. Our findings indicate that the capacity of the diffractive optical network in approximating a given permutation operation increases proportional to the number of diffractive layers and trainable transmission elements in the system. Such deeper diffractive network designs can pose practical challenges in terms of physical alignment and output diffraction efficiency of the system. We addressed these challenges by designing misalignment tolerant diffractive designs that can all-optically perform arbitrarily selected permutation operations, and experimentally demonstrated, for the first time, a diffractive permutation network that operates at THz part of the spectrum. Diffractive permutation networks might find various applications in, e.g., security, image encryption, and data processing, along with telecommunications; especially with the carrier frequencies in wireless communications approaching THz-bands, the presented diffractive permutation networks can potentially serve as channel routing and interconnection panels in wireless networks.
Entanglement Length Scale Separates Threading from Branching of Unknotted and Non-concatenated Ring Polymers in Melts

Current theories on the conformation and dynamics of unknotted and non-concatenated ring polymers in melt conditions describe each ring as a tree-like double-folded object. While evidence from simulations supports this picture on a single ring level, other works show pairs of rings also thread each other - a feature overlooked in the tree theories. Here we reconcile this dichotomy using Monte-Carlo simulations of the ring melts with different bending rigidity. We find that rings are double-folded (more strongly for stiffer rings) on and above the entanglement length scale, while the threadings are localized on smaller scales. The different theories disagree on the details of the tree structure, i.e the fractal dimension of the backbone of the tree. In the stiffer melts we find an indication of a self-avoiding scaling of the backbone, while more flexible chains do not exhibit such a regime. Moreover, the theories commonly neglect threadings, and assign different importance to the impact of the progressive constraint release (tube dilation) on single ring relaxation due to the motion of other rings. Despite each threading creates only a small opening in the double-folded structure, the threading loops can be numerous and their length can exceed substantially the entanglement scale. We link the threading constraints to the divergence of the relaxation time of a ring, if the tube dilation is hindered by pinning a fraction of other rings in space. Current theories do not predict such divergence and predict faster than measured diffusion of rings, pointing at the relevance of the threading constraints in unpinned systems as well. Revision of the theories with explicit threading constraints might elucidate the validity of the conjectured existence of topological glass.