Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive Approach for the K-means Problem on Streaming Data

Currently the amount of data produced worldwide is increasing beyond measure, thus a high volume of unsupervised data must be processed continuously. One of the main unsupervised data analysis is clustering. In streaming data scenarios, the data is composed by an increasing sequence of batches of samples where the concept drift phenomenon may happen. In this paper, we formally define the Streaming $K$-means(S$K$M) problem, which implies a restart of the error function when a concept drift occurs. We propose a surrogate error function that does not rely on concept drift detection. We proof that the surrogate is a good approximation of the S$K$M error. Hence, we suggest an algorithm which minimizes this alternative error each time a new batch arrives. We present some initialization techniques for streaming data scenarios as well. Besides providing theoretical results, experiments demonstrate an improvement of the converged error for the non-trivial initialization methods.
Exploiting Implicit Kinematic Kernel for Controlling a Wearable Robotic Extra-finger

In the last decades, wearable robots have been proposed as technological aids for rehabilitation, assistance, and functional substitution for patients suffering from motor disorders and impairments. Robotic extra-limbs and extra-fingers are representative examples of the technological and scientific achievements in this field. However, successful and intuitive strategies to control and cooperate with the aforementioned wearable aids are still not well established. Against this background, this work introduces an innovative control strategy based on the exploitation of the residual motor capacity of impaired limbs. We aim at helping subjects with a limited mobility and/or physical impairments to control wearable extra-fingers in a natural, comfortable, and intuitive way. The novel idea here presented lies on taking advantage of the redundancy of the human kinematic chain involved in a task to control an extra degree of actuation (DoA). This concept is summarized in the definition of the Implicit Kinematic Kernel (IKK). As first investigation, we developed a procedure for the real time analysis of the body posture and the consequent computation of the IKK-based control signal in the case of single-arm tasks. We considered both bio-mechanical and physiological human features and constraints to allow for an efficient and intuitive control approach. Towards a complete evaluation of the proposed control system, we studied the users' capability of exploiting the Implicit Kinematic Kernel both in virtual and real environments, asking subjects to track different reference signals and to control a robotic extra-finger to accomplish pick-and-place tasks. Obtained results demonstrated that the proposed approach is suitable for controlling a wearable robotic extra-finger in a user-friendly way.
Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks

We propose the Poisson neural networks (PNNs) to learn Poisson systems and trajectories of autonomous systems from data. Based on the Darboux–Lie theorem, the phase flow of a Poisson system can be written as the composition of: 1) a coordinate transformation; 2) an extended symplectic map; and 3) the inverse of the transformation. In this work, we extend this result to the unknotted trajectories of autonomous systems. We employ structured neural networks with physical priors to approximate the three aforementioned maps. We demonstrate through several simulations that PNNs are capable of handling very accurately several challenging tasks, including the motion of a particle in the electromagnetic potential, the nonlinear Schrödinger equation, and pixel observations of the two-body problem.
On-Device Tag Generation for Unstructured Text

With the overwhelming transition to smart phones, storing important information in the form of unstructured text has become habitual to users of mobile devices. From grocery lists to drafts of emails and important speeches, users store a lot of data in the form of unstructured text (for eg: in the Notes application) on their devices, leading to cluttering of data. This not only prevents users from efficient navigation in the applications but also precludes them from perceiving the relations that could be present across data in those applications. This paper proposes a novel pipeline to generate a set of tags using world knowledge based on the keywords and concepts present in unstructured textual data. These tags can then be used to summarize, categorize or search for the desired information thus enhancing user experience by allowing them to have a holistic outlook of the kind of information stored in the form of unstructured text. In the proposed system, we use an on-device (mobile phone) efficient CNN model with pruned ConceptNet resource to achieve our goal. The architecture also presents a novel ranking algorithm to extract the top n tags from any given text.
The effect of nanosilica filler on the foaming process and properties of flexible polyurethane foams obtained with rapeseed oil-based polyol

In this paper, the analysis of the foaming process of flexible polyurethane modified with the addition of silica nanoparticles is presented. Flexible polyurethane foams (FPURF) were obtained using petrochemical components and a rapeseed-oil-based polyol (used in an amount of 20 wt %). Nanosilica was added to the polyurethane system in the amount of 0.5, 1.0 and 1.5 php (parts per hundred polyols). The characteristic parameters of the foaming process, such as the growth velocity of foamed materials, the core temperature and dielectric polarization, were measured using a Foamat device. It was observed that polyurethane-forming reactions slowed down as an effect of the increase of nanosilica content in the polyurethane composition. Consequently, the temperature in the core of the reaction mixture containing 1.5 php nanosilica was lower by approx. 35 °C compared to the reference material. Moreover, the influence of the method of homogenization of the nanofiller with polyols on the selected properties of prepared foams was analyzed. The following properties of flexible polyurethane foams were determined: apparent density, resilience, compressive strength, hardness, hysteresis and support factor. The introduction of nanosilica filler to the polyurethane formulation caused an increase in the apparent density from 24.6 kg/m3 for the reference foam to 28.5 kg/m3 for the foam containing 1.5 php of nanosilica. However, this nanofiller did not significantly affect the cell structure of foamed materials. The foams obtained with the nanosilica additive of 1.0 php had the most preferred properties, such as a slightly higher value of resilience, lower hardness and higher support factor than the reference foam without nanosilica.
GpuShareSat: a SAT solver using the GPU for clause sharing

We describe a SAT solver using both the GPU (CUDA) and the CPU with a new clause exchange strategy. The CPU runs a classic multithreaded CDCL SAT solver. EachCPU thread exports all the clauses it learns to the GPU. The GPU makes a heavy usage of bitwise operations. It notices when a clause would have been used by a CPU thread and notifies that thread, in which case it imports that clause. This relies on the GPU repeatedly testing millions of clauses against hundreds of assignments. All the clauses are tested independantly from each other (which allows the GPU massively parallel approach), but against all the assignments at once, using bitwise operations. This allows CPU threads to only import clauses which would have been useful for them. Our solver is based upon glucose-syrup. Experiments show that this leads to a strong performance improvement, with 22 more instances solved on the SAT 2020 competition than glucose-syrup.
Salt effect on phase equilibria by a recirculating still

A new still to measure the salt effect on the vapor-liquid equilibrium at low pressures was developed. It is of the recirculating type, allowing the recirculation of both phases and the determination of its composition. The still was tested with the system ethanol-water-potassium acetate at different mole fractions of salt at 101.33 kPa
ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios.
Quadratic Dynamics Over Hyperbolic Numbers

Hyperbolic numbers are a variation of complex numbers, but their dynamics is quite different. The hyperbolic Mandelbrot set for quadratic functions over hyperbolic numbers is simply a filled square, and the filled Julia set for hyperbolic parameters inside the hyperbolic Mandelbrot set is a filled rectangle. For hyperbolic parameters outside the hyperbolic Mandelbrot set, the filled Julia set has 3 possible topological descriptions, if it is not empty, in contrast to the complex case where it is always a non-empty totally disconnected set. These results were proved in [1,2,4,5,6,7] and are reviewed here
Self-supervised Deep Learning for Reading Activity Classification

Reading analysis can give important information about a user's confidence and habits and can be used to construct feedback to improve a user's reading behavior. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. In this paper, we propose a self-supervised DL method for reading analysis and evaluate it on two classification tasks. We first evaluate the proposed self-supervised DL method on a four-class classification task on reading detection using electrooculography (EOG) glasses datasets, followed by an evaluation of a two-class classification task of confidence estimation on answers of multiple-choice questions (MCQs) using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used to compare the performance of the proposed self-supervised DL method. The results show that the proposed self-supervised DL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates that the proposed self-supervised DL method is the superior choice for reading analysis tasks. The results of this study are important for informing the design and implementation of automatic reading analysis platforms.
Encoding the Latent Posterior of Bayesian Neural Networks for Uncertainty Quantification

Bayesian Neural Networks (BNNs) have long been considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior distribution of the network parameters, most BNN approaches are either limited to small networks or rely on constraining assumptions, e.g., parameter independence. These drawbacks have enabled prominence of simple, but computationally heavy approaches such as Deep Ensembles, whose training and testing costs increase linearly with the number of networks. In this work we aim for efficient deep BNNs amenable to complex computer vision architectures, e.g., ResNet-50 DeepLabv3+, and tasks, e.g., semantic segmentation and image classification, with fewer assumptions on the parameters. We achieve this by leveraging variational autoencoders (VAEs) to learn the interaction and the latent distribution of the parameters at each network layer. Our approach, called Latent-Posterior BNN (LP-BNN), is compatible with the recent BatchEnsemble method, leading to highly efficient (in terms of computation and memory during both training and testing) ensembles. LP-BNNs attain competitive results across multiple metrics in several challenging benchmarks for image classification, semantic segmentation, and out-of-distribution detection.
ParaNet: Deep Regular Representation for 3D Point Clouds

Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular domains to the irregular 3D point cloud data. To bridge this gap, we propose ParaNet, a novel end-to-end deep learning framework, for representing 3D point clouds in a completely regular and nearly lossless manner. To be specific, ParaNet converts an irregular 3D point cloud into a regular 2D color image, named point geometry image (PGI), where each pixel encodes the spatial coordinates of a point. In contrast to conventional regular representation modalities based on multi-view projection and voxelization, the proposed representation is differentiable and reversible. Technically, ParaNet is composed of a surface embedding module, which parameterizes 3D surface points onto a unit square, and a grid resampling module, which resamples the embedded 2D manifold over regular dense grids. Note that ParaNet is unsupervised, i.e., the training simply relies on reference-free geometry constraints. The PGIs can be seamlessly coupled with a task network established upon standard and mature techniques for 2D images/videos to realize a specific task for 3D point clouds. We evaluate ParaNet over shape classification and point cloud upsampling, in which our solutions perform favorably against the existing state-of-the-art methods. We believe such a paradigm will open up many possibilities to advance the progress of deep learning-based point cloud processing and understanding.
Fixed Priority Global Scheduling from a Deep Learning Perspective

Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority global scheduling (FPGS) problems. We then briefly discuss possible generalizations of Deep Learning adoption for several realistic and complicated FPGS scenarios, e.g., scheduling tasks with dependency, mixed-criticality task scheduling. We believe that there are many opportunities for leveraging advanced Deep Learning technologies to improve the quality of scheduling in various system configurations and problem scenarios.
Recent advances in the preparation of semiconductors as isolated nanometric particles: new routes to quantum dots

Nanoparticles of semiconductor materials have been the subject of intense research in the last five years owing to the novel electronic, catalytical and optical properties observed in such materials. The unusual properties of these so called quantum dots can be attributed to two main factors: the large surface to volume ratio of atoms and the confinement of charge carriers in a ‘quantum mechanical box’. Small particles of semiconductors have been prepared by a number of routes, often using colloidal chemistry methods but more recently using organometallic routes. The recent advances have resulted in high quality nanoparticles which have been incorporated into simple devices. In this article we cover some of the key advances in the preparation of nanometric particles of semiconductors. The cover illustration depicts three sizes of TOPO capped CdSe.
Oxygen-sensitive optical fibre transducer

An optical fibre oxygen transducer was developed for the measurement of gaseous oxygen. The oxygen transducer is fabricated from a polymer optical fibre, an oxygen-sensitive reagent phase and a gas-permeable membrane. Oxygen is determined by the efficient fluorescence quenching of an immobilised fluorescent indicator. The transducer fabrication, its oxygen response and the performance characteristics of this oxygen transducer are discussed.
Fluorine chemistry at the millennium : fascinated by fluorine

Foreword. Preface. Following Fluorine in Nuclear Fuel Manufacture at BNFL (M. J. Atherton). Looking In on Fluorine Chemistry in Russia and Ukraine (D. L. Averre). Forty Years of Fluorine Chemistry (N. Bartlett). Contribution to the Perpetuation of Moissan's Memory: the Moissan Prize (R. Bougon). Organofluorine Chemistry in Novosibirsk, Siberia (G. M. Brooke). The Iowa Connection (D. J. Burton). Organofluorine Chemistry in the University of Durham, UK (R. D. Chambers). Never Say No to a Challenge (K. O. Christe). The Anionic Side of Fluorine Chemistry (J. H. Clark). Laporte and its Fluoride Businesses (A. E. Comyns). Fluorine Chemistry - A Chemical Gardener's Paradise (D. D. DesMarteau). Pursuing Fluorine Chemistry in Poland (W. Dmowski). Biographical Sketch of Paul Tarrant (W. R. Dolbier, Jr.). Fluoropolymers, Stable Nitroxides and Perfluoroalkylation (K. C. Eapen). Fluorine Chemistry in Italy (G. P. Gambaretto). Fluorine Chemistry at Leicester (J. H. Holloway and E. G. Hope). Chinese Research in Organofluorine Chemistry (C.-M. Hu and W.-Y. Huang). Fluorine Chemistry in Japan (Y. Kobayashi et al.). The Discovery of Successful Direct Fluorination Syntheses: Three Eras of Elemental Fluorine Reaction Chemistry (R. J. Lagow). Flogging the Fluorocarbons (D. M. Lemal). Adventures of a Fluorine Chemist at DuPont (W. J. Middleton). Fluorine Chemistry: The ICI Legacy (R. L. Powell). Fluorocarbon Emulsions - Designing an Efficient Shuttle Service for the Respiratory Gases - the so-called 'Blood Substitutes' (J. G. Riess). Some Aspects of Fluorine Chemistry in Gottingen (H. W. Roesky). Fluorocarbon Metal Compounds - Role Models in Organotransition Metal Chemistry (F. G. A. Stone). Aromatic Fluorine Chemistry at Salford (H. Suschitzky and B. J. Wakefield). Fluorine Chemistry at the University of Birmingham - A Cradle of the Subject in the UK (J. C. Tatlow). The Belated Hexafluorobenzene Papers of Yvonne Desirant (D. Tavernier). Highly-toxic Fluorine Compounds (C. M. Timperley). From Complex Fluorides to CFC Alternatives - An Account of Fluorine Chemistry at Glasgow (J. M. Winfield). The Development of Inorganic Fluorine Chemistry in Slovenia (B. Zemva). Going with the Fluo (R. E. Banks).
Self-supervised asymmetric deep hashing with margin-scalable constraint for image retrieval

Due to its effectivity and efficiency, deep hashing approaches are widely used for large-scale visual search. However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing methods are based upon an oversimplified similarity assignment(i.e., 0 for instance pairs sharing no label, 1 for instance pairs sharing at least 1 label), 2) the exploration in multi-semantic relevance are insufficient or even neglected in many of the existing methods. These problems significantly limit the discrimination of generated hash codes. In this paper, we propose a novel self-supervised asymmetric deep hashing method with a margin-scalable constraint(SADH) approach to cope with these problems. SADH implements a self-supervised network to sufficiently preserve semantic information in a semantic feature dictionary and a semantic code dictionary for the semantics of the given dataset, which efficiently and precisely guides a feature learning network to preserve multilabel semantic information using an asymmetric learning strategy. By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. Extensive empirical research on four popular benchmarks validates the proposed method and shows it outperforms several state-of-the-art approaches. The source codes URL of our SADH is: http://github.com/SWU-CS-MediaLab/SADH.
Topical Change Detection in Documents via Embeddings of Long Sequences

In a longer document, the topic often slightly shifts from one passage to the next, where topic boundaries are usually indicated by semantically coherent segments. Discovering this latent structure in a document improves the readability and is essential for passage retrieval and summarization tasks. We formulate the task of text segmentation as an independent supervised prediction task, making it suitable to train on Transformer-based language models. By fine-tuning on paragraphs of similar sections, we are able to show that learned features encode topic information, which can be used to find the section boundaries and divide the text into coherent segments. Unlike previous approaches, which mostly operate on sentence-level, we consistently use a broader context of an entire paragraph and assume topical independence of preceeding and succeeding text. We lastly introduce a novel large-scale dataset constructed from online Terms-of-Service documents, on which we compare against various traditional and deep learning baselines, showing significantly better performance of Transformer-based methods.
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning

Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment classification. We aim to induce domain invariant optimal classifiers rather than distribution matching. To this end, we introduce in-domain contrastive learning and entropy minimization. Also, we find through ablation studies that these two techniques behaviour differently in case of large label distribution shift and conclude that the best practice is to choose one of them adaptively according to label distribution shift. The new state-of-the-art results our model achieves on standard benchmarks show the efficacy of the proposed method.
Codeswitched Sentence Creation using Dependency Parsing

Codeswitching has become one of the most common occurrences across multilingual speakers of the world, especially in countries like India which encompasses around 23 official languages with the number of bilingual speakers being around 300 million. The scarcity of Codeswitched data becomes a bottleneck in the exploration of this domain with respect to various Natural Language Processing (NLP) tasks. We thus present a novel algorithm which harnesses the syntactic structure of English grammar to develop grammatically sensible Codeswitched versions of English-Hindi, English-Marathi and English-Kannada data. Apart from maintaining the grammatical sanity to a great extent, our methodology also guarantees abundant generation of data from a minuscule snapshot of given data. We use multiple datasets to showcase the capabilities of our algorithm while at the same time we assess the quality of generated Codeswitched data using some qualitative metrics along with providing baseline results for couple of NLP tasks.