Miracle-dz/UniFAR-FTUs huggingface dataset:
Swipe Dynamics as a Means of Authentication: Results From a Bayesian Unsupervised Approach

The field of behavioural biometrics stands as an appealing alternative to more traditional biometric systems due to the ease of use from a user perspective and potential robustness to presentation attacks. This paper focuses its attention to a specific type of behavioural biometric utilising swipe dynamics, also referred to as touch gestures. In touch gesture authentication, a user swipes across the touchscreen of a mobile device to perform an authentication attempt. A key characteristic of touch gesture authentication and new behavioural biometrics in general is the lack of available data to train and validate models. From a machine learning perspective, this presents the classic curse of dimensionality problem and the methodology presented here focuses on Bayesian unsupervised models as they are well suited to such conditions. This paper presents results from a set of experiments consisting of 38 sessions with labelled ‘victim’ as well as blind and over-the-shoulder presentation attacks. Three models are compared using this dataset; two single-mode models: a shrunk covariance estimate and a Bayesian Gaussian distribution, as well as a Bayesian non-parametric infinite mixture of Gaussians, modelled as a Dirichlet Process. Equal error rates (EER) for the three models are compared and attention is paid to how these vary across the two single-mode models at differing numbers of enrolment samples.
Daily dynamics of photosynthesis of the freshwater red alga Sirodotia delicatula (Batrachospermales, Rhodophyta)

The daily course of photosynthetic parameters of a population of the freshwater red alga Sirodotia delicatula from São Paulo State, Brazil (20°43′24″S, 49°18′21″W) was investigated under natural and laboratory conditions using dissolved oxygen and in vivo chlorophyll fluorescence techniques. Field specimens in laboratory conditions showed a defined daily pattern for net photosynthesis (NP) with two peaks observed in marine macroalgae and some freshwater red algae: the first (the highest) during the morning, and the second (the lowest and less evident) during the afternoon. Values of electron transport rate did not show a clear pattern of daily variation. NP results suggest the existence of an endogenous rhythm controlling photosynthesis. The study under natural conditions in two contrasting periods (autumn (June) and spring (October)) showed that the daily course of effective and potential quantum yield values was negatively correlated with irradiance and values were similar in the beginning and end of the day. These data evidenced, respectively, high excitement pressure on photosystem II and good recovery capacity (with lower values in spring) and a lack of irreversible photodamage to photosynthetic apparatus due to the prolonged exposure to high irradiances. Non‐photochemical quenching values were also negatively correlated with the irradiance, suggesting a low dissipation capacity of excess energy absorbed by reaction centers. The results evidenced a typical pattern of daily variation with evident response to irradiance.
Probabilistic neural networks for fluid flow surrogate modeling and data recovery

Artificial neural networks (ANNs) have recently been applied to several fluid dynamics applications. However, there are a very limited number of studies that assess the fidelity of ANN deployments, a function of algorithm choice and training data quality, by quantifying uncertainties in predictions. This diminishes their utility for practical modeling requirements. In an effort to address this, a probabilistic NN that provides confidence intervals for its predictions in a computationally effective manner is used. This approach is demonstrated in surrogate modeling and flow reconstruction tasks with promising results.
A robust but easily implementable remote control for quadrotors: Experimental acrobatic flight tests

Experimental flight tests are reported about quadrotors UAVs via a recent model-free control (MFC) strategy, which is easily implementable. We show that it is possible to achieve acrobatic rate control of the UAV, which is beyond the previous standard. The same remote controller is tested on two physical vehicles without any re-tuning. It produces in both cases low tracking error. We show that MFC is robust even when the quadrotor is highly damaged. A video footage can be found at: this https URL
MementoEmbed and Raintale for Web Archive Storytelling

For traditional library collections, archivists can select a representative sample from a collection and display it in a featured physical or digital library space. Web archive collections may consist of thousands of archived pages, or mementos. How should an archivist display this sample to drive visitors to their collection? Search engines and social media platforms often represent web pages as cards consisting of text snippets, titles, and images. Web storytelling is a popular method for grouping these cards in order to summarize a topic. Unfortunately, social media platforms are not archive-aware and fail to consistently create a good experience for mementos. They also allow no UI alterations for their cards. Thus, we created MementoEmbed to generate cards for individual mementos and Raintale for creating entire stories that archivists can export to a variety of formats.
Programmable Quantum Annealing Architectures with Ising Quantum Wires

Quantum annealing aims at solving optimization problems efficiently by preparing the ground state of an Ising spin-Hamiltonian quantum mechanically. A prerequisite of building a quantum annealer is the implementation of programmable long-range two-, three- or multi-spin Ising interactions. We discuss an architecture, where the required spin interactions are implemented via two-port, or in general multi-port quantum Ising wires connecting the spins of interest. This quantum annealing architecture of spins connected by Ising quantum wires can be realized by exploiting the three dimensional character of atomic platforms, including atoms in optical lattices and Rydberg tweezer arrays. The realization only requires engineering on-site terms and two-body interactions between nearest neighboring qubits. We illustrate the approach for few spin devices solving Max-Cut and prime factorization problems, and discuss the potential scaling to large atom based systems.
Scatterer recognition via analysis of speckle patterns

Light scattering due to interaction with a material has long been known to create speckle patterns. We have demonstrated that even though speckle patterns from different objects are very similar, they contain minute dissimilarities that can be used to differentiate between the originating scatterers. We first approached this problem using a convolutional neural network—a deep learning algorithm—to show that indeed specific speckle patterns can be linked to the respective materials creating them. We then progressed to use recorded speckle patterns created from different materials in order to measure statistical parameters that possess a well-defined physical meaning. Using these parameters gave similar scatterer recognition abilities while gaining insight on the physical reasons for these material-dependent statistical deviations.
Low-loss connection of weight vectors: distribution-based approaches

Recent research shows that sublevel sets of the loss surfaces of overparameterized networks are connected, exactly or approximately. We describe and compare experimentally a panel of methods used to connect two low-loss points by a low-loss curve on this surface. Our methods vary in accuracy and complexity. Most of our methods are based on "macroscopic" distributional assumptions, and some are insensitive to the detailed properties of the points to be connected. Some methods require a prior training of a "global connection model" which can then be applied to any pair of points. The accuracy of the method generally correlates with its complexity and sensitivity to the endpoint detail.
All-Digital FPGA-based DAC with None or Few External Components

One of the many limitations with the mixed-signal design is physically testing circuit ideas. While it is easier to test digital circuits with FPGAs, this can not be done usually with mixed-signal circuits. Although some FPGAs have built-in analog-to-digital and digital-to-analog converters, regular commercial FPGAs development boards and low-cost FPGAs lack built-in data converters. Here we introduce an all-digital FPGA-based DAC, which is one of the main blocks to enable mixed-signal experiments. The DAC can be synthesized entirely in an FPGA and does not require the use of external components. Furthermore, and to extend its range of applications, a discussion regarding the proposed DAC's problems and possible solutions is presented. Experimental demonstration of a 4-bit and a 5-bit DAC corroborate the theoretical analysis developed in this work. This work also suggests a scheme which includes few external resistors to improve the linearity (DNL$\leq$0.25LSB and an INL$\leq$0.5LSB), and the power consumption (5X improvement over the standalone configuration).
Some Parasitic Worms from Australia

In this paper short descriptions are given of three new species (two Cestodes and a Nematode) contained in a collection kindly entrusted to the writer by Prof. G. H. F. Nuttall, F.R. S. , for determination. The material was collected in Queensland by Mr F. H. S. Roberts, and forwarded to Prof. Nuttall by Mr Robert Veitch, Chief Entomologist, Department of Agriculture and Stock, Brisbane.
Composition and temporal variability of picoeukaryote communities at a coastal site of the English Channel from 18S rDNA sequences

We analyzed picoeukaryote assemblages at a French coastal site of the English Channel by sequencing cloned eukaryotic 18S rRNA genes in eight genetic libraries constructed from environmental samples (seven coastal, one estuarine) collected at different periods of the year. Eight hundred clones were examined by amplified restriction fragment length polymorphism (RFLP) using the restriction enzyme HaeIII. The coverage value calculated from the relative distribution of RFLP types was low, indicating that the library diversity was not entirely recovered. A variable region of the rRNA gene was partially sequenced (550 bases) and analyzed for 397 clones. Thirty‐two clones were affiliated with metazoans. Of the remaining clones, 132 were affiliated to algal classes (especially Prasinophyceae, Cryptophyceae, Dinophyceae, and Prymnesiophyceae) and 107 to known heterotrophic groups (Cercozoa, choanoflagellates, stramenopiles, and ciliates). One hundred three sequences fell into uncultivated groups of stramenopiles (43 clones) and alveolates (60 clones). We also found two potentially novel eukaryotic lineages, represented by 9 and 14 clones, respectively, not belonging to any known eukaryotic group. The overall composition of the picoeukaryote community remained fairly stable at the class/division level except during the early summer diatom bloom, when groups such as the Cryptophyceae and the ciliates completely disappeared. However, at a finer taxonomic level (corresponding to 98% sequence identity), the majority of the operational taxonomic units (OTU) were only observed once.
BURT: BERT-inspired Universal Representation from Twin Structure

Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream Natural Language Processing (NLP) tasks. However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, leading to unsatisfying representations of various sequences with different granularities. Especially, as sentence-level representations taken as the full training context in these models, there comes inferior performance on lower-level linguistic units (phrases and words). In this work, we present BURT (BERT inspired Universal Representation from Twin Structure) that is capable of generating universal, fixed-size representations for input sequences of any granularity, i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives. Our proposed BURT adopts the Siamese network, learning sentence-level representations from natural language inference dataset and word/phrase-level representations from paraphrasing dataset, respectively. We evaluate BURT across different granularities of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where BURT substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word/phrase-level representation.
Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2k contingency table in which one element-the number of individuals appearing in none of the samples-remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.
Learning-based link prediction analysis for Facebook100 network

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.
Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration

Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. Moreover, both agents may have differing capabilities that are unknown to one another. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting, with an associated online solver. Experiments show our approach leads to better team performance both in simulation and in a real-world study with human subjects.
Combining Breast Cancer Risk Prediction Models

Simple Summary BRCAPRO is a widely used breast cancer risk prediction model based on family history. A major limitation of this model is that it does not consider non-genetic risk factors. We expand BRCAPRO by combining it with another popular model, BCRAT, that uses mostly non-genetic risk factors, and show that the expanded model can achieve improvements in prediction accuracy over both BRCAPRO and BCRAT. Abstract Accurate risk stratification is key to reducing cancer morbidity through targeted screening and preventative interventions. Multiple breast cancer risk prediction models are used in clinical practice, and often provide a range of different predictions for the same patient. Integrating information from different models may improve the accuracy of predictions, which would be valuable for both clinicians and patients. BRCAPRO is a widely used model that predicts breast cancer risk based on detailed family history information. A major limitation of this model is that it does not consider non-genetic risk factors. To address this limitation, we expand BRCAPRO by combining it with another popular existing model, BCRAT (i.e., Gail), which uses a largely complementary set of risk factors, most of them non-genetic. We consider two approaches for combining BRCAPRO and BCRAT: (1) modifying the penetrance (age-specific probability of developing cancer given genotype) functions in BRCAPRO using relative hazard estimates from BCRAT, and (2) training an ensemble model that takes BRCAPRO and BCRAT predictions as input. Using both simulated data and data from Newton-Wellesley Hospital and the Cancer Genetics Network, we show that the combination models are able to achieve performance gains over both BRCAPRO and BCRAT. In the Cancer Genetics Network cohort, we show that the proposed BRCAPRO + BCRAT penetrance modification model performs comparably to IBIS, an existing model that combines detailed family history with non-genetic risk factors.
Self-evolving ghost imaging

Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-evolving ghost imaging (SEGI). We introduce a genetic algorithm to optimize the illumination patterns in real-time to match the objects shape according to the measured total light intensity. We theoretically and experimentally demonstrate this concept for static and dynamic imaging. This method opens new perspectives for real-time ghost imaging in applications such as remote sensing (e.g. machine vision, LiDAR systems in autonomous vehicles) and biological imaging.
Melanoma Image Classification Based on Multivariate Parametric Statistical Tests of Hypothesis

This chapter proposes a novel method, based on the multivariate parametric statistical tests of hypotheses, which classifies the normal skin lesion images and the various stages of the melanoma images. The melanoma images are categorized into two classes, such as initial stage and advanced stage, based on the degree of aggressiveness of the cancer. The region of interest is identified and segmented from the input skin melanoma image. The features, such as HSV color, shape, and texture, are extracted from the region of interest. The features are treated as a feature space, which is assumed to be a multivariate normal random field. The proposed statistical tests are employed to identify and classify the melanoma images. The proposed method yields an average correct classification up to 91.55% for the normal skin lesion versus the initial and the advanced stages of the melanoma images, up to 91.39% for initial stage melanoma versus the normal skin lesion and the advanced stages melanoma, and up to 92.27% for the advanced stage melanoma versus the normal skin lesion and the initial stage melanoma. The proposed method yields better results.
QPLEX: Duplex Dueling Multi-Agent Q-Learning

We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local decision-making. However, in order to achieve scalability, existing MARL methods either limit representation expressiveness of their value function classes or relax the IGM consistency, which may suffer from instability risk or lead to poor performance. This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function. This duplex dueling structure encodes the IGM principle into the neural network architecture and thus enables efficient value function learning. Theoretical analysis shows that QPLEX achieves a complete IGM function class. Empirical experiments on StarCraft II micromanagement tasks demonstrate that QPLEX significantly outperforms state-of-the-art baselines in both online and offline data collection settings, and also reveal that QPLEX achieves high sample efficiency and can benefit from offline datasets without additional online exploration.
Algorithm for Solutions of Nonlinear Equations of Strongly Monotone Type and Applications to Convex Minimization and Variational Inequality Problems

Real-life problems are governed by equations which are nonlinear in nature. Nonlinear equations occur in modeling problems, such as minimizing costs in industries and minimizing risks in businesses. A technique which does not involve the assumption of existence of a real constant whose calculation is unclear is used to obtain a strong convergence result for nonlinear equations of (p, {\eta})-strongly monotone type, where {\eta} > 0, p > 1. An example is presented for the nonlinear equations of (p, {\eta})-strongly monotone type. As a consequence of the main result, the solutions of convex minimization and variational inequality problems are obtained. This solution has applications in other fields such as engineering, physics, biology, chemistry, economics, and game theory.
Bright, continuous beams of cold free radicals

We demonstrate a cryogenic buffer gas-cooled molecular beam source capable of producing bright, continuous beams of cold and slow free radicals via laser ablation over durations of up to 60~seconds. The source design uses a closed liquid helium reservoir as a large thermal mass to minimize heating and ensure reproducible beam properties during operation. Under typical conditions, the source produces beams of our test species SrF, containing $5\times10^{12}~$molecules per steradian per second in the $X^{2}\Sigma(v=0, N=1)$ state with a rotational temperature of $1.0(2)~$K and a forward velocity of $140~$m/s. The beam properties are robust and unchanged for multiple cell geometries but depend critically on the helium buffer gas flow rate, which must be $\geq10~$ standard cubic centimeters per minute to produce bright, continuous beams of molecules for an ablation repetition rate of $55~$Hz.