Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Cognitive States from Multimodal Measures in Information Science

paper presents our work using uni- and multi-modal measures to infer and predict context and mental states of a person engaged in interactive information seeking. We describe our use of measures derived from eye-movement patterns in combination with interaction logs to infer dynamic user states (such as cognitive load), user context (such as task characteristics), and persistent user characteristics (such as domain knowledge). We briefly present on-going projects that add further psycho- physiological signals, such as electro-encephalography (EEG) and galvanic skin response (GSR).
Tilted Snowplow Ponderomotive Electron Acceleration With Spatio-Temporally Shaped Ultrafast Laser Pulses

We propose a novel scheme for using the ponderomotive force of a tilted ultrafast laser pulse to accelerate electrons in free space. The tilt of the intensity envelope results from the angular dispersion of the pulse's spectrum and slows down the interaction of the pulse with free electrons. The slower effective pulse velocity allows time for the electrons to accelerate from rest while remaining on the wave. We present both non-relativistic and relativistic analytic single-particle models in the adiabatic ponderomotive approximation, describing the process for an ideal infinite tilted pulse as well as a finite width beam. The analysis predicts the threshold intensity as a function of the pulse front tilt angle and shows that in the ideal case the output energy of the electrons is four times that of the ponderomotive potential at the capture threshold. Full-field simulations using the 2D OSIRIS 4.0 particle-in-cell code confirm the basic scheme. This tilted pulse acceleration scheme shows promise as a lab-scale method of accelerating electrons to the MeV level with good energy and angular resolution, to be used for ultrafast electron diffraction or injection into a second stage accelerator.
A quantum Boltzmann equation for intrinsic decoherence in a dilute gas

We use a careful treatment of time-dependent wave-mechanical scattering to determine the conditions under which a dilute, non-degenerate quantum gas can obey a Boltzmann equation. If the gas possesses weak long-range coherence, such as may occur when a gas is quantum mechanically throttled by tunnelling through a barrier, collisions within the gas will suppress this long range coherence. We calculate its decay rate and find it to be related to the Boltzmann equation's loss term.
Using Fenton Oxidation to Simultaneously Remove Different Estrogens from Cow Manure

The presence of estrogens in livestock excrement has raised concerns about their potential negative influence on animals and the overall food cycle. This is the first investigation to simultaneously remove estrogens, including estriol (E3), bisphenol A (BPA), diethylstilbestrol (DES), estradiol (E2), and ethinyl estradiol (EE2), from cow manure using a Fenton oxidation technique. Based on the residual concentrations and removal efficiency of estrogens, the Fenton oxidation reaction conditions were optimized as follows: a H2O2 dosage of 2.56 mmol/g, a Fe(II) to H2O2 molar ratio of 0.125 M/M, a solid to water mass ratio of 2 g/mL, an initial pH of 3, and a reaction time of 24 h. Under these conditions, the simultaneous removal efficiencies of E3, BPA, DES, E2, and EE2, with initial concentrations in cow manure of 97.40, 96.54, 100.22, 95.01, and 72.49 mg/kg, were 84.9%, 99.5%, 99.1%, 97.8%, and 84.5%, respectively. We clarified the possible Fenton oxidation reaction mechanisms that governed the degradation of estrogens. We concluded that Fenton oxidation technique could be effective for efficient removal of estrogens in livestock excrement. Results are of great importance for cow manure reuse in agricultural management, and can be used to reduce the threat of environmental estrogens to human health and ecological safety.
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics suffer from the need to select beforehand a small fraction of genes to be quantified over the entire transcriptome. Standard single-cell RNA sequencing (scRNA-seq) is more prevalent, easier to implement and can in principle capture any gene but cannot recover the spatial location of the cells. In this manuscript, we focus on the problem of imputation of missing genes in spatial transcriptomic data based on (unpaired) standard scRNA-seq data from the same biological tissue. Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes. After describing our generative model and an inference procedure for it, we compare gimVI to alternative methods from computational biology or domain adaptation on real datasets and outperform Seurat Anchors, Liger and CORAL to impute held-out genes.
Perceptual Limits of Optical See-Through Visors for Augmented Reality Guidance of Manual Tasks

Objective: The focal length of available optical see-through (OST) head-mounted displays (HMDs) is at least 2 m; therefore, during manual tasks, the user eye cannot keep in focus both the virtual and real content at the same time. Another perceptual limitation is related to the vergence-accommodation conflict, the latter being present in binocular vision only. This paper investigates the effect of incorrect focus cues on the user performance, visual comfort, and workload during the execution of augmented reality (AR)-guided manual task with one of the most advanced OST HMD, the Microsoft HoloLens. Methods: An experimental study was designed to investigate the performance of 20 subjects in a connect-the-dots task, with and without the use of AR. The following tests were planned: AR-guided monocular and binocular, and naked-eye monocular and binocular. Each trial was analyzed to evaluate the accuracy in connecting dots. NASA Task Load Index and Likert questionnaires were used to assess the workload and the visual comfort. Results: No statistically significant differences were found in the workload, and in the perceived comfort between the AR-guided binocular and monocular test. User performances were significantly better during the naked eye tests. No statistically significant differences in performances were found in the monocular and binocular tests. The maximum error in AR tests was 5.9 mm. Conclusion: Even if there is a growing interest in using commercial OST HMD, for guiding high-precision manual tasks, attention should be paid to the limitations of the available technology not designed for the peripersonal space.
Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems.
Perceived Impact as the Underpinning Mechanism of the End-Spurt and U-Shape Pacing Patterns

The end-spurt and U-shape reflect common pacing patterns across a variety of fields (e.g., running, swimming, cycling). To date, however, the literature lacks a clear, parsimonious account for these effects. Here, I propose these pacing patterns can be accounted for by a psychological mechanism termed perceived impact. As athletes perceive their actions to better affect task-progress, they become more motivated and perform better accordingly. To illustrate, if an athlete has five more laps to go during a race, completing the current lap closes 20% of the remaining distance. Alternatively, when she has two more laps to go, the current lap closes 50% of the remaining distance. In the latter case, the impact of completing a single lap on task-progress is perceived to be higher. Her motivation will increase accordingly near the end of the track – giving rise to an end-spurt. I demonstrate the mechanism’s predictive power by reproducing previous findings through simulations. I then move to discuss how this framework is theoretically insightful in view of previous accounts such as the Central Governor Model and the Psyco-Biological Model. I conclude this work with applied strategies for practitioners in their daily routines.
Convergence Rate of Distributed Optimization Algorithms Based on Gradient Tracking

We study distributed, strongly convex and nonconvex, multiagent optimization over (directed, time-varying) graphs. We consider the minimization of the sum of a smooth (possibly nonconvex) function--the agent's sum-utility plus a nonsmooth convex one, subject to convex constraints. In a companion paper, we introduced SONATA, the first algorithmic framework applicable to such a general class of composite minimization, and we studied its convergence when the smooth part of the objective function is nonconvex. The algorithm combines successive convex approximation techniques with a perturbed push-sum consensus mechanism that aims to track locally the gradient of the (smooth part of the) sum-utility. This paper studies the convergence rate of SONATA. When the smooth part of the objective function is strongly convex, SONATA is proved to converge at a linear rate whereas sublinar rate is proved when the objective function is nonconvex. To our knowledge, this is the first work proving a convergence rate (in particular, linear rate) for distributed algorithms applicable to such a general class of composite, constrained optimization problems over graphs.
Relational Analysis of Floating-Point Arithmetic

A set of abstract operations is given that allows the uniform analysis of integer and floating point arithmetic in the abstract domain of convex polyhedra. We show how to implement rounding faithfully and show that the modelling of rounding is necessary even for the analysis of integer variables. Different rounding modes occurring in floating point implementations are discussed and their implementation is given.
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent years, machine learning tools have been developed to aid this process. Intensive care unit (ICU) admissions represent the most data dense and time-critical patient care episodes. In this context, prediction models may help clinicians determine which patients are most at risk and prioritize care. However, flexible tools such as artificial neural networks (ANNs) suffer from a lack of interpretability limiting their acceptability to clinicians. In this work, we propose a novel interpretable Bayesian neural network architecture which offers both the flexibility of ANNs and interpretability in terms of feature selection. In particular, we employ a sparsity inducing prior distribution in a tied manner to learn which features are important for outcome prediction. We evaluate our approach on the task of mortality prediction using two real-world ICU cohorts. In collaboration with clinicians we found that, in addition to the predicted outcome results, our approach can provide novel insights into the importance of different clinical measurements. This suggests that our model can support medical experts in their decision making process.
Photometry, spectroscopy, and polarimetry of distant comet C/2014 A4 (SONEAR)

Context. The study of distant comets, which are active at large heliocentric distances, is important for a better understanding of their physical properties and mechanisms of long-lasting activity. Aims. We analyzed the dust environment of the distant comet C/2014 A4 (SONEAR), with a perihelion distance near 4.1 au, using comprehensive observations obtained by different methods. Methods. We present an analysis of spectroscopy, photometry, and polarimetry of comet C/2014 A4 (SONEAR), which were performed on November 5–7, 2015, when its heliocentric distance was 4.2 au and phase angle was 4.7°. Long-slit spectra and photometric and linear polarimetric images were obtained using the focal reducer SCORPIO-2 attached to the prime focus of the 6 m telescope BTA (SAO RAS, Russia). We simulated the behavior of color and polarization in the coma presenting the cometary dust as a set of polydisperse polyshapes rough spheroids. Results. No emission features were detected in the 3800–7200 Å wavelength range. The continuum showed a reddening effect with the normalized gradient of reflectivity 21.6 ± 0.2% per 1000 Å within the 4650–6200 Å wavelength region. The fan-like structure in the sunward hemisphere was detected. The radial profiles of surface brightness differ for r-sdss and g-sdss filters, indicating a predominance of submicron and micron-sized particles in the cometary coma. The dust color (g–r) varies from 0.75 ± 0.05m to 0.45 ± 0.06m along the tail. For an aperture radius near 20 000 km, the dust productions in various filters were estimated as Afρ = 680 ± 18 cm (r-sdss) and 887 ± 16 cm (g-sdss). The polarization map shows spatial variations in polarization over the coma from about −3% near the nucleus to −8% at a cometocentric distance of about 150 000 km. Our simulations show that the dust particles are dominated (or covered) by ice and tholin-like organics. Spatial changes in the color and polarization can be explained by particle fragmentation.
Genetic Variance in Uncoupling Protein 2 in Relation to Obesity, Type 2 Diabetes, and Related Metabolic Traits: Focus on the Functional −866G>A Promoter Variant (rs659366)

Uncoupling proteins (UCPs) are mitochondrial proteins able to dissipate the proton gradient of the inner mitochondrial membrane when activated. This decreases ATP-generation through oxidation of fuels and may theoretically decrease energy expenditure leading to obesity. Evidence from Ucp(−/−) mice revealed a role of UCP2 in the pancreatic β-cell, because β-cells without UCP2 had increased glucose-stimulated insulin secretion. Thus, from being a candidate gene for obesity UCP2 became a valid candidate gene for type 2 diabetes mellitus. This prompted a series of studies of the human UCP2 and UCP3 genes with respect to obesity and diabetes. Of special interest was a promoter variant of UCP2 situated 866bp upstream of transcription initiation (−866G>A, rs659366). This variant changes promoter activity and has been associated with obesity and/or type 2 diabetes in several, although not all, studies. The aim of the current paper is to summarize current evidence of association of UCP2 genetic variation with obesity and type 2 diabetes, with focus on the −866G>A polymorphism.
Motions of a Rimless Spoked Wheel: a Simple 3D System with Impacts

This paper discusses the mechanics of a rigid rimless spoked wheel, or regular polygon, ‘rolling’ downhill. By ‘rolling’, we mean motions in which the wheel pivots on one ‘support’ spoke until another spoke collides with the ground, followed by transfer of support to that spoke, and so on. We carry out three-dimensional (3D) numerical and analytical stability studies of steady motions of this system. At any fixed, large enough slope, the system has a one-parameter family of stable steady rolling motions. We find analytic approximations for the minimum required slope at a given heading for stable rolling in three dimensions, for the case of many spokes and small slope. The rimless wheel shares some qualitative features with passive–dynamic walking machines; it is a passive 3D system with intermittent impacts and periodic motions. In terms of complexity, it lies between one-dimensional impact oscillators and 3D walking machines. In contrast to a rolling disk on a flat surface which has steady rolling motion.
Kallikrein 12 Regulates Innate Resistance of Murine Macrophages against Mycobacterium bovis Infection by Modulating Autophagy and Apoptosis

Mycobacterium bovis (M. bovis) is a member of the Mycobacterium tuberculosis (Mtb) complex causing bovine tuberculosis (TB) and imposing a high zoonotic threat to human health. Kallikreins (KLKs) belong to a subgroup of secreted serine proteases. As their role is established in various physiological and pathological processes, it is likely that KLKs expression may mediate a host immune response against the M. bovis infection. In the current study, we report in vivo and in vitro upregulation of KLK12 in the M. bovis infection. To define the role of KLK12 in immune response regulation of murine macrophages, we produced KLK12 knockdown bone marrow derived macrophages (BMDMs) by using siRNA transfection. Interestingly, the knockdown of KLK12 resulted in a significant downregulation of autophagy and apoptosis in M. bovis infected BMDMs. Furthermore, we demonstrated that this KLK12 mediated regulation of autophagy and apoptosis involves mTOR/AMPK/TSC2 and BAX/Bcl-2/Cytochrome c/Caspase 3 pathways, respectively. Similarly, inflammatory cytokines IL-1β, IL-6, IL-12 and TNF-α were significantly downregulated in KLK12 knockdown macrophages but the difference in IL-10 and IFN-β expression was non-significant. Taken together, these findings suggest that upregulation of KLK12 in M. bovis infected murine macrophages plays a substantial role in the protective immune response regulation by modulating autophagy, apoptosis and pro-inflammatory pathways. To our knowledge, this is the first report on expression and the role of KLK12 in the M. bovis infection and the data may contribute to a new paradigm for diagnosis and treatment of bovine TB.
Direct pointer: direct manipulation for large-display interaction using handheld cameras

This paper describes the design and evaluation of a technique, Direct Pointer, that enables users to interact intuitively with large displays using cameras equipped on handheld devices, such as mobile phones and personal digital assistant (PDA). In contrast to many existing interaction methods that attempt to address the same problem, ours offers direct manipulation of the pointer position with continuous visual feedback. The primary advantage of this technique is that it only requires equipment that is readily available: an electronic display, a handheld digital camera, and a connection between the two. No special visual markers in the display content are needed, nor are fixed cameras pointing at the display. We evaluated the performance of Direct Pointer as an interaction product, showing that it performs as well as comparable techniques that require more sophisticated equipment.
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement

Security Analysts that work in a ‘Security Operations Center’ (SoC) play a major role in ensuring the security of the organization. The amount of background knowledge they have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat intelligence sources, like text descriptions about cyber-attacks, can be stored in a structured fashion in a cybersecurity knowledge graph. A cybersecurity knowledge graph can be paramount in aiding a security analyst to detect cyber threats because it stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple contains two cybersecurity entities with a relationship between them. In this work, we propose a system to create semantic triples over cybersecurity text, using deep learning approaches to extract possible relationships. We use the set of semantic triples generated through our system to assert in a cybersecurity knowledge graph. Security Analysts can retrieve this data from the knowledge graph, and use this information to form a decision about a cyber-attack.
Advantages and Limitations of Bacteriophages for the Treatment of Bacterial Infections

Bacteriophages (BPs) are viruses that can infect and kill bacteria without any negative effect on human or animal cells. For this reason, it is supposed that they can be used, alone or in combination with antibiotics, to treat bacterial infections. In this narrative review, the advantages and limitations of BPs for use in humans will be discussed. PubMed was used to search for all of the studies published from January 2008 to December 2018 using the key words: “BPs” or “phages” and “bacterial infection” or “antibiotic” or “infectious diseases. ” More than 100 articles were found, but only those published in English or providing evidence-based data were included in the evaluation. Literature review showed that the rapid rise of multi-drug-resistant bacteria worldwide coupled with a decline in the development and production of novel antibacterial agents have led scientists to consider BPs for treatment of bacterial infection. Use of BPs to overcome the problem of increasing bacterial resistance to antibiotics is attractive, and some research data seem to indicate that it might be a rational measure. However, present knowledge seems insufficient to allow the use of BPs for this purpose. To date, the problem of how to prepare the formulations for clinical use and how to avoid or limit the risk of emergence of bacterial resistance through the transmission of genetic material are not completely solved problems. Further studies specifically devoted to solve these problems are needed before BPs can be used in humans.
Testing the Swampland: $H_0$ tension

The de Sitter Swampland conjecture compels us to consider dark energy models where $\lambda(\phi) \equiv |\nabla_{\phi} V|/V$ is bounded below by a positive constant. Moreover, it has been argued for Quintessence models that the constant $\lambda$ scenario is the least constrained. Here we demonstrate that increasing $\lambda$ only exacerbates existing tension in the Hubble constant $H_0$. The identification of dark energy models that both evade observational bounds and alleviate $H_0$ tension constitutes a robust test for the Swampland program.