Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study

Background Research in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. Objective The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an individual’s emotional state. We present our findings of a user study with 50 participants. Methods The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items; Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an individual’s emotion. Results Overall, 50 young adults participated in our study; of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P<. 006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Conclusions Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.
Relating Bryophyte Assemblages to a Remotely Sensed Depth-to-Water Index in Boreal Forests

Given the habitat moisture (air humidity or soil moisture) preferences of many forest bryophytes, we explored whether the depth-to-water (DTW) index, derived from remotely sensed Light Detection and Ranging (LiDAR) data, was related to fine-scale patterns of spatial variation in bryophyte abundance, diversity, and composition. The goal was to assess the utility of the topographic DTW index as a tool to decipher trends in bryophyte assemblages along a site wetness gradient in the boreal mixedwood forest. Discrete Airborne Laser Scanning (ALS) data were acquired over the entire Ecosystem Management Emulating Natural Disturbance (EMEND) experimental site located in northwestern Alberta, Canada (56° 46′ 13″ N, 118° 22′ 28″ W), based on which we calculated a mathematical index of approximate depth to water at or below the soil surface at 1 m resolution using the Wet-Areas Mapping model. Bryophytes (mosses and liverworts) were sampled in permanent sample plots in unmanaged forest stands of varying dominant canopy tree composition. The relationships between DTW and bryophyte cover, richness, diversity, and composition in broadleaf (deciduous)-, mixed, and conifer-dominated boreal forest stands were analyzed using linear mixed-effect models and multivariate analyses. Bryophyte cover was highest in conifer-dominated forest, which occupied the wetter end of the DTW gradient, followed by mixed forest, whereas broadleaf forest, which occupied the drier end of the DTW gradient, had the lowest cover but highest bryophyte diversity. Bryophyte cover in conifer-dominated forests was positively related to site moisture (negatively related to the DTW index). In contrast, bryophyte species richness and diversity were negatively related to site moisture (increased at higher DTW values) in all forest types. DTW explained significant variation in bryophyte species composition in mixed forests, while indicator species analysis identified species with preferences for wet, moist, and dry site conditions in each forest type. Our results corroborate the importance of site moisture as a driver of bryophyte assemblages but, interestingly, there were important differences among forest types, which themselves are distributed across a gradient of site moisture. Our study demonstrates the utility of the topographic DTW index for understanding fine-scale (plot-level) variation in bryophyte assemblages in forested landscapes.
Uncertainty analysis of projections of ozone-depleting substances: mixing ratios, EESC, ODPs, and GWPs

Abstract. The rates at which ozone-depleting substances (ODSs) are removed from the atmosphere, which determine the lifetimes of these ODSs, are key factors for determining the rate of ozone layer recovery in the coming decades. We present here a comprehensive uncertainty analysis of future mixing ratios of ODSs, levels of equivalent effective stratospheric chlorine (EESC), ozone depletion potentials, and global warming potentials (GWPs), using, among other information, the 2013 WCRP/SPARC (World Climate Research Programme/Stratospheric Processes and their Role in Climate) assessment of lifetimes of ODSs and their uncertainties. The year EESC returns to pre-1980 levels, a metric commonly used to indicate a level of recovery from ODS-induced ozone depletion, is 2048 for midlatitudes and 2075 for Antarctic conditions based on the lifetimes from the SPARC assessment, which is about 2 and 4 yr, respectively, later than based on the lifetimes from the WMO (World Meteorological Organization) assessment of 2011. However, the uncertainty in this return to 1980 levels is much larger than the shift due to this change in lifetimes. The year EESC returns to pre-1980 levels ranges from 2039 to 2064 (95% confidence interval) for midlatitudes and from 2061 to 2105 for the Antarctic spring. The primary contribution to these ranges comes from the uncertainty in the lifetimes, with smaller contributions from uncertainties in other modeled parameters. The earlier years of the return estimates derived by the uncertainty analysis, i.e., 2039 for midlatitudes and 2061 for Antarctic spring, are comparable to a hypothetical scenario in which emissions of ODSs cease in 2014. The later end of the range, i.e., 2064 for midlatitudes and 2105 for Antarctic spring, can also be obtained by a scenario with an additional emission of about 7 Mt CFC-11 eq. (eq. – equivalent) in 2015, which is the same as about 2 times the projected cumulative anthropogenic emissions of all ODSs from 2014 to 2050, or about 12 times the projected cumulative HCFC emissions from 2014 to 2050.
Challenges and opportunities in land surface modelling of savanna ecosystems

Abstract. The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes. Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.
The red deer rut revisited: female excursions but no evidence females move to mate with preferred males

In polygynous mammals, most variance in male reproductive success is expected to result from competition between males, and the role of female behavior remains poorly understood. Contests between red deer males during the annual rut are one of the most famous examples of male--male competition. However, anecdotal evidence suggests females in estrus make substantial movements, changing harems, and potentially disrupting the outcome of male contests. In other polygynous mammals, such movements have been interpreted as evidence of female mating preferences. Here, we use 34 years of detailed observational data on wild red deer to show that 43% of estrous females are found in different harems between successive observations and that 64% of such females make substantial movements (up to 4 km) when this occurs. Approximately 45% of these movements result in the male into whose harem a female moved fathering her offspring. We then test whether females move nonrandomly with respect to male phenotype, consistent with the hypothesis that females move to mate with preferred males. Although in general, females were more likely to be found in larger harems and the harems of younger males after harem changes, these effects were not specific to estrous females. Further, estrous females were not more likely to be found in the harems of less related males. We therefore find little support for the idea that estrous females move between harems to mate with a preferred male; as a result, the reasons females make such extraordinary movements when in estrus remain unclear. Copyright 2011, Oxford University Press.
Learning Emotion-enriched Word Representations

Most word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar meanings. As a consequence, emotionally dissimilar words, such as “happy” and “sad” occurring in similar contexts would purport more similar meaning than emotionally similar words, such as “happy” and “joy”. This complication leads to rather undesirable outcome in predictive tasks that relate to affect (emotional state), such as emotion classification and emotion similarity. In order to address this limitation, we propose a novel method of obtaining emotion-enriched word representations, which projects emotionally similar words into neighboring spaces and emotionally dissimilar ones far apart. The proposed approach leverages distant supervision to automatically obtain a large training dataset of text documents and two recurrent neural network architectures for learning the emotion-enriched representations. Through extensive evaluation on two tasks, including emotion classification and emotion similarity, we demonstrate that the proposed representations outperform several competitive general-purpose and affective word representations.
Is the 1-norm the best convex sparse regularization?

The 1-norm is a good convex regularization for the recovery of sparse vectors from under-determined linear measurements. No other convex regularization seems to surpass its sparse recovery performance. How can this be explained? To answer this question, we define several notions of " best " (convex) regulariza-tion in the context of general low-dimensional recovery and show that indeed the 1-norm is an optimal convex sparse regularization within this framework.
Perfluorinated compounds in red-throated divers from the German Baltic Sea: new findings from their distribution in 10 different tissues

Environmental context Perfluorinated compounds are commonly used chemicals that are detected globally in all environmental matrices. We investigated the extent of contamination by perfluorinated compounds in the red-throated diver, a marine predatory bird, and observed an unusual distribution of perfluorinated compounds in tissues. The data help us to better understand the behaviour of these contaminants in organisms. Abstract Twenty poly- and perfluorinated compounds (PFCs) were investigated in four red-throated divers (Gavia stellata) from the German Baltic Sea sampled in 2005. Concentrations of perfluoroalkyl sulfonates (PFSAs), perfluoroalkyl carboxylates (PFCAs), alkylated perfluoroalkyl sulfonamides, alkylated perfluoroalkyl sulfonamidoethanols and perfluorooctane sulfonamides were determined in blood, brain, fatty tissue, gall bladder, heart, kidney, liver, lung, muscle and spleen by high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). For quantification standard addition was applied. Twelve compounds were detected with average total PFC concentrations ranging from 42 ng g–1 in muscle to 220 ng g–1 in liver samples. Perfluorooctane sulfonate (PFOS) was the major compound in each of the 40 tissue samples. Except for brain, perfluoroundecanoate was the dominant PFCA. In brain samples preferential enrichment of long-chain PFSAs and PFCAs was observed. The total PFC body burden was estimated to 100 ± 39 µg. Multivariate statistical analyses supported the identification of the preferred accumulation ‘location’ of individual PFCs in the birds’ body.
Influence of transverse-shear and large-deformation effects on the low-speed impact response of laminated composite plates

An analytical procedure is presented for determining the transient response of simply supported, rectangular laminated composite plates subjected to impact loads from airgun-propelled or dropped-weight impactors. A first-order shear-deformation theory is included in the analysis to represent properly any local short-wave-length transient bending response. The impact force is modeled as a locally distributed load with a cosine-cosine distribution. A double Fourier series expansion and the Timoshenko small-increment method are used to determine the contact force, out-of-plane deflections, and in-plane strains and stresses at any plate location due to an impact force at any plate location. The results of experimental and analytical studies are compared for quasi-isotropic laminates. The results indicate that using the appropriate local force distribution for the locally loaded area and including transverse-shear-deformation effects in the laminated plate response analysis are important. The applicability of the present analytical procedure based on small deformation theory is investigated by comparing analytical and experimental results for combinations of quasi-isotropic laminate thicknesses and impact energy levels. The results of this study indicate that large-deformation effects influence the response of both 24- and 32-ply laminated plates, and that a geometrically nonlinear analysis is required for predicting the response accurately.
ALMA Observations of Dust Polarization and Molecular Line Emission from the Class 0 Protostellar Source Serpens SMM1

We present high angular resolution dust polarization and molecular line observations carried out with the Atacama Large Millimeter/submillimeter Array (ALMA) toward the Class 0 protostar Serpens SMM1. By complementing these observations with new polarization observations from the Submillimeter Array (SMA) and archival data from the Combined Array for Research in Millimeter-wave Astronomy (CARMA) and the James Clerk Maxwell Telescopes (JCMT), we can compare the magnetic field orientations at different spatial scales. We find major changes in the magnetic field orientation between large (∼0.1 pc) scales—where the magnetic field is oriented E–W, perpendicular to the major axis of the dusty filament where SMM1 is embedded—and the intermediate and small scales probed by CARMA (∼1000 au resolution), the SMA (∼350 au resolution), and ALMA (∼140 au resolution). The ALMA maps reveal that the redshifted lobe of the bipolar outflow is shaping the magnetic field in SMM1 on the southeast side of the source; however, on the northwestern side and elsewhere in the source, low-velocity shocks may be causing the observed chaotic magnetic field pattern. High-spatial-resolution continuum and spectral-line observations also reveal a tight (∼130 au) protobinary system in SMM1-b, the eastern component of which is launching an extremely high-velocity, one-sided jet visible in both ) and ); however, that jet does not appear to be shaping the magnetic field. These observations show that with the sensitivity and resolution of ALMA, we can now begin to understand the role that feedback (e.g., from protostellar outflows) plays in shaping the magnetic field in very young, star-forming sources like SMM1.
Ad-Net: Audio-Visual Convolutional Neural Network for Advertisement Detection In Videos

Personalized advertisement is a crucial task for many of the online businesses and video broadcasters. Many of today's broadcasters use the same commercial for all customers, but as one can imagine different viewers have different interests and it seems reasonable to have customized commercial for different group of people, chosen based on their demographic features, and history. In this project, we propose a framework, which gets the broadcast videos, analyzes them, detects the commercial and replaces it with a more suitable commercial. We propose a two-stream audio-visual convolutional neural network, that one branch analyzes the visual information and the other one analyzes the audio information, and then the audio and visual embedding are fused together, and are used for commercial detection, and content categorization. We show that using both the visual and audio content of the videos significantly improves the model performance for video analysis. This network is trained on a dataset of more than 50k regular video and commercial shots, and achieved much better performance compared to the models based on hand-crafted features.
Training a Network of Spiking Neurons with Equilibrium Propagation

Backpropagation is almost universally used to train artificial neural networks. However, there are several reasons that backpropagation could not be plausibly implemented by biological neurons. Among these are the facts that (1) biological neurons appear to lack any mechanism for sending gradients backwards across synapses, and (2) biological “spiking” neurons emit binary signals, whereas back-propagation requires that neurons communicate real numbers between one another. Recently (Scellier and Bengio, 2017), demonstrated an alternative to backpropagation, called Equilibrium Propagation, wherein gradients are implicitly computed by the dynamics of the neural network, so that neurons do not need an internal mechanism for backpropagation of gradients. This provides an interesting solution to problem (1). In this paper, we address problem (2) by proposing a way in which Equilibrium Propagation can be implemented with neurons which are constrained to just communicate binary values at each time step. We show that with appropriate step-size annealing, we can converge to the same fixed-point as a real-valued neural network, and that with predictive coding, we can make this convergence much faster. We demonstrate that the resulting model can be used to train a neural network using the update scheme from Equilibrium propagation.
The Hopkins Ultraviolet Telescope: The Final Archive

The Hopkins Ultraviolet Telescope (HUT) was a 0.9 m telescope and moderate-resolution (Δλ = 3 Å) far-ultraviolet (820–1850 Å) spectrograph that flew twice on the space shuttle, in 1990 December (Astro-1, STS-35) and 1995 March (Astro-2, STS-67). The resulting spectra were originally archived in a nonstandard format that lacked important descriptive metadata. To increase their utility, we have modified the original data-reduction software to produce a new and more user-friendly data product, a time-tagged photon list similar in format to the Intermediate Data Files (IDFs) produced by the Far Ultraviolet Spectroscopic Explorer calibration pipeline. We have transferred all relevant pointing and instrument-status information from locally-archived science and engineering databases into new FITS header keywords for each data set. Using this new pipeline, we have reprocessed the entire HUT archive from both missions, producing a new set of calibrated spectral products in a modern FITS format that is fully compliant with Virtual Observatory requirements. For each exposure, we have generated quick-look plots of the fully-calibrated spectrum and associated pointing history information. Finally, we have retrieved from our archives HUT TV guider images, which provide information on aperture positioning relative to guide stars, and converted them into FITS-format image files. All of these new data products are available in the new HUT section of the Mikulski Archive for Space Telescopes (MAST), along with historical and reference documents from both missions. In this article, we document the improved data-processing steps applied to the data and show examples of the new data products.
Somatic Accumulation of GluA1-AMPA Receptors Leads to Selective Cognitive Impairments in Mice

The GluA1 subunit of the L-α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) plays a crucial, but highly selective, role in cognitive function. Here we analyzed AMPAR expression, AMPAR distribution and spatial learning in mice (Gria1R/R), expressing the “trafficking compromised” GluA1(Q600R) point mutation. Our analysis revealed somatic accumulation and reduction of GluA1(Q600R) and GluA2, but only slightly reduced CA1 synaptic localization in hippocampi of adult Gria1R/R mice. These immunohistological changes were accompanied by a strong reduction of somatic AMPAR currents in CA1, and a reduction of plasticity (short-term and long-term potentiation, STP and LTP, respectively) in the CA1 subfield following tetanic and theta-burst stimulation. Nevertheless, spatial reference memory acquisition in the Morris water-maze and on an appetitive Y-maze task was unaffected in Gria1R/R mice. In contrast, spatial working/short-term memory during both spontaneous and rewarded alternation tasks was dramatically impaired. These findings identify the GluA1(Q600R) mutation as a loss of function mutation that provides independent evidence for the selective role of GluA1 in the expression of short-term memory.