Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear thermodynamic formalism for flows

ABSTRACT We introduce a version of the nonlinear thermodynamic formalism for flows. Moreover, we discuss the existence, uniqueness, and characterization of equilibrium measures for almost additive families of continuous functions with tempered variation. We also consider with some care the special case of additive families for which it is possible to strengthen some of the results. The proofs are mainly based on multifractal analysis.
Diffuse Radiation from the Aquila Rift

We present an analysis of the diffuse ultraviolet (UV) background in a low latitude region near the Aquila Rift based on observations made by the Galaxy Evolution Explorer (GALEX). The UV background is at a level of about 2000 ph cm^-2 s^-1 sr^-1 \AA^-1 with no correlation with either the Galactic latitude or the 100 micron infrared (IR) emission. Rather, the UV emission falls off with distance from the bright B2 star HIP 88149, which is in the centre of the field. We have used a Monte Carlo model to derive an albedo of 0.6 - 0.7 in the UV with a phase function asymmetry factor (g) of 0.2 - 0.4. The value for the albedo is dependent on the dust distribution while g is determined by the extent of the halo.
Inducing strong convergence into the asymptotic behaviour of proximal splitting algorithms in Hilbert spaces

Proximal splitting algorithms for monotone inclusions (and convex optimization problems) in Hilbert spaces share the common feature to guarantee for the generated sequences in general weak convergence to a solution. In order to achieve strong convergence, one usually needs to impose more restrictive properties for the involved operators, like strong monotonicity (respectively, strong convexity for optimization problems). In this paper, we propose a modified Krasnosel'skiĭ–Mann algorithm in connection with the determination of a fixed point of a nonexpansive mapping and show strong convergence of the iteratively generated sequence to the minimal norm solution of the problem. Relying on this, we derive a forward–backward and a Douglas–Rachford algorithm, both endowed with Tikhonov regularization terms, which generate iterates that strongly converge to the minimal norm solution of the set of zeros of the sum of two maximally monotone operators. Furthermore, we formulate strong convergent primal–dual algorithms of forward–backward and Douglas–Rachford-type for highly structured monotone inclusion problems involving parallel-sums and compositions with linear operators. The resulting iterative schemes are particularized to the solving of convex minimization problems. The theoretical results are illustrated by numerical experiments on the split feasibility problem in infinite dimensional spaces.
880 μm SMA POLARIZATION OBSERVATIONS OF THE QUASAR 3C 286

For decades, the bright radio quasar 3C 286 has been widely recognized as one of the most reliable polarization calibrators at centimeter wavelengths because of its unchanging polarization position angle and high polarization percentage. However, it has become clear in recent years that the polarization position angle of 3C 286 changes with increasing frequency, increasing from ∼33° at 3 cm to ∼38° at 1 mm. With the advent of high-sensitivity polarization observations by current and future (sub)millimeter telescopes, knowledge of the position angle of 3C 286 at higher frequencies is critical for calibration. We report the first polarization observations of 3C 286 at submillimeter wavelengths, taken at 880 μm (340 GHz) with the Submillimeter Array. We find a polarization position angle and percentage of and 15.7% ± 0.8%, respectively, which is consistent with previous measurements at 1 mm.
Cis- and trans-regulation by histone H4 basic patch R17/R19 in metazoan development

The histone H4 basic patch is critical for chromatin structure and regulation of the chromatin machinery. However, the biological roles of these positively charged residues and the mechanisms by which they regulate gene expression remain unclear. In this study, we used histone mutagenesis to investigate the physiological function and downstream regulatory genes of H4 residues R17 and R19 in Drosophila. We found all histone mutations including R17A/E/H and R19A/E/H (R17 and R19 of H4 are substituted by A, E and H respectively) result in a range of growth defects and abnormalities in chromosomal high-order structures, whereas R17E mutation is embryonic lethal. RNA-seq demonstrates that downregulated genes in both R17A and R19A show significant overlap and are enriched in development-related pathways. In addition, Western and cytological analyses showed that the R17A mutation resulted in a significant reduction in H4K16 acetylation and male offspring, implying that the R17 may be involved in male dosage compensation mechanisms. R19 mutation on the other hand strongly affect Gpp (Dot1 homologue in flies)-mediated H3K79 methylation, possibly through histone crosstalk. Together these results provide insights into the differential impacts of positive charges of H4 basic patch R17/R19 on regulation of gene transcription during developmental processes.
Optimal control for feedback cooling in cavityless levitated optomechanics

We consider feedback cooling in a cavityless levitated optomechanics setup, and we investigate the possibility to improve the feedback implementation. We apply optimal control theory to derive the optimal feedback signal both for quadratic (parametric) and linear (electric) feedback. We numerically compare optimal feedback against the typical feedback implementation used for experiments. In order to do so, we implement a state estimation scheme that takes into account the modulation of the laser intensity. We show that such an implementation allows us to increase the feedback strength, leading to faster cooling rates and lower center-of-mass temperatures.
Female dewlap ornaments are evolutionarily labile and associated with increased diversification rates in Anolis lizards

The evolution of costly signalling traits has largely focused on male ornaments. However, our understanding of ornament evolution is necessarily incomplete without investigating the causes and consequences of variation in female ornamentation. Here, we study the Anolis lizard dewlap, a trait extensively studied as a male secondary sexual characteristic but present in females of several species. We characterized female dewlaps for 339 species to test hypotheses about their evolution. Our results did not support the hypothesis that female dewlaps are selected against throughout the anole phylogeny. Rather, we found that female dewlaps were evolutionary labile. We also did not find support for the adaptive hypothesis that interspecific competition drove the evolution of female dewlaps. However, we did find support for the pleiotropy hypothesis as species with larger females and reduced sexual size dimorphism were more likely to possess female dewlaps. Lastly, we found that female dewlap presence influenced diversification rates in anoles, but only secondarily to a hidden state. Our results demonstrate that female ornamentation is widespread in anoles and the traditional hypothesis of divergent selection between the sexes does not fully explain their evolution. Instead, female ornamentation is likely to be subject to complex adaptive and non-adaptive evolutionary forces.
OBSERVATIONS OF THE M82 SN 2014J WITH THE KILODEGREE EXTREMELY LITTLE TELESCOPE

We report observations of the bright M82 supernova 2014J serendipitously obtained with the Kilodegree Extremely Little Telescope (KELT). The supernova (SN) was observed at high cadence for over 100 days, from pre-explosion, to early rise and peak times, through the secondary bump. The high cadence KELT data with high signal-to-noise ratio is completely unique for SN 2014J and for any other SNIa, with the exception of the (yet) unpublished Kepler data. Here, we report determinations of the SN explosion time and peak time. We also report measures of the “smoothness” of the light curve on timescales of minutes/hours never before probed, and we use this to place limits on energy produced from short-lived isotopes or inhomogeneities in the explosion or the circumstellar medium. From the non-observation of significant perturbations of the light curves, we derive a 3σ upper limit corresponding to 8.7 × 1036 erg s−1 for any such extra sources of luminosity at optical wavelengths.
Functional space expansion driven by transitions between energetically advantageous traits in the deep sea

Climate change is shifting community structure and biodiversity on a global scale, in part due to alterations of chemical and thermal energy availability. These changes may impact ecosystem functioning through their influence on functional diversity. We investigate patterns of functional diversity, functional niches, and functional traits in bivalve communities across the energetic gradient of the deep Atlantic Ocean. We use the functional traits feeding type, tiering, and motility level to define the axes of functional space and the unique combinations of these traits as functional niches. We find that increased energy affords new species, added into functional space through niche expansion rather than niche packing. Underlying this pattern are complex dynamics of gains and losses of individual functional niches, with few adapted to the low- and high-energy extremes, and most occurring at intermediate energy. Adaptive qualities of specific traits are evidenced by those functional niches occurring at energetic extremes. Tradeoffs between these traits within the intermediate energy zone underlie an increased coexistence of functional niches, which in turn drives a unimodal pattern of functional niches and expansion of used functional space. This work suggests that energy-limited communities may be especially vulnerable to continued shifts in food availability through the Anthropocene.
Resonant plasmonic terahertz detection in graphene split-gate field-effect transistors with lateral p–n junctions

We evaluate the proposed resonant terahertz (THz) detectors on the basis of field-effect transistors (FETs) with split gates, electrically induced lateral p–n junctions, uniform graphene layer (GL) or perforated (in the p–n junction depletion region) graphene layer (PGL) channel. The perforated depletion region forms an array of the nanoconstions or nanoribbons creating the barriers for the holes and electrons. The operation of the GL-FET- and PGL-FET-detectors is associated with the rectification of the ac current across the lateral p–n junction enhanced by the excitation of bound plasmonic oscillations in the p- and n-sections of the channel. Using the developed device model, we find the GL-FET- and PGL-FET-detector characteristics. These detectors can exhibit very high voltage responsivity at the THz radiation frequencies close to the frequencies of the plasmonic resonances. These frequencies can be effectively voltage tuned. We show that in PL-FET-detectors the dominant mechanism of the current rectification is due to the tunneling nonlinearity, whereas in the PGL-FET-detector the current rectification is primarily associated with the thermionic processes. Due to much lower p–n junction conductance in the PGL-FET-detectors, their resonant response can be substantially more pronounced than in the GL-FET-detectors corresponding to fairly high detector responsivity.
Characterizing Strongly First Order Dependencies: The Non-Jumping Relativizable Case

Team Semantics generalizes Tarski's Semantics for First Order Logic by allowing formulas to be satisfied or not satisfied by sets of assignments rather than by single assignments. Because of this, in Team Semantics it is possible to extend the language of First Order Logic via new types of atomic formulas that express dependencies between different assignments. Some of these extensions are much more expressive than First Order Logic proper; but the problem of which atoms can instead be added to First Order Logic without increasing its expressive power is still unsolved. In this work, I provide an answer to this question under the additional assumptions (true of most atoms studied so far) that the dependency atoms are relativizable and non-jumping. Furthermore, I show that the global (or Boolean) disjunction connective can be added to any strongly first order family of dependencies without increasing the expressive power, but that the same is not true in general for not strongly first order dependencies.
Heavy nuclei synthesized in Gamma-Ray Burst outflows as the source of UHECRs

Recent measurements by the Pierre Auger Observatory suggest that the composition of ultra-high energy cosmic rays (UHECRs) becomes dominated by heavy nuclei at high energies. However, until now there has been no astrophysical motivation for considering a source highly enriched in heavy elements. Here we demonstrate that the outflows from Gamma-Ray Bursts (GRBs) may indeed be composed primarily of nuclei with masses A�40− 200, which are synthesized as hot material expands away from the central engine. In particular, if the jet is magnetically-dominated (rather than a thermally-dr iven fireball) its low entropy enables heavy elements to form effi ciently. Adopting the millisecond proto-magnetar model for the GRB central engine, we show that heavy nuclei are both synthesized in proto-magnetar winds and can in principle be accelerated to energies � 10 20 eV in the shocks or regions of magnetic reconnection that are responsible for powering the GRB. Similar results may apply to accretion-powered GRB models if the jet originates from a magnetized disk wind. Depending on the precise distribution of nuclei synthesized, we predi ct that the average primary mass may continue to increase beyond Fe group elements at the highest energies, possibly reaching the A �90 (Zirconium), A �130 (Tellurium), or even A �195 (Platinum) peaks. Future measurements of the UHECR composition at energies � 10 20 eV can thus confirm or constrain our model and, potentially, probe the nature of GRB outflows. The longer attenuation length of ultra-heavy nuclei through the extragalactic bac kground light greatly expands the volume of accesible sources and alleviates the energetic constraints on GRBs as the source of UHECRs.
Limits on dark matter from AMS-02 antiproton and positron fraction data

We derive limits on the dark matter annihilation cross section and lifetime using measurements of the AMS-02 antiproton ratio and positron fraction data. In deriving the limits, we consider the scenario of secondary particles accelerated in supernova remnants (SNRs) which has been argued to be able to reasonably account for the AMS-02 high energy positron/antiproton fraction data. We parameterize the contribution of secondary particles accelerated in SNRs and then fit the observational data within the conventional cosmic ray propagation model by adopting the GALPROP code. We use the likelihood ratio test to determine the 95$\%$ confidence level upper limits of the possible dark matter (DM) contribution to the antiproton/positron fractions measured by AMS-02. Our limits are stronger than that set by the Fermi-LAT gamma-ray Pass 8 data of the dwarf spheroidal satellite galaxies. We also show that the solar modulation (cosmic ray propagation) parameters can play a non-negligible role in modifying the constraints on the dark matter annihilation cross section and lifetime for $m_\chi 100$ GeV), where $m_\chi$ is the rest mass of the dark matter particles. Using this results, we also put limits on the effective field theory of dark matter.
Positional Encoding to Control Output Sequence Length

Neural encoder-decoder models have been successful in natural language generation tasks. However, real applications of abstractive summarization must consider an additional constraint that a generated summary should not exceed a desired length. In this paper, we propose a simple but effective extension of a sinusoidal positional encoding (Vaswani et al., 2017) so that a neural encoder-decoder model preserves the length constraint. Unlike previous studies that learn length embeddings, the proposed method can generate a text of any length even if the target length is unseen in training data. The experimental results show that the proposed method is able not only to control generation length but also improve ROUGE scores.
Industry goes metaverse – the fusion of real and virtual industrial worlds exemplified by the wastewater industry

Abstract Meeting friends to go shopping, visit a concert or do some sports together – all this and more is now “virtually” possible in the Metaverse. Currently, the creative industries have made the most of the potential of virtual worlds. But the metaverse has no limits. The newly created dynamics raise the question of the extent to which the manufacturing industry can benefit from the potential of these virtual worlds. So far, augmented reality (AR) has been recognized as being useful to the manufacturing industry. The combination of virtual reality (VR) and augmented reality (AR) now unleashes new opportunities for industrial companies. For example, by making a virtual inspection of unmanned facilities possible. A project which has been put into practice by the Entsorgungsverband Saar (EVS) in cooperation with the August-Wilhelm Scheer Institute.
VECTOR MESON DOMINANCE AND RADIATIVE DECAYS OF HEAVY SPIN-3/2 BARYONS TO HEAVY SPIN-1/2 BARYONS

Using the calculated values of the strong coupling constants of the heavy sextet spin-3/2 baryons to sextet and antitriplet heavy spin-1/2 baryons with light vector mesons within the light cone QCD sum rules method, and vector meson dominance assumption, the radiative decay widths are calculated. These widths are compared with the "direct" radiative decay widths predicted in the framework of the light cone QCD sum rules.
Machine learning algorithms applied to telemetry data of SCD-2 brazilian satellite

With the advancement of information technologies, big data and data storage and processing capacity, there is an increase in studies on machine learning in different contexts, especially in the spatial context. Specifically talking about artificial satellites, machine learning algorithms can be applied for diferent purposes, for example to identify the satellite's operating conditions and to predict undesirable situations. The work calculated the performance of six supervised machine learning algorithms in the analysis of telemetry data from the Brazilian satellite SCD2. Five experiments were performed for each supervised machine learning algorithm. To evaluate the algorithms, the following metrics were used: mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The Support Vector Machine (SVM) and Bagging Regressor (BR) algorithms obtained better results in the evaluation.
Sensing overlapping geospatial communities from human movements using graph affiliation generation models

Geographical units densely connected by human movements can be treated as a geospatial community. Detecting geospatial communities in a mobility network reveals key characteristics of human movements and urban structures. Recent studies have found communities can be overlapping in that one location may belong to multiple communities, posing great challenges to classic disjoint community detection methods that only identify single-affiliation relationships. In this work, we propose a Geospatial Overlapping Community Detection (GOCD) framework based on graph generation models and graph-based deep learning. GOCD aims to detect geographically overlapped communities regarding the multiplex connections underlying human movements, including weak and long-range ties. The detection process is formalized as deriving the optimized probability distribution of geographic units' community affiliations in order to generate the spatial network, i.e., the most reasonable community affiliation matrix given the observed network structure. Further, a graph convolutional network (GCN) is introduced to approach the affiliation probabilities via a deep learning strategy. The GOCD framework outperformed existing baselines on non-spatial benchmark datasets in terms of accuracy and speed. A case study of mobile positioning data in the Twin Cities Metropolitan Area (TCMA), Minnesota, was presented to validate our model on real-world human mobility networks. Our empirical results unveiled the overlapping spatial structures of communities, the overlapping intensity for each CBG, and the spatial heterogeneous structure of community affiliations in the Twin Cities.
Boosting the sensing granularity of acoustic signals by exploiting hardware non-linearity

Acoustic sensing is a new sensing modality that senses the contexts of human targets and our surroundings using acoustic signals. It becomes a hot topic in both academia and industry owing to its finer sensing granularity and the wide availability of microphone and speaker on commodity devices. While prior studies focused on addressing well-known challenges such as increasing the limited sensing range and enabling multi-target sensing, we propose a novel scheme to leverage the non-linearity distortion of microphones to further boost the sensing granularity. Specifically, we observe the existence of the non-linear signal generated by the direct path signal and target reflection signal. We mathematically show that the non-linear chirp signal amplifies the phase variations and this property can be utilized to improve the granularity of acoustic sensing. Experiment results show that, by properly leveraging the hardware non-linearity, the amplitude estimation error for sub-millimeter-level vibration can be reduced from 0.137 mm to 0.029 mm.
Gauss-Bonnet modified gravity models with bouncing behavior

The following issue is addressed: how the addition of a Gauss-Bonnet term (generically coming from most fundamental theories, as string and M theories), to a viable model, can change the specific properties, and even the physical nature, of the corresponding cosmological solutions? Specifically, brand new original dark energy models are obtained in this way with quite interesting properties, which exhibit, in a unified fashion, the three distinguished possible cosmological phases corresponding to phantom matter, quintessence, and ordinary matter, respectively. A model, in which the equation of state parameter, $w$, is a function of time, is seen to lead either to a singularity of the Big Rip kind or to a bouncing solution which evolves into a de Sitter universe with $w=-1$. Moreover, new Gauss-Bonnet modified gravity models with bouncing behavior in the early stages of the universe evolution are obtained and tested for the validity and stability of the corresponding solutions. They allow for a remarkably natural, unified description of a bouncing behavior at early times and accelerated expansion at present.
Push, Tap, Dwell, and Pinch: Evaluation of Four Mid-air Selection Methods Augmented with Ultrasonic Haptic Feedback

This work compares four mid-air target selection methods (Push, Tap, Dwell, Pinch) with two types of ultrasonic haptic feedback (Select, HoverSelect) in a Fitts’ law experiment. Results revealed that Tap is the fastest, the most accurate, and one of the least physically and cognitively demanding selection methods. Pinch is relatively fast but error prone and physically and cognitively demanding. Dwell is slowest by design, yet the most accurate and the least physically and cognitively demanding. Both haptic feedback methods improve selection performance by increasing users’ spatial awareness. Particularly, Push augmented with Hover & Select feedback is comparable to Tap. Besides, participants perceive the selection methods as faster, more accurate, and more physically and cognitively comfortable with the haptic feedback methods.