Miracle-dz/UniFAR-FTUs huggingface dataset:
Mercury's Circumsolar Dust Ring as an Imprint of a Recent Impact

A circumsolar dust ring has been recently discovered close to the orbit of Mercury. There are currently no hypotheses for the origin of this ring in the literature, so we explore four different origin scenarios here: the dust originated from (1) the sporadic meteoroid complex that comprises the major portion of the Zodiacal Cloud, (2) recent asteroidal/cometary activity, (3) hypothetical dust-generating bodies locked in mean-motion resonances beyond Mercury, and (4) bodies co-orbiting with Mercury. We find that only scenario (4) reproduces the observed structure and location of Mercury’s dust ring. However, the lifetimes of Mercury’s co-orbitals (<20 Ma) preclude a primordial origin of the co-orbiting source population due to dynamical instabilities and meteoroid bombardment, demanding a recent event feeding the observed dust ring. We find that an impact on Mercury can eject debris into the co-orbital resonance. We estimate the ages of six candidate impacts that formed craters larger than 40 km in diameter using high-resolution spacecraft data from MESSENGER and find two craters with estimated surface ages younger than 50 Ma. We find that the amount of mass transported from Mercury’s surface into the co-orbital resonance from these two impacts is several orders of magnitude smaller than what is needed to explain the magnitude of Mercury’s ring inferred from remote sensing. Therefore we suggest that numerous younger, smaller impacts collectively contributed to the origin of the ring. We conclude that the recent impact hypothesis for the origin of Mercury’s dust ring is a viable scenario, whose validity can be constrained by future inner solar system missions.
Simultaneous x-ray and radio observations of GX 5-1

Simultaneous X-ray (Ginga) and radio (VLA) observations of the Z-type low-mass X-ray binary GX 5-1 were made during 1989 September 1-6. The radio flux of GX 5-1 was less than 1 mJy when the X-ray source was in the horizontal branch. In the normal branch, the radio flux increased up to ∼6 mJy, and its variations were anticorrelated with those of the X-ray flux during time intervals of tens of minutes. This radio behavior is opposite to the X-ray/radio correlation found before in the Z sources GX 17+2, Cyg X-2, and Sco X-1
Smooth non‐parametric estimation of the distribution function from balanced ranked set samples

In certain sampling schemes, such as ranked set sampling, the observed data set can consist entirely of independent order statistics. A balanced ranked set sample consists of n independent cycles, in each of which the experimenter selects k random samples, each of size k. The recorded observation in sample r of cycle i is the rth order statistic, X(r). Stokes and Sager (1988) have shown that the empirical distribution function based on such samples is an unbiased estimate of the underlying distribution function and is more precise than the empirical distribution function calculated from a simple random sample of the same size. In this article, the properties of the empirical distribution function developed by Stokes and Sager (1988) are further investigated. In addition, a kernel‐type estimator of the distribution function is developed. For large n, the smooth estimator is shown to be consistent and asymptotically normal. Finally, small sample properties of the two estimators are investigated and compared via computer simulations. Copyright © 2004 John Wiley & Sons, Ltd.
An in silico model to study the impact of carbonic anhydrase IX expression on tumour growth and anti-PD-1 therapy

Immune checkpoint inhibitors (ICIs) are revolutionary cancer treatments. However, the mechanisms behind their effectiveness are not yet fully understood. Here, we aimed to investigate the role of the pH-regulatory enzyme carbonic anhydrase IX (CAIX) in ICI success. Consequently, we developed an in silico model of the tumour microenvironment. The hybrid model consists of an agent-based model of tumour–immune cell interactions, coupled with a set of diffusion-reaction equations describing substances in the environment. It is calibrated with data from the literature, enabling the study of its qualitative behaviour. In our model, CAIX-expressing tumours acidified their neighbourhood, thereby reducing immune infiltration by 90% (p < 0.001) and resulting in a 25% increase in tumour burden (p < 0.001). Moreover, suppression of CAIX improved the response to anti-PD-1 (23% tumour reduction in CAIX knockouts and 6% in CAIX-expressing tumours, p < 0.001), independently of initial PD-L1 expression. Our simulations suggest that patients with CAIX-expressing tumours could respond favourably to combining ICIs with CAIX suppression, even in the absence of pre-treatment PD-L1 expression. Furthermore, when calibrated with tumour-type-specific data, our model could serve as a high-throughput tool for testing the effectiveness of such a combinatorial approach.
Environmental control of social goals: using Pavlovian-to-instrumental transfer to test cue-based pro-self and pro-social outcome responses

A large amount of literature demonstrates that social behaviour can be triggered by environmental cues. A long-standing debate involves the question of whether such stimuli trigger behaviour directly (i.e. habits) or whether these effects mediate goals. As studies on automatic goal pursuit typically use real-world cues that are already associated with the behaviour and potentially the goal, it is impossible to make strong claims about the nature of the effects. In the present paper, we use a paradigm inspired by the Pavlovian-to-instrumental transfer (PIT) literature to examine how the environment can trigger goal-directed behaviour. Building on the essence of pro-self and pro-social motives in humans, two experiments explored the PIT effect when the outcomes were framed in terms of self- versus other-interest. Participants performed actions to earn money for themselves or a charity. Each outcome was linked to a different cue. The results showed that a cue predictive of self-interest outcomes facilitated responses instrumental in gaining the outcome, while such specific PIT effect for other-interest outcomes only emerged when participants were free to donate the money. We briefly discuss these findings reflecting on whether the PIT effect in our paradigm is indeed sensitive to the value of social goals.
Genetic evidence for widespread population size expansion in North American boreal birds prior to the Last Glacial Maximum

Pleistocene climate cycles are well documented to have shaped contemporary species distributions and genetic diversity. Northward range expansions in response to deglaciation following the Last Glacial Maximum (LGM; approximately 21 000 years ago) are surmised to have led to population size expansions in terrestrial taxa and changes in seasonal migratory behaviour. Recent findings, however, suggest that some northern temperate populations may have been more stable than expected through the LGM. We modelled the demographic history of 19 co-distributed boreal-breeding North American bird species from full mitochondrial gene sets and species-specific molecular rates. We used these demographic reconstructions to test how species with different migratory strategies were affected by glacial cycles. Our results suggest that effective population sizes increased in response to Pleistocene deglaciation earlier than the LGM, whereas genetic diversity was maintained throughout the LGM despite shifts in geographical range. We conclude that glacial cycles prior to the LGM have most strongly shaped contemporary genetic diversity in these species. We did not find a relationship between historic population dynamics and migratory strategy, contributing to growing evidence that major switches in migratory strategy during the LGM are unnecessary to explain contemporary migratory patterns.
Objective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data

AbstractAn objective method for estimating tropical cyclone (TC) intensity using historical hurricane satellite data (HURSAT) is developed and tested. This new method, referred to as feature analogs in satellite imagery (FASI), requires a TC's center location to extract azimuthal brightness temperature (BT) profiles from current imagery as well as BT profiles from imagery 6, 12, and 24 h prior. Instead of using regression techniques, the estimated TC intensity is determined from the 10 closest analogs to this TC based on the BT profiles using a k-nearest-neighbor algorithm. The FASI technique was trained and validated using intensity data from aircraft reconnaissance in the North Atlantic Ocean, where the data were restricted to include storms that are over water and south of 45°N. This subset comprised 2016 observations from 165 storms during 1988–2006. Several tests were implemented to statistically justify the FASI algorithm using n-fold cross validation. The resulting average mean absolute intensity e.
The great solar energetic particle events of 1989 observed from geosynchronous orbit

Los Alamos energetic proton instruments at geosynchronous orbit observed more major solar energetic particle events during 1989 than any other year since this series of detectors began observations in 1976. The temporal flux profiles of four intervals, which contain six distinct events, are compared illustrating the uniqueness of each event. Characteristic risetime and decay time are computed for each event. During two of these events, brief order-of-magnitude increases of the proton flux are observed. They are associated with sudden commencement events and dramatic changes in the solar wind. We conclude that these two brief events are likely the result of shock acceleration in the solar wind. We have fit the measured count rates to a spectral form which is exponential in rigidity, and we have examined the changes in spectral slope with time for each of the four intervals. In general, harder spectra are measured near the onset of an event followed by a softening of the spectrum as the fluxes decay. We have also investigated the effects of these events on geomagnetic activity by comparing the fluxes of >30-keV electrons at geosynchronous orbit and Kp geomagnetic index during the early part of two of the solar energetic particle events.
A Quarter Century of Monitoring the Fish Assemblages of San Diego Bay, California from 1995 to 2019

Abstract. A time series of 22 samples collected in April and July were taken of the fish assemblages of San Diego Bay over 11 non-consecutive years from 1995 to 2019. Each sample consisted of four ecoregions using a variety of collection methods designed to assess all components of the ichthyofauna. These samples yielded a total of 525,288 fishes belonging to 90 species and weighing 3,507 kg over the 25-year period. Northern anchovy was the most abundant fish species comprising 41% of the total catch despite its virtual absence near the end of the survey period, followed by topsmelt, slough anchovy, shiner perch, and Pacific sardine. Round stingrays dominated in weight constituting more than 27% of the total biomass taken followed by spotted sand bass, and northern anchovy. Approximately 64% of all individual fish captured in San Diego Bay during this study were juveniles. In a canonical correlation analysis, temperature, distance from the mouth of the bay, and salinity accounted for nearly 93% of the variance in individual species abundances. In the time series analyses, we found all three potential temporal patterns of fish species abundance, biomass, and diversity, namely: 1) significant decreases over time, 2) significant increases over time, and 3) no significant change over time. Abundance of eight of the top 35 species (including northern anchovy, topsmelt, slough anchovy, and shiner perch) and all forage species combined decreased over the study. Two species increased significantly in abundance, spotted sand bass and dwarf perch. Whereas, total abundance, total biomass, species richness, Shannon diversity, and the majority (71%) of species abundances did not change over the 25-year period. Despite various environmental perturbations and the general trends of decreases in larval and fish abundance indices over the Southern California Bight in recent years, the stability in species richness and composition over time reflects the generally resilient nature of the fish assemblage structure of San Diego Bay that has been maintained by active management including restoration practices.
Analytic approximation for charge current and deposition by 0.1 to 100 MeV electrons in thick slabs

An analytic function is developed for approximation of current penetration and charge deposition as a function of depth with normal incidence high energy electrons on slabs. The slabs must be thicker than the electron penetration depth and be composed of one material. Six adjustable parameters are evaluated to fit the function to tabulated ITS monte carlo and experimental electron deposition data. The results were developed from data spanning the entire energy range in C, Al, Cu, Ag, and Au, but are assumed to apply to all atomic number materials at all energies in the range.
Stellar populations in the edge-on spiral galaxy NGC 4565. I: Surface brightness and color distributions

Results are presented for photographic and photoelectric photometry of the edge-on Sb galaxy NGC 4565. Major-axis, minor-axis, and perpendicular surface brightness profiles are determined, along with color gradients parallel and perpendicular to the plane of the galaxy. It is found that the galaxy's light can be deconvolved naturally into five components: (1) a starlike nucleus located at the center of the bulge and the edge of the dust lane; (2) a thin disk containing the gas and dust, the young OB stars, and the spiral arms; (3) a thick disk that may be a locally isothermal sheet of old Population I metal-rich stars; (4) a bulge that merges smoothly into the thick disk at a radial distance of about 2.9 kpc; and (5) a corona whose light dominates the perpendicular profiles from about 2.9 to at least 8.2 kpc above the galactic plane.
JACOBIAN IDEALS AND THE NEWTON NON-DEGENERACY CONDITION

Abstract In this paper we extract some conclusions about Newton non-degenerate ideals and the computation of Łojasiewicz exponents relative to this kind of ideal. This motivates us to study the Newton non-degeneracy condition on the Jacobian ideal of a given analytic function germ $f:(\mathbb{C}^n,0)\to(\mathbb{C},0)$. In particular, we establish a connection between Newton non-degenerate functions and functions whose Jacobian ideal is Newton non-degenerate. AMS 2000 Mathematics subject classification: Primary 32S05. Secondary 57R45
Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia

Abstract Common bean is among the vital legumes cultivated for nutrition, foreign currency earnings, and income generation. Common bean production is constrained mainly by nutrient deficiencies. A field experiment was conducted at Sodo Zuria Woreda to assess the effects of blended N–P–S–B fertilizer rates and Rhizobium strain on yield and yield traits of common bean and income of farmers. Treatments were five different rates of blended NPSB fertilizer (0, 50, 100, 150, and 200 kg ha−1) and two types of Rhizobium strains (HB-A15 and HB-429 (Rhizobia etli)) and control (without inoculation). Randomized complete block design in factorial arrangement with three replications were used. Results showed that interaction of blended NPSB rate and Rhizobium strain type had significant effect on the number of effective nodule, grain yield, and above ground dry biomass. Application of 150 kg NPSB ha−1 along with inoculation of Rhizobium etli resulted in the highest grain yield (3017.7 tons ha−1). Similarly, economic analysis indicated that the highest net return of 38298.545 ETB ha−1 was obtained from combined application of 150 kg NPSB ha−1 along with Rhizobium etli. Thus, it could be concluded that using Rhizobium etli strain with 150 kg NPSB ha−1 was found to be appropriate for common bean production in the area.
Real-Time Tracking of Smartwatch Orientation and Location by Multitask Learning

In this demo, we present RTAT, a real-time arm tracking system that tracks both orientation and location of a smartwatch simultaneously by a multitask learning neural network. We incorporate an attention layer and design a dedicated loss for the multitask neural network to learn the dynamic relationships among Inertial Measurement Unit (IMU) sensors. RTAT supports real-time tracking by performing deep learning inference on a smartphone. Finally, to train RTAT, we develop an easy-to-use labeled data collection system that uses a low-cost virtual reality system to measure the ground truth orientation and location of the smartwatch. Extensive experiments show RTAT outperforms significantly the state-of-the-art solutions in inference accuracy and latency.
LoRa Based Linear Network Applications, Design Considerations and Open Challenges: A Review

Wireless sensor networks (WSNs) have emerged as an increasingly important technology with wide application in fields such as industrial automation, transportation, smart cities, and similar areas. LoRa based LPWAN provides long range, robust, economical, and reliable communication with high link budget compared to other LPWAN technologies. However, LoRa based networks are usually deployed using a star topology (LoRaWAN). This type of network topology is not suitable for long range wireless communication and is also subject to single point of failure at the LoRaWAN gateway. In this study we evaluate the potential of LoRa as links in linear networks. We have addressed the potential applications, design considerations and current challenges in adaptation of LoRa technology for linear networks. This survey presents a detailed analysis of LoRa technology as a linear network and provides recommendations to researchers and practitioners to explore LoRa technology as a linear network technology for various IoT applications.
SpeechQoE: A Novel Personalized QoE Assessment Model for Voice Services via Speech Sensing

Quality of Experience (QoE) assessment is a long-lasting but yet-to-be-resolved task. Existing approaches, especially for conversational voice services, are restricted to leveraging network-centric parameters. However, their performances are hardly satisfactory due to the failure to consider comprehensive QoE-related factors. Moreover, they develop a one-for-all model that is uniform for all individuals and thus incapable of handling user diversity in QoE perception. This paper proposes a personalized QoE assessment model, namely SpeechQoE. It exploits speaker's speech signals to infer individual's perceived quality in voice services. SpeechQoE fundamentally addresses the drawback of conventional models. Instead of enumerating and incorporating unlimited QoE-related factors, SpeechQoE takes as input speech signals that inherently bear rich information needed for QoE assessment of the speaker. SpeechQoE employs an efficient few-shot learning framework to adapt the model to a new user quickly. We additionally design a lightweight data synthetic scheme to minimize the overhead of data collection needed for model adaption. A modular integration with a conventional parametric model is further implemented to avoid issues caused by the clean-slate data-driven approach. Our experiments show that SpeechQoE achieves an accuracy of 91.4% in QoE assessment which outperforms the state-of-the-art solutions by a clear margin. As another contribution of this work, we build a dataset that would be the first source of annotated audio tracks for QoE assessment of conversational calls.
Wi-Mesh: A WiFi Vision-Based Approach for 3D Human Mesh Construction

In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system. Our system leverages the advances of WiFi to visualize the shape and deformations of the human body for 3D mesh construction. In particular, it leverages multiple transmitting and receiving antennas on WiFi devices to estimate the two-dimensional angle of arrival (2D AoA) of the WiFi signal reflections to enable WiFi devices to "see" the physical environment as we humans do. It then extracts only the images of the human body from the physical environment, and leverages deep learning models to digitize the extracted human body into a 3D mesh representation. Experimental evaluation under various indoor environments shows that Wi-Mesh achieves an average vertices location error of 2.81cm and joint position error of 2.4cm, which is comparable to the systems that utilize specialized and dedicated hardware. The proposed system has the advantage of reusing the WiFi devices that already exist in the environment for potential mass adoption. It can also work in non-line of sight (NLoS), poor lighting conditions, and baggy clothes, where the camera-based systems do not work well.
Wireless Identification and Sensing Platform Version 6.0

Connected devices are becoming more ubiquitous, but powering them remains a challenge. The Wireless Identification and Sensing Platform (WISP) is a fully programmable device capable of energy harvesting and backscatter communication. It can accommodate a variety of sensing modalities and operate without batteries or a wired power supply, making it a suitable device for ubiquitous computing. A new version of WISP is presented. WISP-6.0 is designed to be low-power, modular, and enable dual energy harvesting from sources like a solar panel. Additionally, an upgraded cross-platform host application is built using the latest web technologies. Compared to its predecessor, WISP-5.1, WISP-6.0 consumes 13.62% and 6.29% less power in active accelerometer and active acknowledgment modes respectively. Furthermore, WISP-6.0 is better able to harvest RF energy collected from its antenna, with the greatest improvements at higher input powers.
BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN Inference

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, heterogeneous CPU and GPU resources must be efficiently utilized for the inference of multiple DNN models. Such a cross-processor real-time DNN inference paradigm poses major challenges due to the inherent performance imbalance among different processors and the lack of real-time support for cross-processor inference from existing deep learning frameworks. In this work, we propose a new system named BlastNet that exploits duo-block - a new model inference abstraction to support highly efficient cross-processor real-time DNN inference. Each duo-block has a dual model structure, enabling efficient fine-grained inference alternatively across different processors. BlastNet employs a novel block-level Neural Architecture Search (NAS) technique to generate duo-blocks, which accounts for computing characteristics and communication overhead. The duo-blocks are optimized at design time and then dynamically scheduled to achieve high resource utilization of heterogeneous CPU and GPU at runtime. BlastNet is implemented on an indoor autonomous driving platform and three popular edge platforms. Extensive results show that BlastNet achieves 35.07 % less deadline missing rate with a mere 1.63% of model accuracy loss.