Exploring the surface properties of Transneptunian Objects and Centaurs with polarimetric FORS1/VLT observations

Polarization is a powerful remote-sensing method to investigate solar system bodies. It is an especially sensitive diagnostic tool to reveal physical properties of the bodies whose observational characteristics are governed by small scatterers (dust, regolith surfaces). For these objects, at small phase angles, a negative polarization is observed, i.e., the electric vector E oscillates predominantly in the scattering plane, contrary to what is typical for rather smooth homogeneous surfaces. The behavior of negative polarization with phase angle depends on the size, composition and packing of the scatterers. These characteristics can be unveiled by modelling the light scattering by the dust or regolith in terms of the coherent backscattering mechanism. We have investigated the surface properties of TNOs and Centaurs by means of polarimetric observations with FORS1 of the ESO VLT. TNOs Ixion and Quaoar, and Centaur Chiron show a negative polarization surge. The Centaur Chiron has the deepest polarization minimum (-1.5 - 1.4%). The two TNOs show differing polarization curves: for Ixion, the negative polarization increases rapidly with phase; for Quaoar, the polarization is relatively small (~ -0.6%), and nearly constant at the observed phase angles. For all three objects, modelling results suggest that the surface contains an areal mixture of at least two components with different single-scatterer albedos and photon mean-free paths.Comment: 11 pages, 7 postscript figures, accepted by A&A; astro-ph abstract has been replaced with a more complete on

Uptake of groundwater nitrogen by a near-shore coral reef community on Bermuda

Nutrient enrichment can slow growth, enhance bioerosion rates, and intensify algal competition for reef-building corals. In areas of high human population density and/or limited waste management, submarine groundwater discharge can transfer anthropogenic nutrients from polluted groundwater to coastal reefs. In this case study, we investigate the impact of submarine groundwater discharge on a near-shore reef in Bermuda, where over 60% of sewage generated by the island’s 64,000 residents enters the groundwater through untreated cesspits. Temperature, salinity, pH, and alkalinity were monitored at a groundwater discharge vent, three locations across the adjacent coral reef (0–30 m from shore), and a comparison patch reef site 2 km from shore. Groundwater discharge was characterized by low salinity, low aragonite saturation state (Ω_(ar)), high alkalinity, elevated nitrate + nitrite (NO₃₋ + NO₂₋; hereafter, “NO₃₋”) concentrations (> 400 µM), and an elevated ¹⁵N/¹⁴N ratio of NO₃₋ (δ¹⁵N = 10.9 ± 0.02‰ vs. air, mean ± SD). Rainfall and tidal cycles strongly impacted groundwater discharge, with maximum discharge during low tide. NO₃₋ concentrations on the near-shore reef averaged 4 µM, ten times higher than that found at the control site 2 km away, and elevated NO₃₋ δ¹⁵N at the near-shore reef indicated sewage-contaminated groundwater as a significant nitrogen source. Tissue δ¹⁵N of Porites astreoides, a dominant reef-building coral, was elevated by ~ 3‰ on the near-shore reef compared to the control site, indicating that corals across the near-shore reef were assimilating groundwater-derived nitrogen. In addition, coral skeletal density and calcification rates across the near-shore reef were inversely correlated with NO₃₋ concentration and δ¹⁵N, indicating a negative coral health response to groundwater-borne nutrient inputs. P. astreoides bioerosion rates, in contrast, did not show an effect from the groundwater input

Video-Based Autism Detection with Deep Learning

Individuals with Autism Spectrum Disorder (ASD) often experience challenges in health, communication, and sensory processing; therefore, early diagnosis is necessary for proper treatment and care. In this work, we consider the problem of detecting or classifying ASD children to aid medical professionals in early diagnosis. We develop a deep learning model that analyzes video clips of children reacting to sensory stimuli, with the intent of capturing key differences in reactions and behavior between ASD and non-ASD participants. Unlike many recent studies in ASD classification with MRI data, which require expensive specialized equipment, our method utilizes a powerful but relatively affordable GPU, a standard computer setup, and a video camera for inference. Results show that our model effectively generalizes and understands key differences in the distinct movements of the children. It is noteworthy that our model exhibits successful classification performance despite the limited amount of data for a deep learning problem and limited temporal information available for learning, even with the motion artifacts.Comment: Poster Abstract. Accepted into 2024 IEEE Green Technologies Conferenc

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Effects of vermicompost on the growth and yield of spring onion (Allium fistulosum L.)

Spring onion (Allium fistulosum L.) is a popular salad vegetable produced widely over the world, including in Vietnam. Thanks to its flavor and aroma, it is an indispensable ingredient used to flavor soups and other dishes. Vermicompost is a natural and environmentally friendly fertilizer used widely to increase crop production and maintain the sustainability of agrosystems. Consequently, this study was conducted to investigate the efficiency of vermicompost at different application rates in promoting the growth and yield parameters of spring onion. The results show that adding vermicompost to spring onion production had significant positive effects on plant height, number of leaves, number of tillers, individual plant weight, and plot yield. Particularly, the application of vermicompost at 40 t ha-1 showed the highest performance in the observed parameters, increasing the number of leaves, number of tillers, individual plant weight, and plot yields to 64.78, 21.18, 302.96 g plant-1, and 4.86 kg m-2, respectively. The plot yields in the treatments of the highest and lowest vermicompost application increased by 49.1% and 3.9%, respectively, in comparison to the control. Consequently, there was a strongly positive relationship between the application rate of vermicompost and the plot yield

Effect of spent coffee grounds and liquid worm fertilizer on the growth and yield of Brassica campestris L.

Brassica campestris L. plants are widely grown, including in Asian countries where the leaves are used to prepare Chinese sour pickled mustard greens. The potential benefits of the application of organic by-products and organic fertilizers in sustainable agricultural production have been shown in previous studies. Consequently, this study investigated the effectiveness of liquid worm fertilizer (LWF) and spent coffee grounds (SCG) individually and in combination on the growth of B. campestris. The results showed that LWF at the highest dose had positive effects on the growth and yield of B. campestris, but SGC had inhibitory effects. The treatment consisting of composted SCG + triple of the standard dose of LWF resulted in the best plot yield with 3,866.7 g.plot-1, followed by the treatment of fresh SCG + triple of the standard dose of LWF, which produced a yield of 3,766.7 g.plot-1. The lowest yield (2,100.0 g.plot-1) was observed in the treatment of 1 kg.m-2 fresh SCG + no LWF. The interaction effect between SCG and LWF on the plot yield of B. campestris L. was significant (F(4,18) = 4.6; p = 0.01) demonstrating enhanced yield when both SCG and LWF were used in combination

Bit-Vector Model Counting using Statistical Estimation

Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms