Miracle-dz/UniFAR-FTUs huggingface dataset:
The stable water isotopes and snow accumulation from Weddell Sea sector imprint the large-scale atmospheric circulation variability

Stable water isotopes and accumulation data extracted from polar ice/firn cores provide valuable climate information. Here, we present novel isotopic and accumulation time series from an upstream area of the Möller Ice Stream (MIS) basin, Weddell Sea Sector (WSS), Antarctica – a Brazilian research area (84°00’00’’S; 79°29’39’’W; 1276 m a.s.l.). 15 Our purpose was to understand the depositional history and investigate how much the recent climate signal (21st century) is stored in the shallowest ice sheet layers in this area. Therefore, we crossed the isotopic (δ18O, δD, and d-excess) and snow accumulation data of two shallow firn cores (both ⁓9.0 m deep) with glaciological information, local and regional meteorological data (both ERA5 and AWS), indices of large-scale atmospheric modes (as SAM and ENSO) and the Amundsen Sea Low (ASL). The isotopic records cover 16 years (from 1999 to 2015-austral summer) and the accumulation 20 records cover 20 years (from 1999 to 2018). We find that interannual δs variability is strongly explained by changes in the phase of the SAM (r = 0.74; p < 0.05; α = 0.05) and, consequently, also by changes in pressure of both the WSS (r = -0.57; p < 0.05; α = 0.05) and the ASL (r = -0.56; p < 0.05; α = 0.05). The regional temperature in WSS (r = 0.50; p < 0.05; α = 0.05) and Antarctica Peninsula (r = 0.70; p < 0.05; α = 0.05), as well as the sea ice concentration in the Weddell Sea (r = -0.49; p = 0.05; α = 0.05) are other factors that measurably influence the δs in the study area. In the period covered by our study, the 25 rarest and strongest wind events (SWE; > ⁓15 m/s) and extreme precipitation events (EPE) oscillate almost completely out of phase, and this relationship is largely explained by the sub-decadal changes in the SWE-ENSO relationship and by the SAM variability. This oscillatory pattern between SWE and EPE justifies the non-temporally stable correlation between δs and local temperature in the study area. For the period of 2013-2018, we show that the trigger to start accumulating snow on the studied site is the occurrence of a range of EPE in a short time or of the one EPE with higher snowfall rates and that, the 30 low snowfall events are hardly ever preserved. Our snow accumulation composite record shows that the SWE-EPE seesaw governs the snow accumulation in the upstream area of the MIS basin in the 1999-2018 period. When the frequency of SWE increases and EPE decreases, the local snow accumulation increase. In contrast, in the opposite scenario, the accumulation approaches the forecast precipitation data indicating that the influence of blowing snow and wind drift decreases. Because of this relation, incredibly there was a significant decrease in snow accumulation in the study area in the 1999-2018 period due 35 https://doi.org/10.5194/tc-2022-161 Preprint. Discussion started: 26 September 2022 c © Author(s) 2022. CC BY 4.0 License.
Morphological characteristics and conditions of drainage basins contributing to the formation of debris flow fans: examination of regions with different rock strength using decision tree analysis

Debris flows cause severe disasters, such as human damage and the collapse of houses. Establishment of the early warning systems in the basins with high debris flow risks is needed to reduce debris flow disasters. Because debris flows often form debris flow fans near the mouths of valleys, debris flow fans are regarded as important topographical elements that indicate the occurrence of debris flows. The presence or absence of a debris flow fan makes it possible to clarify the morphological conditions of the contributing area that has generated debris flows. These morphological conditions may depend 15 on the rock strength, which controls weathering activity and grain size of sediments. In this study, we investigated the morphological conditions of a drainage basin that contribute to the formation of debris flow fans using decision tree analysis. The analysis was conducted at two sites with clearly differences in rock strength due to geological sedimentation processes: Neogene sedimentary rocks and Paleogene accretionary prism sites. As a result of decision tree analysis using data sets of total 158 basins, the thresholds of morphological parameters needed for forming debris flow fans differed depending on the 20 geological features. In the Paleogene accretionary prism site, when the relief ratio was less than 0.29, coarse-grained sediments were less likely to pass out from the valley, resulting in the absence of debris flow fans. On the other hand, in Neogene sedimentary rocks sites, short basins were determined to form debris flow fans, even if the relief ratio was less than 0.36 because the sediments were fine-grained and tended to flow downstream. In contrast, morphological factors that influence the presence or absence of debris flow fans were common at both sites. The first,
Advanced Methodology for Radiation Field Decomposition with Hybrid Pixel Detectors

Hybrid pixel detectors have gathered interest in the fields of high energy physics, medicine, and space science for their noiseless single particle processing capabilities. The presented work concentrates on hybrid detectors of the Timepix3 type, and their capability to identify and track particles of the entire radiation field. In the presented work, non-idealised datasets describing traces of ionising particles interacting in a pixel sensor were simulated and subsequently validated through experiment. Based on these datasets, novel algorithms for particle fluence, identification, and tracking were developed. For particle tracking, a Random Forest Regressor was developed, producing a mean absolute error of 8.65◦, and thus a 38% increase in accuracy over the next-best performing state-of-the-art algorithm. For particle classification, two novel algorithms were developed: one utilising Bayesian deconvolution and the other utilising machine learning with a combination of an XGBoosted classifier and Random Forest Regressor. The deconvolution and machine learning algorithms produced an electron/proton classification accuracy of 93.06% and 96.29%, respectively, and a total proton energy prediction accuracy of 54.65 MeV and 42.15 MeV , respectively. The developed algorithms were then tested in an experimental environment from proton beam exposure at the Danish Centre for Particle Therapy, from which consistent results were obtained. The algorithms were later applied to unknown complex radiation fields in the extra-terrestrial environment from the SATRAM detector, located on the European Space Agency satellite Proba-V in low Earth orbit, and at the MoEDAL experiment at the Large Hadron Collider located in CERN. The South Atlantic Anomaly was successfully isolated by analysing the data obtained from SATRAM, on which a Timepix detector was used. For the first time using Timepix detectors, following statistical manipulation, a physically reasonable proton spectrum was extracted. These results present the possibility of using Timepix-type detectors to develop models of the space environment in low Earth orbit. Regarding the experimental data obtained by the Timepix3 detector system in the MoEDAL experiment, the particle tracking algorithms allowed for the determination of the 3D-orientation of particles born at the point of collisions of opposing beams at the Large Hadron Collider to be θ = 56 ± 1◦ and φ = 139 ± 1◦ angles. At the same time, the interaction point was determined
Thundercloud structures detected and analyzed based on coherent Doppler wind lidar

Abstract. The studies of intracloud discharges may shed light on the microphysical structure of thunderclouds, as both the magnitude and the sign of charge separation due to graupel collides with ice crystals within the strong updrafts are influenced by the surrounding environment. Here, a compact all-fiber coherent Doppler wind lidar (CDWL) working at the 1.5 µm wavelength is applied for probing the dynamics and microphysics structure of thunderstorms. Thanks to the precise spectrum measurement, multi-component spectra signals of thunderstorms can be analyzed by the CDWL. The spectrum width, skewness, and Doppler velocity of CDWL is used to separate and identify the particle composition and polarity. In the experiment, the thundercloud properties are detected by the CDWL, 10.6 cm Doppler weather radar (DWR), and Advanced Geosynchronous Radiation Imager (AGRI) onboard Fengyun-4 satellites. In particular, the spectrum width and skewness of the thundercloud below the 0 ∘C isotherm are increased, and when a cloud-ground lightning occurs, there is additional graupel with a velocity greater than 5 m s−1. It indicates that this region is a melting layer, and lightning activity changes the motion characteristics of graupel, affecting the charge structure of the whole thundercloud. In general, our findings provide details on the velocity, phase, and composition of particles in the outside updraft region of the thunderstorm. The identification and analysis of graupel is particularly important. It is proved that the precise spectrum of CDWL is a promising indicator for studying the charge structure of thunderstorms.
3D-TTP: Efficient Transient Temperature-Aware Power Budgeting for 3D-Stacked Processor-Memory Systems

The heat produced during computation severely limits the performance of multi-/many-core processors. High-performance 3D-stacked processor-memory systems stack cores and main memory on a single die. However, 3D-stacked systems suffer more severe thermal issues than their non-stacked planar 2D counterparts. Consequently, the aggressive thermal throttling required for their thermally-safe operation limits the potential performance gains. Power budgeting is an effective thermal management technique that prevents thermal throttling in multi-/many-core processors by assigning a thermally-safe power budget to cores within the processors. State-of-the-art power budgeting techniques for 2D processors do not account for the vertical thermal coupling between the layers of the 3D-stacked system and will fail to prevent thermal throttling in them. Furthermore, estimating thermals for a 3D-stacked processor with power budgeting requires a finer-grained RC thermal model than non-stacked processors. This requirement inhibits the porting of existing power budgeting solutions for 2D processors to 3D-stacked processor-memory systems. This work is the first to present the linear algebra-based algorithmic time-invariant transformations required to enable power budgeting in 3D-stacked systems. Based on the transformations, we propose the first transient-temperature-aware power budgeting technique, 3D-TTP, for 3D-stacked systems. Detailed interval thermal simulations with the advanced CoMeT simulator designed for 3D-stacked systems also confirm no thermal violations with our 3D-TTP technique. 3D-TTP exhibits an average 11.41% speedup over the state-of-the-art reactive-based thermal management technique.
Effects of a commercial immersive virtual reality device-based training on gait and cognition in people with Parkinson's disease

Objective: Evaluate the effectiveness and the usability of an immersive virtual reality training using Samsung Gear VR OculusTM (SGVR) for gait and cognitive in people with Parkinson's disease (PD). Controlled, quasi-experimental and blinded clinical trial was carried out. Methods: Forty people with PD were divided into two groups. Samsung Gear Virtual Reality Group (VRG) participated in 10 sessions, twice a week, lasting one hour each, performing four games that required head movement and center of gravity shift. Control group didn't receive treatment. Participants were evaluated before, after the intervention and 30 days after intervention, with the Timed Up and Go test, 10 meters walking test, single and dual tasking 30 seconds walking test, verbal fluency test, forward and backward Digit Span tests and Stroop Color test. At the end of the training, VRG responded to the System Usability Scale questionnaire to assess the usability of the system. Results: Even though the system was rated with excellent usability by the users, there were no interaction effects and, therefore, our results do not support that immersive virtual reality training using SGVR games was superior to no training. However, when each group was examined separately, the VRG experienced improvements, after the intervention and 30 days after intervention, for gait velocity (p<0.005, p<0.001, respectively), working memory, attention, and information processing (p<0.01 in both evaluation time points), response inhibition, working and long-term memory (p< 0.01 in 30 days after intervention). Conclusion: Further evaluation of the SGVR device is required.
HANDLING INTERACTIVE SYSTEMS IN PRIMAL-DUAL FEEDBACK-OPTIMIZING CONTROL

This paper aims to solve a steady-state optimization problem with simple feedback controllers using the Primal-Dual approach. Compared to solving the full original optimization (standard real-time optimization (RTO)), this approach controls the constraints without the need of an explicit model, and has less computation time. The main idea is to control the constraints in an upper slow layer by manipulating the Dual variables (Lagrange multipliers). Given values of dual variables, the problem in the lower layer is unconstrained with the Lagrange function as the cost function, and the Primal variables (inputs) as decision variables. The solution can be obtained by solving the equation set that results when the gradient of the cost is equal to zero. It is possible to solve this equation set using decentralized feedback control for a weakly interactive system where the pairing of Primal variables and the gradient is obvious. This results in a decomposition of the optimization problem which may have significant advantages for practical implementation. However, the main focus in this paper is to study interactive systems where the pairing is not obvious. In such cases, we may use the alternative approach where we solve the equation set analytically or numerically to find the Primal variables. Both strategies are applied to a continuously stirred tank reactor. The simulation results for this case study show that both approaches obtain similar performance.
HONO chemistry at a suburban site during the EXPLORE-YRD campaign in 2018: HONO formation mechanisms and impacts on O 3 production

HONO is an important precursor for OH radicals that impact secondary pollutants production. However, there are still large uncertainties about different HONO sources, which hinder accurate predictions of HONO concentration and hence atmospheric oxidation capacity. Here HONO was measured during the EXPLORE-YRD 15 campaign, along with other important parameters, enabling us to comprehensively investigate HONO variation characteristics and evaluate the relative importance of different HONO sources by using a box model. HONO showed significant variations, ranging from several tens of ppt to 4.4 ppb. The average diurnal pattern of HONO/NO x showed a maximum of 0.17 around noon and resembled that of j(O 1 D), indicating the existence of photo-induced sources. Modeling simulations with only the default HONO source (OH+NO) largely underestimated HONO concentrations, 20 with the modeled averaged noontime HONO concentration an order of magnitude lower than the observed concentration. The calculated unknown source strength (P unknown ) of HONO showed a nearly symmetrical diurnal profile with a maximum of 2.5 ppb h -1 around noon. The correlation analysis and sensitivity tests showed that photo-induced NO 2 conversion on the ground was able to explain P unknown .
ON LINEAR MODELS AND THE GRAPHS OF MINKOWSKI

THIS PAPER presents a graph-theoretic approach to linear models and to Minkowski-Leontief matrices which have significant realizations in mathematical economics and in the theory of stochastic processes [1, 2, 3, 5, 6, 13, 14]. We designate any finite nonnegative square matrix A with no row sum greater than unity as a matrix of Minkowsski-Leontief type. In the body of this paper a simple graph-theoretic formulation is set forth of necessary and sufficient conditions for the existence of the inverse (I -A)-', where I denotes the identity matrix and A is a matrix of Minkowski-Leontief type. We consider linear systems of the form x(I - A) = w, where w is a nonnegative vector, and give a complete graphtheoretic characterization of all nontrivial, finite and nonnegative solutions x of such systems for (I - A) singular or not. In the present approach, any matrix A of Minkowski-Leontief type is viewed as a principal submatrix of a stochastic matrix A* and it is shown that the properties of essential interest in the development rest on fundamental results in the theory of finite-dimensional Markov chains (discrete time parameter). We cite an algorithm for computing the unique stationary stochastic vector of an indecomposable stochastic matrix and show its relevance in the treatment of linear models. In the course of exposition, an economic interpretation is consistently set forth in the context of certain input-output models. The investigation of the character of solutions is of interest for descriptive as well as for normative economics. A static open input-output model of the form x(I - A) = w may, for example, be treated from an essentially phenomenological standpoint or it may be regarded as a representation compatible with the assumptions of the competitive market [10]. Delineation of the economically meaningful solutions ("equilibria") affords an intrinsic insight into the structure of the linear model.2 In the following development, we employ the graph-theoretic concepts and formulations introduced in [7] for the treatment of finite nonnegative matrices, in particular the concept of "cyclic net. " 1 This paper was prepared as a part of the project "Symbolic Methods in the Study of Organizations" under Contract Nonr-1180(00) with the Office of Naval Research. An earlier version of this paper appeared as Technical Report B, July 1956. 2 The relation of the present linear systems to multi-sector trade models and to issues of dynamic coupling and of macroeconomic stability has been well stated in [11] and we accordingly adhere to the economy of a single interpretation. 325
Bioremediation of textile dyes

- The food, pharmaceutical, cosmetic, printing, textile, and leather sectors all employ dyes extensively. Due to this, highly colored effluents have been released, affecting the water bodies' clarity and gas solubility. The textile sector uses a lot of water and creates a tone of contaminated water, with azo dyes being the main contaminant. The advantage of using bioremediation processes to treat textile wastewater is that they are less sludge-producing, cost-effective, and environmentally beneficial. Traditional technologies are not always effective and may not be ecologically being when used to treat hazardous and stubborn colors. This is what motivated the quest for alternative technologies like bacteria-based biodegradation. However, some aerobic bacteria can reduce azo compounds and generate aromatic amine in the presence of certain oxygen-catalyzed enzymes known as azo reductases. It can be completely degrade under coupled aerobic and anaerobic degradation. Bioremediation treats the hazardous pollutants from the wastewater as an effective and efficient method. This article briefly discussed the removal of pollutants from the environment with the help of microorganisms.
Locality-aware batch scheduling of I/O intensive workloads

: Clusters make use of workload schedulers such as the Slurm Workload Manager to allocate computing jobs onto nodes. These schedulers usually aim at a good trade-o(cid:27) between increasing resource utilization and user satisfaction (decreasing job waiting time). However, these schedulers are typically unaware of jobs sharing large input (cid:28)les, which may happen in data intensive scenarios. The same input (cid:28)les may be loaded several times, leading to a waste of resources. We study how to design a data-aware job scheduler that is able to keep large input (cid:28)les on the computing nodes, without impacting other memory needs, and can use previously loaded (cid:28)les to limit data transfers in order to reduce the waiting times of jobs. We present three schedulers capable of distributing the load between the computing nodes as well as re-using an input (cid:28)le already loaded in the memory of some node as much as possible. We perform simulations using real cluster usage traces to compare them to classical job schedulers. The results show that keeping data in local memory between successive jobs and using data locality information to schedule jobs allows a reduction in job waiting time and a drastic decrease in the amount of data transfers.
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale

Abstract. High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model.
Achieving Robustness in a Drawer Manipulation Task by using High-level Feedback instead of Planning

—Robotic manipulation behavior should be robust to disturbances that violate high-level task-structure. Such robustness can be achieved by constantly monitoring the environment to observe the discrete high-level state of the task. This is possible because different phases of a task are characterized by different sensor patterns and by monitoring these patterns a robot can decide which controllers to execute. This eliminates the need to plan a temporal sequence of those controllers and makes the behavior robust to unforeseen disturbances. We implement this idea as a probabilistic ﬁlter over discrete states where each state directly activates a controller. Based on this framework we present a robotic system that is able to robustly open a drawer and grasp tennis balls from it.