Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Aerosol Dry Deposition on Black Carbon Spatial Distributions and Radiative Effects in the Community Atmosphere Model CAM5

Dry deposition is an important process affecting the lifetime and spatial distributions of atmospheric aerosols. Black carbon (BC) plays an important role in the Earth's climate, but is subject to large bias in remote regions in model simulations. In this study, to improve the BC simulations, the scheme of Petroff and Zhang ( ) (PZ10) is implemented into the Community Atmospheric Model version 5 (CAM5), and model simulations using PZ10 are compared with the one using the default scheme of Zhang et al. ( ) (Z01) and observations. The PZ10 scheme predicts much lower dry deposition velocity (Vd) than Z01 for fine particles in Aitken, primary carbon, and accumulation modes, resulting in 73.0% lower of global mean BC dry deposition fluxes and 23.2% higher of global mean BC column burdens. CAM5 with PZ10 increases modeled BC concentrations at all altitudes and latitudes compared to Z01, which improves the agreement with observations of BC profiles in the lower troposphere in the Arctic. It also improves the simulation of surface BC concentrations in high‐latitudes remote regions and its seasonality in the Arctic. The global annual mean radiative effects due to aerosol‐radiation interactions (REari) and aerosol‐cloud interactions (REaci) of BC from the CAM5 experiment using Z01 are 0.61 ± 0.007 and −0.11 ± 0.02 W m−2, respectively, compared to slightly larger REari (0.75 ± 0.01 W m−2) and REaci (–0.14 ± 0.02 W m−2) from CAM5 using PZ10. The results suggest that Brownian diffusion efficiency is a key factor for the predictions of Vd, which requires better representation in the global climate models.
Successes and Difficulties in Calculating Atomic Oscillator Strengths and Transition Rates

There is an on-going need for accurate oscillator strengths to be used in astrophysical applications, particularly in plasma diagnostics and in the modelling of stellar atmospheres and the interstellar medium. There are several databases in regular use which contain some of the required data, although often insufficiently complete, and sometimes not sufficiently accurate. In addition, several atomic structure packages are available through the literature, or from their individual authors, which would allow further calculations to be undertaken. Laboratory measurements provide an important check on the accuracy of calculated data, and the combined efforts of theorists and experimentalists have succeeded in providing data of an accuracy sufficient for some astrophysical applications. However, the insufficiency or inadequacy of atomic data is a continuing problem. We discuss in the context of appropriate examples some of the principal steps which researchers have taken to calculate accurate oscillator strengths, including both ab initio results and also various extrapolation processes which attempt to improve such results. We also present some examples of the main causes of difficulty in such calculations, particularly for complex (many-electron) ions, and indicate ways in which the difficulties might be overcome.
The Impact of Atmospheric Phenomena on South Korean Students’ Engagement at School through the Mediating Effect of the Students’ Health

This study examined how South Korea’s regular significant atmospheric phenomena, the yellow dust and the monsoon, affected students’ health and academic engagement at school. Five hundred middle and high school students were surveyed to figure out the atmospheric impacts on the students’ lives. The results of this study found that the severity of yellow dust and monsoon conditions experienced by the students negatively influenced their academic engagement. However, the variable of students’ concerns about their health was shown to have a mediating effect between the intensity of yellow dust and the level of academic engagement. These results indicated the need to alleviate the yellow dust and monsoon atmospheric phenomena and their effects at both the school and national levels in order to improve the academic engagement of students in South Korea.
A compositional logic for control flow and its application in foundational proof-carrying code

Proof-Carrying Code (PCC) is a static mechanism that mechanically verifies type safety of machine-language programs. But the problem in conventional PCC is, who will verify the verifier (the type checker) itself? The Foundational Proof-Carrying Code (FPCC) project at Princeton verifies the soundness of the type checker from the smallest possible set of axioms—logic plus machine semantics. One challenge in the verification is that machine code, unlike high-level languages, contains unstructured control flow (due to arbitrary jumps). A piece of machine code can contain multiple entry points that jump instructions might jump to, and multiple exit points. Traditional Hoare logic and its variants either verify the partial correctness of programs with only one entry and one exit, or need the whole program to verify jump instructions, which is not modular. The major contribution of this dissertation is a program logic, Lc , which modularly verifies properties of machine-code fragments. Unlike previous program logics, the basic reasoning units in Lc are multiple-entry and multiple-exit code fragments. Lc provides composition rules to combine code fragments and to eliminate intermediate entries/exits in the combined fragment. Lc is not only useful for reasoning about properties of machine code with unstructured control flow, but also useful for deriving rules for common control-flow structures such as while-loops, repeat-until-loops, among others. We also present a semantics for Lc and prove that Lc is both sound and complete. As an application to the FPCC project, I have implemented Lc on top of the SPARC machine language and used Lc 's rules to verify the soundness of the instruction rules of a full-fledged low-level typed assembly language—LTAL. This demonstrates Lc 's applicability of verifying properties of machine-language programs.
Parallelization techniques with improved dependence handling

Continuing exponential growth in transistor density and diminishing returns from the increasing transistor count have forced processor manufacturers to pack multiple processor cores onto a single chip. These processors, known as multi-core processors, generally do not improve the performance of single-threaded applications. Automatic parallelization has a key role to play in improving the performance of legacy and newly written single-threaded applications in this new multi-threaded era. Automatic parallelizations transform single-threaded code into a semantically equivalent multi-threaded code by preserving the dependences of the original code. This dissertation proposes two new automatic parallelization techniques that differ from related existing techniques in their handling of dependences. This difference in dependence handling enables the proposed techniques to outperform related techniques. The first technique is known as parallel-stage decoupled software pipelining (PS-DSWP). PS-DSWP extends pipelined parallelization techniques like DSWP by allowing certain pipelined stages to be executed by multiple threads. Such a parallel execution of pipelined stages requires distinguishing inter-iteration dependences of the loop being parallelized from the rest of the dependences. The applicability and effectiveness of PS-DSWP is further enhanced by applying speculation to remove some dependences. The second technique, known as speculative iteration chunk execution (Spice), uses value speculation to ignore inter-iteration dependences, enabling speculative execution of chunks of iterations in parallel. Unlike other value-speculation based parallelization techniques, Spice speculates only a few dynamic instances of those inter-iteration dependences. Both these techniques are implemented in the VELOCITY compiler and are evaluated using a multi-core Itanium 2 simulator. PS-DSWP results in a geometric mean loop speedup of 2.13 over single-threaded performance with five threads on a set of loops from five benchmarks. The use of speculation improves the performance of PS-DSWP resulting in a geometric mean loop speedup of 3.67 over single-threaded performance on a set of loops from six benchmarks. Spice shows a geometric mean loop speedup of 2.01 on a set of loops from four benchmarks. Based on the above experimental results and qualitative and quantitative comparisons with related techniques, this dissertation demonstrates the effectiveness of the proposed techniques.
Nonlinear Science and Numerical Simulation

A deterministic model with testing of infected individuals has been proposed to inves- tigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depend- ing on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic’s severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.
The Dependence of Convective Core Overshooting on Stellar Mass: A Semi-empirical Determination Using the Diffusive Approach with Two Different Element Mixtures

Convective core overshooting has a strong influence on the evolution of stars of moderate and high mass. Studies of double-lined eclipsing binaries and stellar oscillations have renewed interest in the possible dependence of overshooting on stellar mass, which has been poorly constrained by observations so far. Here, we have used a sample of 29 well-studied double-lined eclipsing binaries in key locations of the H–R diagram to establish the explicit dependence of on mass, where is the free parameter in the diffusive approximation to overshooting. Measurements of the masses, radii, and temperatures of the binary components were compared against stellar evolution calculations based on the MESA code to infer semi-empirical values of for each component. We find a clear mass-dependence such that rises sharply from zero in the range , and levels off thereafter up to the 4.4 limit of our sample. Tests with two different element mixtures indicate the trend is the same, and we find it to also be qualitatively similar to the one established in our previous study with the classical step-function implementation of overshooting characterized by the free parameter . Based on these measurements, we infer an approximate relationship between the two overshooting parameters of , with a possible dependence on stellar properties.
Structure-measure: A New Way to Evaluate Foreground Maps

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Session-based Recommendations with Recurrent Neural Networks

We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: Information systems (IS) research has primarily focused on explicit perceptions, attitudes, and intentions of which users are largely aware. We argue that this view may be too narrow. We extend it by presenting the concept of implicit attitude, which is as a stable subconscious evaluation of an IS that is developed a priori, stored in memory
The Description Length of Deep Learning Models

Solomonoff’s general theory of inference (Solomonoff, 1964) and the Minimum Description Length principle (Grünwald, 2007; Rissanen, 2007) formalize Occam’s razor, and hold that a good model of data is a model that is good at losslessly compressing the data, including the cost of describing the model itself. Deep neural networks might seem to go against this principle given the large number of parameters to be encoded. We demonstrate experimentally the ability of deep neural networks to compress the training data even when accounting for parameter encoding. The compression viewpoint originally motivated the use of variational methods in neural networks (Hinton and Van Camp, 1993; Schmidhuber, 1997). Unexpectedly, we found that these variational methods provide surprisingly poor compression bounds, despite being explicitly built to minimize such bounds. This might explain the relatively poor practical performance of variational methods in deep learning. On the other hand, simple incremental encoding methods yield excellent compression values on deep networks, vindicating Solomonoff’s approach.
Initial hate query External hate lexicon Step 1 : Data acquisition & lexicon construction Data sources

User-generated content online is shaped by many factors, including endogenous elements such as platform affordances and norms, as well as exogenous elements, in particular significant events. These impact what users say, how they say it, and when they say it. In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence. We anchor this study in a series of attacks involving Arabs and Muslims as perpetrators or victims, occurring in Western countries, that have been covered extensively by news media. These attacks have fueled intense policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and hateful speech. The focus of our research is to model the effect of the attacks on the volume and type of hateful speech on two social media platforms, Twitter and Reddit. Among other findings, we observe that extremist violence tends to lead to an increase in online hate speech, particularly on messages directly advocating violence. Our research has implications for the way in which hate speech online is monitored and suggests ways in which it could be fought.
Title: Arctic sea ice radar freeboard retrieval from ERS-2 using altimetry : Toward sea ice thickness observation from 1995 to 2021

The authors construct a 25-year record of Arctic sea ice radar freeboard by reconciling the measurements from three radar altimetry missions, one ongoing and two historic. Their primary motivation is to generate the first step towards a long-term sea ice thickness record for the Arctic Ocean. This would be the first observational sea ice thickness record spanning such a long period and would offer valuable comparison to existing proxy sea ice thickness (SIT) records based on ice age and models. In my view, a robust 25+ year time series of Arctic sea ice thickness would represent a major scientific breakthrough with 10 implications for understanding global climate changes in the modern era and validating and improving sea ice models, among other potential applications. Generally, I find the approach and methods to be scientifically sound. I have some minor comments but nothing that questions the rigour of the generated time series. The validation against existing SIT data from satellites, airborne and in situ sensors is comprehensive and convincing
AdaDepth : Unsupervised Content Congruent Adaptation for Depth Estimation Supplementary material

Layer Type Filter Size Filter Num Stride Output Size Input 128 × 160 × 1 c1 Conv 3 × 3 64 1 128 × 160 × 64 c2 Conv* 3 × 3 64 2 64 × 80 × 64 c3 Conv* 3 × 3 128 1 64 × 80 × 128 c4 Conv* 3 × 3 128 2 32 × 40 × 128 c5 Conv* 3 × 3 256 1 32 × 40 × 256 c6 Conv* 3 × 3 256 2 16 × 20 × 256 c7 Conv* 3 × 3 512 1 16 × 20 × 512 c8 Conv* 3 × 3 1024 2 8 × 10 × 1024 c9 Conv* 1 × 1 1024 1 8 × 10 × 1024 c10 Conv 1 × 1 1 1 8 × 10 × 1
ON TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS

The method of invisible invariants was developed originally in order to verify safety properties of parameterized systems in a fully automatic manner. The method is based on (1) a project&generalize heuristic to generate auxiliary constructs for parameterized systems and (2) a small-model theorem, implying that it is sufficient to check the validity of logical assertions of a certain syntactic form on small instantiations of a parameterized system. The approach can be generalized to any deductive proof rule that (1) requires auxiliary constructs that can be generated by project&generalize, and (2) the premises resulting when using the constructs are of the form covered by the small-model theorem. The method of invisible ranking, presented here, generalizes the approach to liveness properties of parameterized systems. Starting with a proof rule and cases where the method can be applied almost “as is,” the paper progresses to develop deductive proof rules for liveness and extend the small-model theorem to cover many intricate families of parameterized systems.
Ahead of His Time: Jacob Lipman's 1930 Estimate of Atmospheric Sulfur Deposition for the Conterminous United States

Abstract A 1936 New Jersey Agricultural Experiment Station Bulletin provided an early quantitative assessment of atmospheric deposition of sulfur for the United States that has been compared in this study with more recent assessments. In the early 20th century, anthropogenic sulfur additions from the atmosphere to the soil by the combustion of fossil fuels were viewed as part of the requisite nutrient supply of crops. Jacob G. Lipman, the founding editor of Soil Science, and his team at Rutgers University, made an inventory of such additions to soils of the conterminous United States during the economic depression of the 1930s as part of a federally funded project looking at nutrient balances in soils. Lipman's team gathered data compiled by the US Bureau of Mines on coal and other fuel consumption by state and calculated the corresponding amounts of sulfur emitted. Their work pioneered a method of assessment that became the norm in the 1970s to 1980s—when acid rain emerged as a national issue. Lipman's estimate of atmospheric sulfur deposition in the 1930 is in reasonable agreement with recent historic reconstructions.
Development and Initial Validation of a Stochastic Discrete Event Simulation to Assess Disaster Preparedness

Introduction: Assessing disaster preparedness in a given region is a complex problem. Current methods are often resource-intensive and may lack generalizability beyond a specific scenario. Computer-based stochastic simulations may be an additional method but would require systems that are valid, flexible, and easy to use. Emergo Train System (ETS) is an analog simulation system used for disaster preparedness assessments. Aim: To digitalize the ETS model and develop stochastic simulation software for improved disaster preparedness assessments. Methods: A simulation software was developed in C#. The simulation model was based on ETS. Preliminary verification and validation (V&V) tests were performed, including unit and integration testing, trace validation, and a comparison to a prior analog ETS disaster preparedness assessment exercise. Results: The software contains medically validated patients from ETS and is capable of automatically running disaster scenarios with stochastic variations in the injury panorama, available resources, geographical location, and other variables. It consists of two main programs: an editor where scenarios can be constructed and a simulation system to evaluate the outcome. Initial V&V testing showed that the software is reliable and internally consistent. The comparison to the analog exercise showed a general high agreement in terms of patient outcome. The analog exercise featured a train derailment with 397 injured, of which 45 patients suffered preventable death. In comparison, the computer simulation ran 100 iterations of the same scenario and indicated that a median of 41 patients (IQR 31 to 44) would suffer a preventable death. Discussion: Stochastic simulation methods can be a powerful complement to traditional capability assessments methods. The developed simulation software can be used for both assessing emergency preparedness with some validity and as a complement to analog capability assessment exercises, both as input and to validate results. Future work includes comparing the simulation to real disaster outcomes.
Closed-loop feedback control of bluff body aeroacoustics

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Influence of lattice defects on the coherent interaction of photovoltaic lattice solitons

The influence of an optically induced waveguide lattice with a defect on the coherent interaction of one-dimensional lattice solitons (LSs) formed via the photovoltaic photorefractive effect is investigated by employing numerical simulations. The results show that the uniform lattice can keep the neighboring LSs propagating with constant spacing and can affect the energy transfer between them. Further research has shown that the energy transfer can be effectively controlled by adjusting the relative phase difference between neighboring LSs and by introducing a defect in the waveguide lattice.