Miracle-dz/UniFAR-FTUs huggingface dataset:
The sulfate‐CCN‐cloud albedo effect

Aerosol particles, such as sulfate aerosols, can act as cloud condensation nuclei (CCN). The CCN spectrum and the water vapour supply in a cloud determine the cloud droplet number concentration (CDNC) and hence the shortwave optical properties of low-level liquid clouds. The capability of anthropogenic aerosols to increase cloud reflectivity and thereby cool the Earth's surface is referred to as the indirect effect of anthropogenic aerosols. To obtain an estimate of this effect on climate, we empirically relate the CDNC, and thus the cloud optical properties, of two general circulation models (GCM) to the sulfate aerosol mass concentration derived from a chemical transport model. Based on a series of model experiments, the normalized globally averaged indirect forcing is about − 1 W/m 2 and ranges from – 0.5 to − 1.5 W/m 2 in both GCMs for different experiments. However, it is argued that the total uncertainty of the forcing is certainly larger than this range. The overall agreement between the two climate models is good, although the geographical distributions of the forcing are somewhat different. The highest forcings occur in and off the coasts of the polluted regions of the Northern Hemisphere. The regional distribution of the forcing and the land/sea contrast are very sensitive to the choice of the CDNC-sulfate mass relationship. The general patterns of the forcing, and the appropriateness of the different CDNC-sulfate mass relationships, are assessed. We also examine the simulated droplet effective radii and compare them with satellite retrievals. DOI: 10.1034/j.1600-0889.47. issue3.1. x
Factors Associated with Types of Mathematics Anxiety in College Students.

This article investigates the interrelatedness of various types of mathematics anxiety with attitudes toward mathematics, learning preferences, study motives, and strategies. An 80-item version of the Mathematics Anxiety Rating Scale (MARS) was completed by 173 university students enrolled in one of three introductory statistics courses offered by the departments of mathematics, psychology, or sociology. Mathematics attitude scales were also administered in conjunction with the Study Process Questionnaire (SPQ), which depicts students' motivations to use study methods associated with contrasting learning approaches. Factor analysis of the MARS identified six factors labeled as General Evaluation Anxiety, Everyday Numerical Anxiety, Passive Observation Anxiety, Performance Anxiety, Mathematics Test Anxiety, and Problem-Solving Anxiety. The examination of both substantive and peripheral factors supported a multidimensional view of mathematics anxiety. Correlational analysis indicated complex interaction patterns between attitudes toward mathematics and the six MARS factors, depending on the overall level of anxiety experienced. Variation in orientation to learning also proved significantly related to specific types of anxiety, attitudes, and instructional factors. The results confirm the utility of learning-approach theory and instruments for analyzing relationships between cognitive and affective components of mathematics anxiety. Mathematics anxiety has become a euphemism for debilitating test stress, low self-confidence, fear of failure, and negative attitudes toward mathematics learning. Despite substantial inquiry into the dimensions and correlates of mathematics anxiety, several issues remain unresolved. Many researchers conclude that the Mathematics Anxiety Rating Scale (MARS; Suinn, Edie, Nicoletti, & Spinelli, 1972) is a multidimensional construct, without discussing the theoretical meaning(s) of this concept. However, the complexity of mathematics anxiety cannot be limited to factors identified in the MARS. Recent studies also confirm the interrelatedness of mathematics attitudes and anxieties but disregard the importance of cognitive processes in generating proneness to anxiety. This study analyzes mathematics anxiety in relation to learning approaches, cognitive processes, and affective dispositions. The background section summarizes literature that illustrates the empirical and theoretical bases of this analytical framework.
Cirrus Cloud Properties Derived from High Spectral Resolution Infrared Spectrometry during FIRE II. Part III: Ground-Based HIS Results

Abstract During FIRE II, cirrus clouds were observed in the wavelength range 3–19, µm with two High Resolution Interferometer Sounders as described in the Part I companion paper. One, known as AC-HIS, was mounted on the NASA ER-2 aircraft in order to look down on the clouds; these results are described in the Part II companion paper. The other, GB-HIS, also known as the Atmospheric Emitted Radiance Interferometer (AERI), was ground based. The AERI observations have been simulated, assuming scattering from spherical ice particles, using a single-layer doubling model for the cloud, for two atmospheric windows at 700–1250 and 2650–3000 cm−1. The second of these windows is affected by scattered sunlight, which has been included in the calculations. The sensitivity of the cloud signal to quantities such as the ice water path (IWP) and effective radius (reff) have been determined. Using the cloud model, best fits have been derived for IWP and reff, for both windows individually and together. Possible errors in .
Relative importance of calcium and magnesium in hardness‐based modification of copper toxicity

We cause of the relationship between water hardness and the toxicity of many metals, total hardness is used as a model parameter to calculate ambient water quality criteria for copper and other metals. However, the relative contribution of the Ca and Mg components of total hardness as modifiers of metals toxicity is not considered in the water quality criteria. Acute Cu toxicity was measured in rainbow trout (Oncorhynchus mykiss) and chinook salmon (O. tshawytscha) swim‐up fry in laboratory waters that were formulated to have similar total hardness and alkalinity but different Ca and Mg concentrations. Experiments were performed at nominal total hardness values of 40 and 90 mg/L (as CaCO3). In four paired toxicity tests, acute Cu toxicity was significantly lower, i.e., 96‐h LC50s were higher, in laboratory waters containing proportionately more Ca (Ca:Mg molar ratios of 1.5–5.2) than in waters containing less Ca (Ca:Mg molar ratios of 0.2–0.8). The relative increase in the 96‐h Cu LC50 at higher Ca concentrations, but similar total hardness concentrations, was between 28 and 86% when the low Ca treatment was similar to American Society for Testing and Materials laboratory water (Ca:Mg molar ratio = 0.7). Failure to account for differences in Ca when matching or adjusting for total hardness thus exerts an important influence on the prediction of metal toxicity. These differences must be addressed in water‐effect ratio testing in which paired tests with laboratory and site waters are conducted.
Geochemical contamination of urban water by concrete stormwater infrastructure: applying an epoxy resin coating as a control treatment

Urban drainage systems that use concrete gutters, pits and pipes have been adopted worldwide by drainage engineers. This study tested the hypothesis that treating a concrete pipe with a coating of epoxy resin is an effective method to reduce the concrete mineral leaching and associated contamination of water carried within the pipe. Four 20 litre samples of rainwater were individually circulated through the untreated and epoxy treated portions of the pipe for 100 minutes. After recirculation through the untreated portion of the pipe pH increased by almost two units, electrical conductivity doubled and there were significant increases in bicarbonate, calcium and other ions. In contrast, rainwater circulated through the epoxy treated portion of the pipe showed a minimal pH increase (0.32 pH units) but no other significant increases in any other water chemistry attributes. The epoxy resin greatly reduced mineral contamination of recirculated water, supporting the hypothesis.
Preparative Parameters, Magnesium Effects, and Anion Effects in the Crystallization of Birnessites

Na-birnessites (Na4Mn14O27·9H2O) have been prepared by aging MnOx gels, whose composition can be represented as MnA·wMgA·xKMnO4·yNaOH·zH2O (A is any of the anions of chloride, nitrate, sulfate, and acetate). The effects of aging time, aging temperature, basicity, the amount of water, the addition of Mg2+, the ratios of MnO4-/Mn2+, and types of anions on the crystallization of birnessite, the phase transformations of intermediate phases, the morphology, the ion-exchange properties, and the thermal stability have been investigated. XRD (X-ray diffraction), SEM (scanning electronic microscope), EDX (energy dispersive X-ray), IR (Infrared), DSC (differential scanning calorimetry), and TGA (thermogravimetric analysis) techniques have been used. The preparative parameters such as composition and temperature have been optimized. Three stages are observed in the crystallization process of birnessite on increasing the crystallization time: an induction period, a fast crystallization period, and a steady-state per.
In situ manipulation of van der Waals heterostructures for twistronics

Rotation of 2D materials by polymer patch and gel stamp manipulator opens up a new strategy for twistronics. In van der Waals heterostructures, electronic bands of two-dimensional (2D) materials, their nontrivial topology, and electron-electron interactions can be markedly changed by a moiré pattern induced by twist angles between different layers. This process is referred to as twistronics, where the tuning of twist angle can be realized through mechanical manipulation of 2D materials. Here, we demonstrate an experimental technique that can achieve in situ dynamical rotation and manipulation of 2D materials in van der Waals heterostructures. Using this technique, we fabricated heterostructures where graphene is perfectly aligned with both top and bottom encapsulating layers of hexagonal boron nitride. Our technique enables twisted 2D material systems in one single stack with dynamically tunable optical, mechanical, and electronic properties.
Research on Activation Function in Deep Convolutional Neural Network

Nowadays, the deep learning technology has been widely and increasingly used in all fields, the convolutional neural networks is a representative network structure in deep learning technology, and the activation functions have a very great effect on realizing nonlinear modeling. This paper introduces the structure of convolution neural network and the ability of nonlinear activation functions, analyzes the formula, graph, advantages and disadvantages of commonly used nonlinear activation function, which can provide reference for related research.
An Automatic Labeling Approach for Stable and Unstable Human Gait Classification in Videos

Many approaches based on human skeletons have been proposed for detecting human falls and even predicting the falling risk. Traditionally, labeling skeleton sequences artificially is a time-consuming task. To alleviate the above problem, we propose an automatic approach for labeling skeleton sequences extracted from human walking videos as stable or unstable gaits. The proposed approach is mainly based on calculations of shoulder-inclining angles and knees' bending angles of skeletons. Experimental analysis has shown the proposed approach possesses the potential of automatically labeling skeleton sequences as stable or unstable.
BATF regulates innate lymphoid cell hematopoiesis and homeostasis

Batf supports differentiation of ILC progenitors and optimal population of NK, ILC1, ILC2, and ILC3 in peripheral tissues. A path to innate lymphoid cells Mature innate lymphoid cell (ILC) subsets develop from ILC progenitors that have differentiated from early hematopoietic progenitors, but the regulation of this process is not well understood. Liu et al. show that basic leucine zipper ATF-like transcription factor (Batf) is a critical regulator of ILC development. IL-7 induces Batf expression in α-lymphoid progenitors, and Batf activates transcription of the Nfil3 gene, which induces ILC hematopoiesis. The presence of Batf is needed for maintenance of early and late ILC bone marrow progenitors and peripheral homeostasis of ILC1, ILC2, ILC3, and NK cells. Batf deficiency is associated with ILC lymphopenia and defective ILC responses, including inflammation and inadequate protection against enteric bacteria. These findings define the critical role of Batf in ILC development. Early hematopoietic progenitors undergo sophisticated developmental processes to become committed innate lymphoid cell (ILC) progenitors and ultimately mature ILC subsets in the periphery. Basic leucine zipper ATF-like transcription factor (Batf) plays important roles in lymphocyte biology. We report here that Batf regulates the production of bone marrow ILC progenitors and maintenance of peripheral ILCs. The expression of Batf is induced during ILC development at the α-lymphoid progenitor stage in response to the cytokine IL-7. As a potential mechanism, up-regulated Batf binds and activates transcription of the Nfil3 gene to promote ILC hematopoiesis. Batf is necessary to maintain normal numbers of early and late ILC progenitors in the bone marrow and mature ILC1, ILC2, ILC3, and NK cells in most peripheral tissues. Batf deficiency causes ILC lymphopenia, leading to defective ILC responses to inflammatory cytokines and defective immunity to enteric bacterial infections. Thus, Batf plays critical roles in bone marrow hematopoiesis, peripheral homeostasis, and effector functions of ILCs.
Planning with General Objective Functions: Going Beyond Total Rewards

Standard sequential decision-making paradigms aim to maximize the cumulative reward when interacting with the unknown environment. , i.e., maximize (cid:80) Hh =1 r h where H is the planning horizon. However, this paradigm fails to model important practical applications, e.g., safe control that aims to maximize the lowest reward, i.e., maximize min Hh =1 r h . In this paper, based on techniques in sketching algorithms, we propose a novel planning algorithm in deterministic systems which deals with a large class of objective functions of the form f ( r 1 , r 2 , .r H ) that are of interest to practical applications. We show that efﬁcient planning is possible if f is symmetric under permutation of coordinates and satisﬁes certain technical conditions. Complementing our algorithm, we further prove that removing any of the conditions will make the problem intractable in the worst case and thus demonstrate the necessity of our conditions.
Keepers of the comments: How comment moderators handle audience contributions

As news commenting has evolved as a participatory tool and journalists have developed traditional practices for moderation, there are questions about how to promote quality spaces for news discourse. Using gatekeeping theory, this study analyzes in-depth interviews with 13 news comment moderators to understand how these individuals establish moderation routines and define their professional role. This provides new insight into the journalist–audience relationship and the development of new media practices for online news production.
Freins et leviers à la diversification des cultures : étude au niveau des exploitations agricoles et des filières

La diversification des cultures est souvent presentee comme un levier d’action pour accroitre la durabilite des systemes de production agricoles. Bien raisonnee, elle favorise en effet une reduction de l’usage d’intrants exterieurs a l’exploitation – pesticides, engrais, eau – et des nuisances environnementales associees a leur utilisation excessive. A l’inverse, la simplification des systemes de culture engagee depuis plus de 40 ans s’est accompagnee d’un recours croissant aux intrants. Cependant, malgre son interet pour les systemes de production en termes de durabilite ecologique mais aussi economique (repartition des risques), et son inscription dans divers plans et dispositifs incitatifs, la diversification des cultures progresse peu. Les ministeres en charge de l’agriculture et de l’environnement ont donc commande a l’INRA une etude visant a identifier les freins a la diversification des cultures en France et les leviers mobilisables, par les pouvoirs publics notamment, pour la favoriser. L’hypothese de travail est que ces freins relevent du fonctionnement global du systeme agro-industriel, et de la capacite de developpement de filieres valorisant les cultures de diversification. L’etude a examine un certain nombre de cas representatifs de la diversite des filieres et notamment de leurs modes d’organisation, qui determinent la coordination et l’engagement des acteurs economiques impliques dans le developpement d’une culture de diversification.
Parameterized Complexity of Deletion to Scattered Graph Classes

Graph-modiﬁcation problems, where we add/delete a small number of vertices/edges to make the given graph to belong to a simpler graph class, is a well-studied optimization problem in all algorithmic paradigms including classical, approximation and parameterized complexity. Speciﬁcally, graph-deletion problems, where one needs to delete at most k vertices to place it in a given non-trivial hereditary (closed under induced subgraphs) graph class, captures several well-studied problems including Vertex Cover , Feedback Vertex Set , Odd Cycle Transveral , Cluster Vertex Deletion , and Perfect Deletion . Investigation into these problems in parameterized complexity has given rise to powerful tools and techniques. While a precise characterization of the graph classes for which the problem is ﬁxed-parameter tractable (FPT) is elusive, it has long been known that if the graph class is characterized by a ﬁnite set of forbidden graphs, then the problem is FPT. In this paper, we initiate a study of a natural variation of the problem of deletion to scattered graph classes where we need to delete at most k vertices so that in the resulting graph, each connected component belongs to one of a constant number of graph classes. A simple hitting set based approach is no longer feasible even if each of the graph classes is characterized by ﬁnite forbidden sets. As our main result, we show that this problem (in the case where each graph class has a ﬁnite forbidden set) is ﬁxed-parameter tractable by a O ∗ (2 k O (1) ) 1 algorithm, using a combination of the well-known techniques in parameterized complexity – iterative compression and important separators . Our approach follows closely that of a related problem in the context of satisﬁability [Ganian, Ramanujan, Szeider, TAlg 2017], where one wants to ﬁnd a small backdoor set so that the resulting CSP (constraint satisfaction problem) instance belongs to one of several easy instances of satisﬁability. While we follow the main idea from this work, there are some challenges for our problem which we needed to overcome
Investigating Gender Bias in Language Models Using Causal Mediation Analysis

Many interpretation methods for neural models in natural language processing investigate how information is encoded inside hidden representations. However, these methods can only measure whether the information exists, not whether it is actually used by the model. We propose a methodology grounded in the theory of causal mediation analysis for interpreting which parts of a model are causally implicated in its behavior. The approach enables us to analyze the mechanisms that facilitate the ﬂow of information from input to output through various model components, known as mediators. As a case study, we apply this methodology to analyzing gender bias in pre-trained Transformer language models. We study the role of individual neurons and attention heads in mediating gender bias across three datasets designed to gauge a model’s sensitivity to gender bias. Our mediation analysis reveals that gender bias effects are concentrated in speciﬁc components of the model that may exhibit highly specialized behavior.
Self-Supervised Generative Adversarial Compression

Deep learning’s success has led to larger and larger models to handle more and more complex tasks; trained models often contain millions of parameters. These large models are compute-and memory-intensive, which makes it a challenge to deploy them with latency, throughput, and storage constraints. Some model compression methods have been successfully applied to image classiﬁcation and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks. In this paper, we show that a standard model compression technique, weight pruning and knowledge distillation, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has compelling performance to high degrees of sparsity, can be easily applied to new tasks and models, and enables meaningful comparisons between different compression granularities.
Digraph Inception Convolutional Networks

Graph Convolutional Networks (GCNs) have shown promising results in modeling graph-structured data. However, they have difﬁculty with processing digraphs because of two reasons: 1) transforming directed to undirected graph to guarantee the symmetry of graph Laplacian is not reasonable since it not only misleads message passing scheme to aggregate incorrect weights but also deprives the unique characteristics of digraph structure; 2) due to the ﬁxed receptive ﬁeld in each layer, GCNs fail to obtain multi-scale features that can boost their performance. In this paper, we theoretically extend spectral-based graph convolution to digraphs and derive a simpliﬁed form using personalized PageRank. Speciﬁcally, we present the Digraph Inception Convolutional Networks (DiGCN) which utilizes digraph convolution and k th -order proximity to achieve larger receptive ﬁelds and learn multi-scale features in digraphs. We empirically show that DiGCN can encode more structural information from digraphs than GCNs and help achieve better performance when generalized to other models. Moreover, experiments on various benchmarks demonstrate its superiority against the state-of-the-art methods.
Average bit error rate performance of free-space optical systems over double generalized gamma fading channels based on avalanche photodiode detector

Abstract. The performance of free-space optical (FSO) communication systems based on avalanche photodiodes (APDs) is investigated over the aggregated double generalized gamma (double GG) fading channels. The approximate average bit error rate (ABER) expression is theoretically derived in terms of the double GG distributions under moderate and strong turbulent atmospheric conditions considering thermal noise and shot noise. The union bound and Hermite polynomials are then used to estimate the ABER performance of M-ary pulse-position modulation (PPM)-based systems and orthogonal frequency division multiplexing scheme (OFDM)-based systems. The ABER performance is studied with different receiver noise temperatures, modulation orders, turbulence strengths, and average photon counts assuming plane wave and spherical wave propagations. The results show that adopting an optimal average APD gain affected by the receiver temperature minimizes the ABER value for both wave propagations. In addition, the present APD-based system offers better ABER performance than that of a PIN-based FSO system over the double GG fading channels at two receiver noise temperatures of 200 and 400 K for both the PPM and OFDM schemes. We provide a reference for FSO system design.