Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistency and Reproducibility of Bioaerosol Delivery for Infectivity Studies on Mice

Abstract : Questions about the clinical significance of an antimicrobial resin use don personal respirators led system to generate consistent test bioaerosols for use in animal studies. The hypothesis was proposed that an aerosol delivery system based on the Collision nebulizer can be designed and engineered to provide, at selectable concentrations, a respiratory challenge of bioaerosol particles that is verifiably consistent in time and that can be fed in separate experiments through treated and untreated filters to deliver a consistent challenge to a small-animal model of human respiration. To verify this hypothesis, such an experimental filtration system was designed and built. Challenge trials were performed with MS2 bacteriophage and Bacillus atrophaeus. Over 30 to 40 minutes, the particle size distribution (PSD) was measured, and viability of microorganisms collected in All-Glass Impingers (AGI-4s) was determined. Concentrations of particles and microorganisms downstream of the filter were too low to measure, and the viable counts for MS2 bacteriophage were not measured at all owing to problems with the assay method. However, in each experiment, the coefficients of variations (CVs) of time-series measurements of the total particle count, count median diameter, and geometric experiments with viability data, all CVs of time-series measurements of upstream viable airborne concentration were less than 26%. This CV is somewhat higher than has been reported in the literature for tests with airborne Bacillus spores, but the plating method used to measure the viability may have introduced additional variation that was not caused within the system itself. It can be reasoned based on this data that the system can provide a sufficiently steady aerosol challenge to be used for later studies using a small-animal model of human respiration. The system provides a design for an animal exposure system incorporating aerosol filtration, which is a capability previously unreported.
Exploring the phenomena of income tax justice and tax Evasion through understanding the basic relationship between income tax departmentsAnd the private companies

This study is about exploring the phenomena of income tax justice and tax evasion through understanding the basic relationship between Income Tax Departments and the Private Companies in Palestine. It aimed at achieving and spreading tax values and tax justice among taxpayers. It examined specific factors for rising tax evasion phenomena and establishing mechanism to curb the problem. It provided insights into the problem and helped developing ideas on eliminating the phenomena of tax evasion, and achieving the desirable tax justice purposes. The study was conducted in State of Palestine. This study has used Qualitative paper with phenomenology approach. Data were collected through documentation, observation and online interviews. Qualitative data were subjected to content analysis. The findings showed that the phenomena of tax evasion were extensively found in the middle-small sized companies and free professions, not in big corporations. The middle-small sized companies did not meet the target of achieving the phenomena of tax justice as it was expected. Many factors were associated with tax evasion problem in Palestine namely high tax rate, complexity of tax laws systems and procedures, no public enlightenment campaign, lack of adequate tax incentives, poor relationship of tax payers with tax administrations, proliferation of taxes, illiteracy of tax calculation by some private companies, taxpayer’s perception towards government ability to utilize tax collection for social welfare, and limited resources and capacity of tax administration. The study recommended to work on enhancing the coordination between the income tax department and the private companies in modern methods and developing an electronic tax system and increasing and the taxation awareness to limit the phenomena of taxes evasion by conducting awareness campaigns through the mass media and different publication methods.
Self-similar structure of resistive ADAFs with outflow and large-scale magnetic field

Abstract The observations and simulations have revealed that large-scale magnetic field and outflows can exist in the inner regions of an advection-dominated accretion disc where the resistive diffusion may also be important. In the present paper, the roles of large-scale magnetic field and outflows in the structure of resistive advection-dominated accretion discs are explored by assuming that the accretion flow is radially self-similar. In the non-ideal magnetohydrodynamic (MHD) approximation, the results show that the angular velocity is always sub-Keplerian when both the outflow and the large-scale magnetic field are taken into account. A stronger toroidal field component leads to faster rotation, while the disc rotates with faster rate if the vertical field component is weaker. The increase of magnetic diffusivity causes the infall velocity to be close to Keplerian velocity. Although the previous studies in the ideal MHD approximation have shown that the disc temperature decreases due to the vertical field component, we find that the effect of vertical field component on the temperature of a resistive disc depends on the magnetic diffusivity. When the magnetic diffusivity is high, the more efficient mechanism for decreasing the disc temperature can be the outflows, and not the large-scale magnetic field. In such a limit of the magnetic diffusivity, the components of the large-scale magnetic field enhance the gas temperature. The increase of temperature can lead to heating and acceleration of the electrons and help us to explain the origin of phenomena such as the flares in Sgr A*. On the other hand, the infall velocity in such a limit rises as the temperature increases, and therefore the surface density falls to too low values. Any change in the density profile can alter the structure and the emitted spectrum of disc.
6G White Paper on Edge Intelligence

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-view Language Representations

Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other's language characterisation. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.
The Interaction between Credit Constraints and Uncertainty Shocks

This paper proposes a novel link between credit markets and uncertainty shocks. We introduce a role for credit uncertainty via collateral constraints in an otherwise standard real business cycle (RBC) model and show that an increase in credit uncertainty triggers a precautionary response that interacts with the collateral constraint to generate a simultaneous decline in output, consumption, investment, real wages, and hours; a feature that previous work on uncertainty shocks without credit constraints is unable to produce in a flexible‐price environment. We also empirically test the theoretical predictions and show that an unforeseen increase in credit uncertainty generates a simultaneous decline in a broad measure of real activity in recessions.
Comparing galaxy formation in the L-GALAXIES semi-analytical model and the IllustrisTNG simulations

We perform a comparison, object-by-object and statistically, between the Munich semi-analytical model, L-Galaxies, and the IllustrisTNG hydrodynamical simulations. By running L-Galaxies on the IllustrisTNG dark matter-only merger trees, we identify the same galaxies in the two models. This allows us to compare the stellar mass, star formation rate and gas content of galaxies, as well as the baryonic content of subhaloes and haloes in the two models. We find that both the stellar mass functions and the stellar masses of individual galaxies agree to better than $\sim0.2\,$dex. On the other hand, specific star formation rates and gas contents can differ more substantially. At $z=0$ the transition between low-mass star-forming galaxies and high-mass, quenched galaxies occurs at a stellar mass scale $\sim0.5\,$dex lower in IllustrisTNG than in L-Galaxies. IllustrisTNG also produces substantially more quenched galaxies at higher redshifts. Both models predict a halo baryon fraction close to the cosmic value for clusters, but IllustrisTNG predicts lower baryon fractions in group environments. These differences are due primarily to differences in modelling feedback from stars and supermassive black holes. The gas content and star formation rates of galaxies in and around clusters and groups differ substantially, with IllustrisTNG satellites less star-forming and less gas-rich. We show that environmental processes such as ram-pressure stripping are stronger and operate to larger distances and for a broader host mass range in IllustrisTNG. We suggest that the treatment of galaxy evolution in the semi-analytic model needs to be improved by prescriptions which capture local environmental effects more accurately.
WT5?! Training Text-to-Text Models to Explain their Predictions

Neural networks have recently achieved human-level performance on various challenging natural language processing (NLP) tasks, but it is notoriously difficult to understand why a neural network produced a particular prediction. In this paper, we leverage the text-to-text framework proposed by Raffel et al.(2019) to train language models to output a natural text explanation alongside their prediction. Crucially, this requires no modifications to the loss function or training and decoding procedures -- we simply train the model to output the explanation after generating the (natural text) prediction. We show that this approach not only obtains state-of-the-art results on explainability benchmarks, but also permits learning from a limited set of labeled explanations and transferring rationalization abilities across datasets. To facilitate reproducibility and future work, we release our code use to train the models.
Improving Semantic Segmentation via Self-Training

Deep learning usually achieves the best results with complete supervision. In the case of semantic segmentation, this means that large amounts of pixelwise annotations are required to learn accurate models. In this paper, we show that we can obtain state-of-the-art results using a semi-supervised approach, specifically a self-training paradigm. We first train a teacher model on labeled data, and then generate pseudo labels on a large set of unlabeled data. Our robust training framework can digest human-annotated and pseudo labels jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets while requiring significantly less supervision. We also demonstrate the effectiveness of self-training on a challenging cross-domain generalization task, outperforming conventional finetuning method by a large margin. Lastly, to alleviate the computational burden caused by the large amount of pseudo labels, we propose a fast training schedule to accelerate the training of segmentation models by up to 2x without performance degradation.
Impact of Diary Writing Practice on Mental Health of Adolescent Girls

This study has been conducted to study the impact of diary writing on mental health of adolescent girls. A sample of 100 girlsfrom Shri Ram Collage Muzaffernagar (U.P.), who are studying in first year of graduation, was selected from accidental sampling method.Sample was divided into two groups, 50 in experimental group and 50 in control group. Diary writing was selected as the independent variable, which was appliedfor 3 months where girls used to write down their thoughts and feelings; independently 30 minutes each for 5 days a week. Mental health of girls was assessed using the ‘Mental Health Inventory’. Statistical analysis of the data was performed using the SPSS software (version 18) the result indicated that there was a significant difference between experimental and control group on the level of mental health of adolescent girls. The findings of this study indicate that the practice of diary writing significantly improved the level of mental health of adolescent girls.
Rényi Bounds on Information Combining

Bounds on information combining are entropic inequalities that determine how the information, or entropy, of a set of random variables can change when they are combined in certain prescribed ways. Such bounds play an important role in information theory, particularly in coding and Shannon theory. The arguably most elementary kind of information combining is the addition of two binary random variables, i.e. a CNOT gate, and the resulting quantities are fundamental when investigating belief propagation and polar coding.In this work we will generalize the concept to Rényi entropies. We give optimal bounds on the conditional Rényi entropy after combination, based on a certain convexity or concavity property and discuss when this property indeed holds. Since there is no generally agreed upon definition of the conditional Rényi entropy, we consider four different versions from the literature.Finally, we discuss the application of these bounds to the polarization of Rényi entropies under polar codes.
“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition

Humor plays an important role in human languages and it is essential to model humor when building intelligence systems. Among different forms of humor, puns perform wordplay for humorous effects by employing words with double entendre and high phonetic similarity. However, identifying and modeling puns are challenging as puns usually involved implicit semantic or phonological tricks. In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence. PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols. Extensive experiments are conducted on two benchmark datasets. Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks. In-depth analyses verify the effectiveness and robustness of PCPR.
Language Model Prior for Low-Resource Neural Machine Translation

The scarcity of large parallel corpora is an important obstacle for neural machine translation. A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data. In this work, we propose a novel approach to incorporate a LM as prior in a neural translation model (TM). Specifically, we add a regularization term, which pushes the output distributions of the TM to be probable under the LM prior, while avoiding wrong predictions when the TM "disagrees" with the LM. This objective relates to knowledge distillation, where the LM can be viewed as teaching the TM about the target language. The proposed approach does not compromise decoding speed, because the LM is used only at training time, unlike previous work that requires it during inference. We present an analysis of the effects that different methods have on the distributions of the TM. Results on two low-resource machine translation datasets show clear improvements even with limited monolingual data.
Three new late-type hypervelocity star candidates from Gaia DR2 with refined selection criteria

Several dozen hypervelocity star (HVS) candidates have been reported based on the second data release of Gaia (Gaia DR2). However, it has been proven that the radial velocities of some Gaia HVS candidates are not reliable. In this paper, we employ refined astrometric criteria to re-examine Gaia DR2, arriving at a more reliable sample of HVS and high velocity star candidates than those found by previous authors. We develop a method called Binary Escape Probability Analysis to identify some HVS candidates. This method allows us to work with stars having only two epochs of measured radial velocity. These stars were usually discarded in previous similar studies. A scrutiny of our final results sheds light on selection effects present in our studies, which we propose to be the focus of future studies. In total, we find three late-type (2 G-type and 1 K-type) HVS and 21 high velocity star candidates, 3 and 11 of which are new, respectively. Judging by their historical trajectories, which we calculate, all three HVS candidates could not have had Galactic center origins. Further monitoring is required to confirm their status.
Combinations of glucose oxidase, α‐amylase and xylanase affect dough properties and bread quality

Summary The objective of this work was to study the effects of the combination of glucose oxidase (Gox), α-amylase (AM) and xylanase (Xyl) on dough properties and bread quality, applying response surface analysis. Gox improved dough stickiness and bread crumb uniformity, but had a negative effect on specific volume. A positive synergist effect was observed combining Xyl and AM on specific volume and crumb firmness but this synergist effect was negative on crumb uniformity. Using the ‘desirability function’, two optimal formulations for the baking process were obtained. In the first optimisation, the combination of Gox 0.0026, Xyl 0.016 and AM 0.01 g per 100 g of flour will allow to obtain breads with high specific volume and low firmness. Second optimisation (Gox 0.0037, Xyl 0.0089 and AM 0.0105 g per 100 g of flour) will allow to obtain breads with 37% higher specific volume than control and, additionally, with low dough stickiness.
On Detecting Hidden Third-Party Web Trackers with a Wide Dependency Chain Graph: A Representation Learning Approach

Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users privacy at risk and that's why users demand to block these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists that are built and maintained manually by a large community of users. In this work, we seek to simplify the update of these filter lists by automatic detection of hidden advertisements. Existing tracker detection approaches generally focus on each individual website's URL patterns, code structure and/or DOM structure of website. Our work differs from existing approaches by combining different websites through a large scale graph connecting all resource requests made over a large set of sites. This graph is thereafter used to train a machine learning model, through graph representation learning to detect ads and tracking resources. As our approach combines different sources of information, it is more robust toward evasion techniques that use obfuscation or change usage patterns. We evaluate our work over the Alexa top-10K websites, and find its accuracy to be 90.9% also it can block new ads and tracking services which would necessitate to be blocked further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third party tracking and advertising
Sensitivity Kernels for Inferring Lorentz Stresses from Normal-mode Frequency Splittings in the Sun

Departures from standard spherically symmetric solar models, in the form of perturbations such as global and local-scale flows and structural asphericities, result in the splitting of eigenfrequencies in the observed spectrum of solar oscillations. Drawing from prevalent ideas in normal-mode-coupling theory in geophysical literature, we devise a procedure that enables the computation of sensitivity kernels for general Lorentz-stress fields in the Sun. Mode coupling due to any perturbation requires careful consideration of self- and cross coupling of multiplets. Invoking the isolated-multiplet approximation allows for limiting the treatment to purely self coupling, requiring significantly less computational resources. We identify the presence of such isolated multiplets under the effect of Lorentz stresses in the Sun. Currently, solar missions allow for precise measurements of self coupling of multiplets via “a-coefficients” and the cross-spectral correlation signal that enables the estimation of the “structure coefficients”. We demonstrate the forward problem for both self coupling (a-coefficients) and cross coupling (structure coefficients). In doing so, we plot the self-coupling kernels and estimate a-coefficients arising from a combination of deep-toroidal and surface-dipolar axisymmetric fields. We also compute the structure coefficients for an arbitrary general magnetic field (real and solenoidal) and plot the corresponding “splitting function”, a convenient way to visualize the splitting of multiplets under 3D internal perturbations. The results discussed in this paper pave the way to formally pose an inverse problem and infer solar internal magnetic fields.