Miracle-dz/UniFAR-FTUs huggingface dataset:
T RANS NAS-B ENCH -101: I MPROVING T RANSFERRA - BILITY AND G ENERALIZABILITY OF C ROSS -T ASK N EURAL A RCHITECTURE S EARCH

Recent breakthroughs of Neural Architecture Search (NAS) are extending the field’s research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on one single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the huge computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing transferrable NAS algorithms are also based on different settings, e.g., datasets and search spaces, which raises concerns on performance comparability. Although existing NAS benchmarks provided some solutions, they all focus on one single type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark containing network performance across 7 tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among the tasks, and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search spaces: cell-level search space and macro-level search space. With 7,352 backbones evaluated on 7 tasks, 51,464 trained models with detailed training information are provided. Generating this benchmark takes about 193,760 GPU hours, which is equivalent to 22.12 years of computation on a single Nvidia V100 GPU. Analysis of 4 benchmark transfer schemes highlights that: (1) Direct deployment of both architectures and policies can easily lead to negative transfer unless guided by carefully designed mechanisms. (2) Evolutionary methods’ role in transferrable NAS might be overlooked in the past. (3) It is a valid challenge for NAS algorithms to perform well across tasks and search spaces consistently. We also provide our suggestions for future research along with the analysis. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level.
Using Carbon-awareness to Reduce the Cloud’s Environmental Impact

Carbon emissions and power are a challenge for the growth of cloud computing. We consider a carbon-aware approach to acquiring power that caps emissions hourly, adapting to the carbon content of the power grid. The objective of this approach is to reduce the cloud’s carbon footprint. Because this approach produces variable capacity, we assess performance on production cloud workloads (Microsoft, Google), exploring several dimensions of variation, dynamic range, structure, and change frequency. The robust high performance shows that carbon-aware policies need not damage cloud efficiency. With foresight and scheduler improvement, potential job impact is minimized under extreme dynamic range. Todemonstrate the potential benefits, we consider a case study with a cloud datacenter in Germany. Our results show that the constant hourly emission approach can reduce carbon footprint by up to 19% while maintaining system performance. An additional benefit is lower power cost.
A Reexamen Of Fiscal Cyclicality In Waemu

We want to characterize and explain the cyclical behaviour of fiscal policy in WAEMU countries in the period from 1980 to 2016, using system GMM as the preferred estimation method for the underlying sample and model specification.Like in other sub-Saharan countries, total publicexpenditure in the WAEMU is found to be strongly procyclical. This is most pronounced for public investment, which overreacts to output growth with elasticity above 1.We further find that financing constraints (proxied by the foreign aid-to-GDP ratio) and fiscal space (proxied by the lagged public debt-GDP-ratio) partly explain this behavior. However, the improvement in the quality of fiscal governance as well as the adoption of multilateral budgetary surveillance rules (PCSCS) conditional on the quality of fiscal governance allow member countries to conduct a less procyclical fiscal policy.
Pool Boiling on Structured Surfaces: Heat Transfer and Critical Heat Flux -Experiments and Mechanistic Modelling

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ANTI-QUORUM SENSING AND ANTITUMOR ACTIVITY OF PRUNELLA VULGARIS, SAMBUCUS NIGRA, CALENDULA OFFICINALIS: potential use in food industry

With the emergence of health related side effects of synthetic substances, the trend towards natural products has increased and has directed researchers to determine their pharmacological properties. At the same time, the resistance of the microorganisms to the antibiotics used in the treatment revealed that they should be controlled without allowing them to gain resistance. In this study, the total phenolic content, volatile composition and antioxidant, antimicrobial, anti-quorum sensing and antitumor activities of Prunella vulgaris, Sambucus nigra and Calendula officinalis extracts were determined. The antioxidant capacity of the extracts was determined by the Trolox Equivalent Antioxidant Capacity (TEAC) method, volatile component analyses were determined by GC-MS, and antimicrobial activity was determined by the disc diffusion method. Chromobacterium violaceum 026 and Agrobacterium tumefaciens A136 were used to determine the antiquorum sensing activity. Additionally, the antitumor potential of the extracts was determined by the potato disc method. Prunella vulgaris was the plant with the highest antioxidant capacity, while the extract with the highest antimicrobial activity was determined to be Sambucus nigra against Staphylococcus aureus ATCC 25923. The results showed that all extracts have anti-quorum sensing properties. Prunella vulgaris was the plant with the highest anti-quorum sensing properties. There was a correlation between the extract concentration and tumor inhibition. The Prunella vulgaris extract was found to have the highest antitumor activity. As a result, it was determined that the plants used in the study have the potential to be used in alternative medicine treatment and can be utilized for the control of microorganisms.
Sterculia guttata Roxb. As insect growth regulatory agent and its influence on developmental indices of filarial vector, Culex quinquefasciatus Say

The extensive and repeated use of synthetic insecticides disrupted natural systems leading to resistance and resurgence in target populations and destruction of non-target beneficial fauna. The present study took an effort to contribute to control or reducing the mosquito population of Culex quinquefasciatus Say by employing different column fractions of the methanolic seed extract of Sterculia guttata Roxb. The higher dose treatments caused mortality in a dose-dependent manner and it also produced various growth inhibiting effects on the developmental stages when, exposed at lower dose treatments. MeOH: EA-4:1 column fraction of the methanol extract of S. guttata exhibited significant insect growth regulatory activities on Cx. quinquefasciatus as can be evidenced from the effective interference with the life cycle stages of the filarial vector.
Synthesis and characterization of CMC/PVA/PVP composite microfiltration membrane

The aim of this study was to develop a composite membrane by phase inversion using carboxymethyl cellulose (CMC) as a basic material followed by characterization and performance testing. CMC was prepared from cellulose by alkalization and etherification of cellulose. CMC along with polyvinylpyrrolidone (PVP) and polyvinyl alcohol (PVA) were used to prepare composite membranes. Polyethylene glycol (PEG) was used as a pore forming agent. Characterization was done using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), thermo-gravimetric analysis (TGA), and universal testing machine. SEM results indicated that the addition of PVP resulted in a smoother surface with a pore size ranged 0.1–10 micron as compared to the membrane fabricated without PVP. FTIR spectroscopy gave the bands that are attributed to the dispersion of PVA and PVP in substrate matrix. Weight loss started at 270°C with PVP treatment as compared to 210°C in the absence of PVP indicating the improved thermal stability of the membrane. Salt rejection efficiency was in the following order: NaCl < KCl < CaCl2 with solutions passed through having 0.1%, 0.3%, 1.0%, and 3.0% salinity. Overall salt removal or salt rejection efficiency ranged from 27% to 37%. It was concluded that PVP had a positive effect on thermal stability, surface morphology, and mechanical properties of the membrane. The properties highlight the potential usage of the synthesized membrane in pretreatment for removal of larger particles such as macromolecules, proteins, colloids, and microbes.
A conservation strategy for khulan in Mongolia: background and key considerations

Kaczensky, P., Buuveibaatar, B. , Payne, J.C. , Strindberg, S. , Walzer, C. , Batsaikhan, N. , Bolortsetseg, S. , Victurine, R. & Olson, K.A. 2020. A conservation strategy for khulan in Mongolia: background and key considerations. NINA Report 1889. Norwegian Institute for Nature Research. Asiatic wild ass (Equus hemionus), referred to as khulan in Mongolian, are among the most mobile ungulates globally. Their movements exc ed the much better known migrations of caribou in the Arctic or wildebeest and zebra in the Serengeti-Maasai Mara ecosystem. These wide-ranging movements allow khulan to thrive in large numbers under the harsh climate and unpredictable conditions of Central Asia’s resource-poor drylands. The very same harsh climate also gave rise to the traditional nomadic herding practised by local communities. However, this need to move makes khulan extremely vulnerable to the fragmentation and loss of habitat which is currently ongoing throughout their range. In turn, this mobility makes khulan an ideal umbrella species for largely intact and functionally connected dryland ecosystems, which could benefit many other threatened dryland species, ecological processes, and the local communities that rely on them (Fig. 1). Figure 1: The ecological role of khulan in dryland ecosystems of Central Asia and the conflict potential in shared multi-use landscapes. Graphic: P. Kaczensky, Photo: E. Sos Long-distance movements and aggregations of ungulates fascinate people worldwide and safeguarding this globally declining phenomena and its associated ecosystem service has become a conservation goal in itself, formally recognized by the Convention of Migratory Species (CMS) and the International Union for Conservation of Nature (IUCN) via the IUCN Connectivity Conservation Specialist Group (https://conservationcorridor.org/ccsg/). Mongolia signed CMS in 1999 and in 2002 added the khulan to the list of CMS species.
A Nonintrusive POD Approach for High Dimensional Problems using Sparse Grids

Reduced order models are effective in reducing the computational burden of large-scale complex systems. Proper Orthogonal Decomposition (POD) is one of the most important methods for such application. Nevertheless, problems parametrized on high dimensional spaces require computations of an enormous number of simulations in the offline phase. In this paper, the use of sparse grids is suggested to select the sampling points in an efficient manner. The method exploits the hierarchical nature of the Smolyak algorithm to select the sparse grid level based on the singular values of the POD basis. Then, a nonintrusive reduced order model is built using Smolyak’s combination technique. The proposed method was tested and compared with Radial Basis Functions in two nuclear applications. The first was a one-dimensional slab solved as a diffusion eigenvalue problem and the second was the two-dimensional IAEA benchmark problem. In both cases, the results showed that while Radial Basis Functions resulted in a faster reduced order model, Smolyak’s model provided superior accuracy.
Preanalytical Factors Affecting the Mean Platelet Volume: a Review

Mean platelet volume (MPV) as an indicator of platelet activation is a subject of many studies. Since the introduction of automated hematology analyzers, many authors described mean platelet volume as a marker for different pathologic conditions. It is also known that numerous preanalytical conditions could affect MPV results. Specimens are usually tested several hours after blood collection. There are some specific hematology analyzers which are in use by the majority of hematology laboratories. This review demonstrates some important aspects related to MPV measurement in routine laboratory.
Predicting Pressure Loss for Pulse Jet Filters

The analysis of pressure loss characteristics for pulse jet filters suggests that the relationship between dust adhesion to the fabric and the opposing force generated by pulse jet action plays a major role in dust removal. Hence, fabric cleanability is examined in terms of the adhesion-cohesion forces bonding the dust to the fabric vs. the intensity and frequency of the dust dislodgement forces produced by the high energy air pulses. The effect of jet size and location, jet air volume, and the intensity (pressure) and duration of the jet pulses is related to operating pressure loss. The mechanics of energy transfer from the jet pulse to the dustladen fabric are explored in terms of jet pressure, solenoid valve action, the ratio of delivered pulse air volume to bag (tube) volume, and the elastic and flex properties of the felt bags. Effective and actual fabric dust holdings before and after cleaning are discussed with respect to steady-state dust deposition and removal rates, and operating pressure losses.
Using Attention Networks to Learn Representations for House Price Prediction

Estimating the market price of a house is important for many businesses such as real estate and mortgage lending companies. The price of a house depends not only on its structural features (e.g. area and number of bedrooms) but also the spatial context where it is located. This context can be explicitly captured, for instance, by collecting satellite images or points of interest in the neighborhood, or implicitly by looking at the price of the nearby houses. Since collecting explicit spatial context is usually costly, in this work we estimate the price of a house based solely on its structural features and the characteristics and price of its neighbors. To capture the implicit spatial context of a house, we propose a hybrid attention mechanism that weights neighbors based on their similarity in terms of their structural features and geographic location to the house. For the structural features, we apply an euclidean-based attention and, for the geographic location, we implemented an attention layer based on a radial basis function kernel. Those attention mechanisms are used by a neural network regressor to learn a vector representation of the house defined as the house embedding. This vector can then be used as a feature set by any regressor to perform house price prediction. We have performed an extensive experimental evaluation on 5 different real-world datasets that shows that: (1) regressors using house embedding obtained, in most cases, the best results on all 5 datasets; (2) the learned house embedding improves the performance of the evaluated regressors in almost all scenarios comparing to their results using raw features; (3) simple regressor models such as Linear Regression using house embedding achieved comparable results to more competitive algorithms, such as Random Forest and XGboost; (4) Our proposed solution obtains better results about the use of points of interest; (5) Our approach outperformed traditional spatial predictive models; and (6) our proposed solution outperformed previous Deep Learning approaches for house price prediction that use more costly strategies to capture the spatial context.
Measurements of NO, NO2, and HNO3 in the Stratosphere

Atmospheric models designed to predict the effect on the ozone budget of the injection of nitric oxide into the stratosphere by high-flying aircraft require a knowledge of the NO concentration in the unperturbed atmosphere. Moreover, a monitoring program will require a base line against which to measure the magnitude of any future stratospheric pollution. A number of measurements have recently been reported, not only on NO, but also on NO2, and HNO3 which are interconverted by stratospheric photochemistry. All the methods but two employ "remote-sensing" optical absorption or emission techniques. Infrared solar absorption using long optical paths at twilight has proven to be very sensitive in detecting trace atmospheric constituents but quantitative interpretation appears to be very difficult. Chemiluminescence and direct "particle" sampling have provided insitu measurements.The spread in the reported values of mixing ratios is very large for NO2, appreciable, but smaller for NO, and smallest for HNO3. Ins.
Holographic 3D Particle Imaging With Model-Based Deep Network

Gabor holography is an amazingly simple and effective approach for three-dimensional (3D) imaging. However, it suffers from a DC term, twin-image entanglement, and defocus noise. The conventional approach for solving this problem is either using an off-axis setup, or compressive holography. The former sacrifices simplicity, and the latter is computationally demanding and time-consuming. To cope with this problem, we propose a model-based holographic network (MB-HoloNet) for three-dimensional particle imaging. The free-space point spread function (PSF), which is essential for hologram reconstruction, is used as a prior in the MB-HoloNet. All parameters are learned in an end-to-end fashion. The physical prior makes the network efficient and stable for both localization and 3D particle size reconstructions.
Collective Text Classification Using Topic-enhanced Latent Textual Links

Collective text classification studies the problem of classifying a collection of documents together by exploiting their inter-document relationships such as paper citations or web hyperlinks. Previous studies typically assume that such relational data are informative for prediction, and use them in ways that highly trust their usefulness. Such assumption and reliance, however, make them easily suffer when the given relational data are actually less informative. We propose that an effective solution is to enrich more types of relational information and to consider various relational information simultaneously when modeling the text. Our work explores this possibility by exploiting latent but rich textual links among documents that can be explicitly formulated by just analyzing the text. In our framework, we first construct a heterogeneous text network to instantiate and encode various semantic relationships among documents bridged through topics and n-grams. Then, we design a Graph Edge-Attention Network to embed the structural heterogeneity and model the complex interactions between various text entities and their relationships. Experiments on multiple benchmark datasets show that our effective usage of latent textual links helps significantly improve classification performance over the state-of-the-art baselines. Various ablation studies and hyperpa-rameter analysis provide further insight into interesting properties of the latent textual links we construct.
Individual Psychology Approaches to Group Sex Offender Treatment Individual Psychology Approaches to Group Sex Offender Treatment

Traditional sex offender treatment differs from other forms of counseling due to limited emphasis on the therapeutic relationship and a greater reliance on confrontation to achieve treatment goals. Cognitive behavioral group therapy has been the popular choice for treating sex offenders, but recent trends in research show support for a holistic approach based on Adlerian psychotherapy, also known as individual psychology. Individual psychology emphasizes social interest, style of life, responsibility, feelings of superiority and inferiority, and relationship. The purpose of this article is to review recent literature supporting Adlerian approaches to treating sex offenders, as well as to demonstrate empirical support for holistic conceptualizations of sex offender treatment. Implications for therapists and suggestions for future research are also provided.
Security Benefits for Agile Software Development

-Agile methodologies such as scrum and Extreme Programming (XP) are efficient development processes by accepting changes at any phase and delivering software quickly to customers. However, these methodologies have been criticized because of the unavailability of security as an important quality goal of software systems. Although there are pre-existing research results on this topic, there is no pure approach for identifying security benefits of agile practices that relate to the core “embrace-changes” principle of agile. Specifically, we analyze agile practices to find the security benefits in customer-and developer activities. Identifying these benefits supports the secure development of software using an agile methodology.
AN ADAPTIVE NUMERICAL INTEGRATOR FOR MOLECULAR INTEGRALS

We present a new numerical integrator for molecular integrals that generates automatically an adaptive molecular grid. The tolerance of the numerical integration is the only input parameter of the integrator besides the atomic coordinates and the atomic numbers. The adaptive numerical integrator was successfully implemented in our new density-functional theory method (DFT method) ALLCHEM using the self consistent field (SCF) procedure. The accuracy of the numerical integration is superior to pruned fixed grids for a given number of grid points. The adaptive grid generator allows a very efficient optimization of the grid for an individual molecular system. It is shown that the grid accuracy increases monotonically, if the given tolerance of the numerical integration is decreased. In this way it is possible to obtain results of high numerical precision. For a given tolerance of the numerical integration the adaptive grid generator automatically adjusts the grid to the basis set and the molecular structure.