Miracle-dz/UniFAR-FTUs huggingface dataset:
S COTCH : A S EMANTIC C ODE S EARCH E NGINE FOR IDE S

Code search is the task of finding relevant code snippets given a natural language query. In order to facilitate real time code search, we introduce Scotch, a semantic code search tool that runs within an IDE. The semantic nature of code search in Scotch allows us to leverage the semantic meaning of code via learned vector representations, while the in-IDE nature helps to improve developers’ productivity by eliminating the need to navigate to web-browsers to search for code. The query used for code search is oftentimes ambiguous without the surrounding context of the search. In direct contrast to traditional search engines tailored to take a single line of input, the in-IDE nature of Scotch allows it to automatically infer code context during search and utilize it for search results. Hence, we propose the task ‘contextual code search’ and present an analysis of how this code context can help improve the relevance of search results. Since no existing dataset could fit our task of contextual code search, we collect and contribute a dataset of about 19M functions from GitHub repositories with permissive licenses, which is the first large-scale dataset openly available for the task of contextual code search. 1 We also present a manually-curated test set to assess the code ranking quality for code search in four programming languages. We finetune the CodeBERT model (Feng et al., 2020) to perform code search given a natural language query, with and without surrounding code context. Results from automated as well as human evaluation suggest that the inclusion of code context in search significantly improves the retrieval of the correct code snippet but slightly impairs ranking quality among code snippets. Our work provides motivation and resources for future research into contextual code search. Our code and models are available at https:
Examining neural network behavior at the classiﬁcation boundary

We evaluate the classiﬁcation of both human volunteers and various neural network models on a set of GAN-generated images that reﬂect the transition from one MNIST class to another. We ﬁnd that models that obtain the same test accuracy on the standard MNIST test data set exhibit different behavior on these images. Further, we ﬁnd that although the number of misclassiﬁed images decreases with test accuracy, the spread in predictions over multiple runs on images that are difﬁcult to classify (for humans) also decreases with test accuracy. Our results raise the question of how we want networks to behave on images that could plausibly belong to multiple classes and hint at the value of complementing test accuracy with other evaluation metrics.
Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.
Case Report: Silent β-thalassemia: Transition Mutation of the β-globin Gene (Promoter nt-101 C>T)

Introduction: Heterozygote β-thalassemia is called carrier or β-thalassemia trait (BTT). Carriers have no clinical symptoms but sometimes have a mild anemia. They can often be identified with MCV<80 fl, MCH<27 pg and HbA2> 3.5 %. However, these tests are not enough to diagnose some unexpected beta-globin mutations in premarital or prenatal screening. Case Presentation: The mentioned case was one of the most common silent β-thalassemia mutations (promoter nt-101C>T). Conclusion: It was the first report from Fars (Iran) and the second one from Iran. The case had normal hematologic indices and borderline hemoglobin A2 values that may be mistakenly interpreted as normal. The presented case showed that electrophoresis and PCR sequencing methods should be applied for screening thalassemia. Cite this article as: Gharehdaghi Z, Hosseinpouri Obeidi N. Silent β-thalassemia: Transition Mutation of the β-globin Gene (Promoter 13:E36666.
Detection of Leishmania major using PCR-ELISA

Background and objectives : Cutaneous leishmaniasis is endemic in most areas of Iran, and the diagnosis of its species is essential for controlling the disease. Leishmania major is the causative agent for cutaneous leishmaniasis in humans. Molecular methods are generally more sensitive than microscopic methods. The present study aimed to use a polymerase chain reaction-enzyme linked immunosorbent assay (PCR-ELISA) technique for detecting live L. major from wounds of patients with cutaneous leishmaniasis. Methods : In the present study, a standard strain of L. major promastigotes was used as the positive control for purification of DNA. The Novy–MacNeal–Nicolle and RPMI-1640 media were used for reproduction of parasites. DNA was isolated from specimens taken from 35 patients with suspected cutaneous leishmaniasis whose disease was confirmed by direct smear method. The PCR-ELISA technique was later applied by using the standard strain, patient specimens, and primers specific for the 18s rRNA. Results : Out of 35 patients, 17 (48.6%) were male and 18 (51.4%) were female. In addition, 8.6% of the patients lived in the Gonbad-e Kavus County, while all patients had been infected in villages around Gonbad-e Kavus. Of 35 patients with confirmed cutaneous leishmaniasis according to the direct smear method, 31 patients (86.31%) had leishmaniasis based on the PCR method and the PCR-ELISA methods. Conclusion : Based on the results, the PCR-ELISA method is more sensitive and accurate for detecting L. major .
The Effect of Mindfulness-based Cognitive Therapy on State Anxiety among Infertile Women under IVF Treatment

The Effect of Mindfulness-based Cognitive Therapy on State Abstract Background and Objective: Infertile women often use laboratory fertilization (IVF-ET), a stressful process, to fulfill their desire for childbearing. The aim of this study was to determine the effect of mindfulness-based cognitive therapy training on state anxiety in infertile women undergoing IVF treatment. Materials and Methods: This quasi-experimental study was conducted with participation of 60 women with a history of infertility who were referred to an infertility treatment center in Mashhad and were selected via convenience sampling method and then randomly assigned to two experimental and control groups. The experimental group received eight sessions of 2-hour mindfulness training weekly, while the control group did not receive any psychological services. The two groups completed the questionnaires before and after the intervention. Three months later, the test group completed the questionnaire again to track the effects of the educational intervention. Spielberger's state anxiety questionnaire was used to measure anxiety. Data analysis was performed using single-variable covariance via SPSS software version 21. Results: The mean (SD) age of the experimental group was 31.1 (4.9), and the control group 31.2 (5.1). The mean (SD) score of state anxiety in the experimental group decreased from 44.1 (12.8) in the pre-test to 41.4 (11.1) in the post-test and 40.8 (11.1) in the follow-up stage (P<0.001). However, the mean (SD) score of state anxiety in the control group decreased from 47.1 (12.3) in the pre-test to 46.5 (12.1) in the post-test and 46.2 (12.1) in the follow-up stage, which was not statistically significant. Conclusion: The study showed that mindfulness-based cognitive therapy training reduced state anxiety among infertile women undergoing IVF treatment.
SAID: State-aware Defense Against Injection Attacks on In-vehicle Network

Modern vehicles are equipped with many ECU s (Electronic Control Unit) that are connected to the IVN (In-Vehicle Network) for controlling the vehicles. Meanwhile, various interfaces of vehicles, such as OBD-II port, T-Box, sensors, and telematics, implement the interaction between the IVN and external environment. Although rich value-added functionalities can be provided through these interfaces, such as diagnostics and OTA (Over The Air) updates, the adversary may also inject malicious data into IVN , thus causing severe safety is-sues. Even worse, existing defense approaches mainly focus on detecting the injection attacks launched from IVN , such as malicious/compromised ECU s, by analyzing CAN frames, and cannot defend against the higher layer MIA s (Message Injection Attacks) that can cause abnormal vehicle dynamics. In this paper, we propose a new state-aware abnormal message injection attack defense approach, named S AID . It detects the abnormal data to be injected into IVN by considering the data semantics and the vehicle dynamics and prevents the MIA s launched from devices connected to the vehicles, such as the compromised diagnostic tools and T-boxes. We develop a prototype of S AID for defending against MIA s and evaluate it using both real road data and simulation data. The experimental results show that S AID can defend against more than 99% of the network and service layer attack trafﬁc and all state layer MIA s, effectively enforcing the safety of vehicles.
IR upgrade with quadrupoles first and dipoles first

We review recent work on the LHC IR upgrades by the US-LARP collaboration. There are several optics designs under consideration – each design differs in the potential luminosity reach, accelerator physics, operational and technical challenges etc. Here we consider two main issues: (i) the likely benefits of moving the IR magnets closer to the IP for both quadrupole first and dipole first designs and (ii) the impact of beam-beam interactions in the two designs. We conclude with a summary of accelerator physics parameters for the two designs.
Who is the Most Diligent Employee? A Discriminate Model for e-Government Service Time Analyzing

This paper discusses the human-related factors contributing to the service time for government services. We build a discriminate model to extract the subjective factor, e.g. diligence, and objective factor from the working behaviors in logs. This model can be trained by maximum likeness estimation (MLE) to get the parameters which denote the factors. To evaluate the accuracy of our model, we conduct an external experiment on a real dataset from the workflow system employed in the government of Hangzhou City in China, which results in 2,367,598 logs from 400 activities and 732 employees in two years. The experiment result not only proves the correctness of our model but also draws some interesting conclusions about the diligence of different people in different ages and genders.
Improving hydrologic models for predictions and process understanding using neural ODEs

Abstract. Deep learning methods have frequently outperformed conceptual hydrologic models in rainfall-runoff modelling. Attempts of investigating such deep learning models internally are being made, but the traceability of model states and processes and their interrelations to model input and output is not yet fully understood. Direct interpretability of mechanistic processes has always been considered an asset of conceptual models that helps to gain system understanding aside of predictability. We introduce hydrologic neural ordinary differential equation (ODE) models that perform as well as state-of-the-art deep learning methods in stream flow prediction while maintaining the ease of interpretability of conceptual hydrologic models. In neural ODEs, internal processes that are represented in differential equations, are substituted by neural networks. Therefore, neural ODE models enable the fusion of deep learning with mechanistic modelling. We demonstrate the basin-specific predictive performance for 569 catchments of the continental United States. For exemplary basins, we analyse the dynamics of states and processes learned by the model-internal neural networks. Finally, we discuss the potential of neural ODE models in hydrology.
PRBM_A_365972 1325..1346

1School of Management, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, People’s Republic of China; 2School of Management, Zhejiang Shuren University, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Commerce & Business, Government College University Faisalabad, Layyah Campus, Layyah, Punjab, 31200, Pakistan; 4Faculty of Management Sciences, International Islamic University, Islamabad, Pakistan; 5Faculty of Economics, Administrative and Social Sciences, Nisantasi University, Istanbul, 34485, Turkey; 6Department of Business Administration, National College of Business Administration and Economics, Multan Campus, Multan, 60000, Pakistan
stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts

Abstract. Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land–atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series; however, no such model exists for the generation of a stochastically plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1∘ resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability in hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate any number of synthetic series of randomised hourly PET for a specific climate scenario at any point of the global land surface between 55∘ N and 55∘ S. In addition to supporting a stochastic analysis of historical PET, stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include (1) a user-defined percentage increase in annual PET, (2) a step change in PET based on a unit increase in temperature, and (3) the extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at 12 locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET and an independent PET dataset from CRU, thereby capturing their diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide examples of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources with minimal computational demand.
Aluminum Alloys Anodisation for Nanotemplates Application

The aim of this investigation was to reveal the processing differences in achieving nanoporous anodized aluminium from aluminium alloys and their application for cobalt nanowires electrodeposition. The following types of aluminium were tested: pure Al (99.99%), commercial AA1050 alloy, commercial 6082 alloy and commercial 6060 alloy. Because of the differences in the surface temperature and high voltages during the anodizing steps, some stresses can be built up in the material. Therefore a strict temperature control should be was done to limit thermal stresses in materials. Alloying elements (Si, Mg) cause precipitates that are observed on the surface, especially for 6060 alloy. Nevertheless, a nanoporous structure can be obtained at the end of second anodization step on all aluminium alloys investigated. It was shown that the number density of pores on the surface is practically independent on the aluminium alloys used. However, the degree of hexagonal distribution of the pores depends on the type of anodized aluminium alloy. Also, a successful fabrication of Co nanowire arrays using nanoporous anodic alumina template produced on Al alloy has been demonstrated, and the uniform filling of the template by cobalt nanowires arrays is discussed.
Calibration of groundwater seepage on the spatial distribution of the stream network to assess catchment-scale hydraulic conductivity

To supplement the use of hydraulic tests and assess catchment-scale hydraulic conductivity ( 𝐾 ), we propose a methodology for shallow aquifers only based on the Digital Elevation Model (DEM) and on the observation of the stream network. The 15 methodology requires the groundwater system to be a main determinant of the stream density and extension. It assumes that the perennial stream network is set by the intersection of the groundwater table with the topography. The topographical structures and the subsurface hydraulic conductivity divided by the recharge rate 𝐾/𝑅 determine the groundwater table depth and the development of the stream network. Using a parsimonious 3D groundwater flow model, we calibrate 𝐾/𝑅 by minimizing newly defined distances between the simulated groundwater seepage zones and the observed stream network. 20 Deployed on 24 selected headwater catchments from 12 to 141 km 2 located in north-western France, the method successfully matches the stream network in 80% of the cases and provides catchment-scale hydraulic conductivities between 9 x 10 -6 and 9 x 10 -5 m s -1 for shallow aquifers sedimentary and crystalline rocks. Results show a high sensitivity of 𝐾 to the density and extension of the low-order streams and limited impacts of the DEM resolution as long the DEM remains consistent with the stream network observations. With the emergence of global remote-sensing databases combining information of high- 25 resolution DEM and stream network, this approach will contribute to assess hydraulic properties of We investigate the capacity to calibrate the catchment-scale effective hydraulic conductivities of shallow aquifers from the observed stream network with the help of a parsimonious 3D groundwater model to link groundwater flows and surface 80 observations. We propose a novel performance criterion to assess the similarity between the simulated seepage areas and the observed stream network. We present the full methodology and its sensitivity to different stream network observation products on 24 catchments covering various geological contexts in north-western France. We discuss its relevance and perspectives to extensively characterize shallow aquifers from continuous surface information as proposed by Gleeson et al. (2021). 24). these issues and 13 within observatories and (ZAAr)” network CZO”). None of these catchments present reservoir or stream obstacle that would alter stream network. The study sites cover 5 major lithologies including: Brioverian schist (sedimentary rock), Paleozoic sandstone and schist (sedimentary rock), plutonic rocks granite), micaschist (metamorphic sedimentary rock) and limestone (sedimentary rock). Sites have a homogeneous lithology (1:1 000 000 scale) throughout the catchment except for 5 sites (Sites 7, 8, 17, 18, 19) that present 2
Few-camera Dynamic Scene Variational Novel-view Synthesis

Structure from motion (SfM) enables us to reconstruct a scene via casual capture from cameras at different viewpoints, and novel view synthesis (NVS) allows us to render a captured scene from a new viewpoint. However, in the casual setting, dynamic scenes are hard: SfM can produce noisy and spatio-temporally sparse reconstructed point clouds, and so the resulting NVS often produces spatio-temporally inconsistent effects. We consider SfM and NVS parts together to ease the challenge. First, for SfM, we recover stable camera poses, then we relax the requirement for temporally-consistent points across the scene and reconstruct only a sparse point cloud per timestep that is noisy in space-time. Second, for NVS, we present a variational diffusion formulation on depths and colors that lets us robustly cope with the noise by enforcing spatio-temporal consistency via per-pixel reprojection weights derived from the input views. Together, these parts let us generate novel views for dynamic scenes without requiring challenging spatio-temporally consistent reconstructions, which expands the kinds of scenes to which these techniques can be applied. We demonstrate our algorithm on real-world dynamic scenes against recent baseline approaches.
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases

Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. However, such a one-size-fits-all strategy may not perform well since complex questions exhibit an uneven distribution in many dimensions, such as question types, involved KB relations, and query structures, resulting in insufficient learning for long-tailed samples under different dimensions. To address this problem, we propose a meta-learning framework for complex question generation. The meta-trained generator can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples through a few most related training samples. To retrieve similar samples for each input query, we design a self-supervised graph retriever to learn distributed representations for samples, and contrastive learning is leveraged to improve the learned representations. We conduct experiments on both WebQuestionsSP and ComplexWebQuestion, and results on long-tailed samples of different dimensions have been significantly improved, which demonstrates the effectiveness of the proposed framework.