Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-layer graphene doped with boron to enhance ammonium hydroxide vapour detection at low temperature

Few-layer graphene and multilayer graphene are grown using atmospheric pressure chemical vapour deposition method with polystyrene as a solid carbon source on copper foil. These experimentations demonstrate that the sensor made of few layer graphene compared to multilayer graphene shows 12.5 times higher sensitivity to the vapour of ammonium hydroxide. Doping of a few layer graphene by boron is done by using boric acid as a solid precursor. The sensitivity of the few layer graphene sensor after 20 min has increased by 4.7 times when the graphene layer was doped with boron atoms (∼2.1%) for ammonium hydroxide detection. Selectivity of the boron doped sensor for ammonium hydroxide is about 16 times more than the presence of ethanol and methanol vapours. All measurements were done at room temperature and in air ambient. Atomic force microscopy, X-ray photoelectron spectroscopy and Raman spectroscopy have been used for characterisation of the layers. The dynamic response of the sensor is well fitted with Freundlich isotherm.
CHANGES IN FEMALE EMPLOYMENT IN MEXICO: DEMOGRAPHICS, MARKETS AND POLICIES CAMBIOS EN LA PARTICIPACIÓN FEMENIL EN MÉXICO: DEMOGRAFÍA, MERCADOS

Resumen: Después de la crisis económica de 2008, las brechas de género en desempleo y participación laboral se redujeron en México. Para identificar los determinantes que permitieron dicha reducción, estimamos un modelo probit con datos de 2007 y 2017 de la Encuesta Nacional de Ocupación y Empleo (ENOE), año en que la tasa de desempleo regresó a niveles precrisis. Los resultados sugieren que: el ingreso de mujeres entre 36 y 65 años al mercado laboral permitió el crecimiento general de la participación laboral femenil, la educación de las mujeres contrarresta cualquier caracteŕıstica individual o del hogar afectandonegativamente su empleabilidad, y el acceso a estancias infantiles significativamente aumenta la probabilidad de que estén empleadas.
Limited Associativity Makes Concurrent Software Caches a Breeze

Software caches optimize the performance of diverse storage systems, databases and other software systems. Existing works on software caches automatically resort to fully associative cache designs. Our work shows that limited associativity caches are a promising direction for concurrent software caches. Specifically, we demonstrate that limited associativity enables simple yet efficient realizations of multiple cache management schemes that can be trivially parallelized. We show that the obtained hit ratio is usually similar to fully associative caches of the same management policy, but the throughput is improved by up to x5 compared to production-grade caching libraries, especially in multi-threaded executions.
Sequential Diagnosis Prediction with Transformer and Ontological Representation

Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient’s interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient’s visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient’s visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository 1.1Github repository: https://github.com/Xueping/SETOR
Full Factorial Design for Production of Methyl 3-(3,5-Di-Tert-Butyl-4-Hydroxyphenyl) Propionate Using Oil Palm Frond Juice as a Sole Substrate by Ceratocystis fimbriata

Oil palm frond (OPF) juice has been known as a good source of sugars to replace commercial sugars to produce methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate. The aim of this study was to investigate the effect of temperature (°C), initial pH medium, agitation speed (rpm) and concentration of glucose (g/L) on the production of methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate from OPF juice by using Ceratocystis fimbriata (C. fimbriata). The design of experiment (DOE) method was used to screen the best parameters affecting the production of methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate. The Gas Chromatography-Mass Spectroscopy (GC-MS) with Solid Phase Micro Extraction (SPME) was used to analyze and separate the peak of methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate. Based on the GC-MS analysis, it showed that the most favorable condition for methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate production was achieved at an initial pH medium (8), agitation speed (100 rpm), temperature (25°C) and 30 g/L of glucose in OPF juice. Methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate were produced when the retention time was 32.80 min, the relative peak area was 0.24 % of chromatogram area. This result showed the great potential usage of OPF juice as a substrate to produce methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate by C. fimbriata.
Improving Dietary Assessment Via Integrated Hierarchy Food Classification

Image-based dietary assessment refers to the process of determining what someone eats and how much energy and nutrients are consumed from visual data. Food classification is the first and most crucial step. Existing methods focus on improving accuracy measured by the rate of correct classification based on visual information alone, which is very challenging due to the high complexity and inter-class similarity of foods. Further, accuracy in food classification is conceptual as description of a food can always be improved. In this work, we introduce a new food classification framework to improve the quality of predictions by integrating the information from multiple domains while maintaining the classification accuracy. We apply a multi-task network based on a hierarchical structure that uses both visual and nutrition domain specific information to cluster similar foods. Our method is validated on the modified VIPER-FoodNet (VFN) food image dataset by including associated energy and nutrient information. We achieve comparable classification accuracy with existing methods that use visual information only, but with less error in terms of energy and nutrient values for the wrong predictions.
Statistical Model Checking of Common Attack Scenarios on Blockchain

Blockchain technology has developed significantly over the last decade. One of the reasons for this is its sustainability architecture, which does not allow modification of the history of committed transactions. That means that developers should consider blockchain vulnerabilities and eliminate them before the deployment of the system. In this paper, we demonstrate a statistical model checking approach for the verification of blockchain systems on three real-world attack scenarios. We build and verify models of DNS attack, double-spending with memory pool flooding, and consensus delay scenario. After that, we analyze experimental results and propose solutions to avoid these kinds of attacks.
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization

First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning. Variance reduction for these algorithms has become an important research topic. In contrast to common approaches, which rarely leverage global models of the objective function, we exploit convexity and L-smoothness to improve the noisy estimates outputted by the stochastic gradient oracle. Our method, named COCO denoiser, is the joint maximum likelihood estimator of multiple function gradients from their noisy observations, subject to co-coercivity constraints between them. The resulting estimate is the solution of a convex Quadratically Constrained Quadratic Problem. Although this problem is expensive to solve by interior point methods, we exploit its structure to apply an accelerated first-order algorithm, the Fast Dual Proximal Gradient method. Besides analytically characterizing the proposed estimator, we show empirically that increasing the number and proximity of the queried points leads to better gradient estimates. We also apply COCO in stochastic settings by plugging it in existing algorithms, such as SGD, Adam or STRSAGA, outperforming their vanilla versions, even in scenarios where our modelling assumptions are mismatched.
Analysis of Dampers in Time-Sensitive Networks With Non-Ideal Clocks

Dampers are devices that reduce delay jitter in the context of time-sensitive networks, by delaying packets for the amount written in packet headers. Jitter reduction is required by some real-time applications; beyond this, dampers have the potential to solve the burstiness cascade problem of deterministic networks in a scalable way, as they can be stateless. Dampers exist in several variants: some apply only to earliest-deadline-first schedulers, whereas others can be associated with any packet schedulers; some enforce FIFO ordering whereas some others do not. Existing analyses of dampers are specific to some implementations and some network configurations; also, they assume ideal, non-realistic clocks. In this paper, we provide a taxonomy of all existing dampers in general network settings and analyze their timing properties in presence of non-ideal clocks. In particular, we give formulas for computing residual jitter bounds of networks with dampers of any kind. We show that non-FIFO dampers may cause reordering due to clock non-idealities and that the combination of FIFO dampers with non-FIFO network elements may very negatively affect the performance bounds. Our results can be used to analyze timing properties and burstiness increase in any time-sensitive network, as we illustrate on an industrial case-study.
FixMe: A GitHub Bot for Detecting and Monitoring On-Hold Self-Admitted Technical Debt

Self-Admitted Technical Debt (SATD) is a special form of technical debt in which developers intentionally record their hacks in the code by adding comments for attention. Here, we focus on issue-related "On-hold SATD", where developers suspend proper implementation due to issues reported inside or outside the project. When the referenced issues are resolved, the On-hold SATD also need to be addressed, but since monitoring these issue reports takes a lot of time and effort, developers may not be aware of the resolved issues and leave the On-hold SATD in the code. In this paper, we propose FixMe, a GitHub bot that helps developers detecting and monitoring On-hold SATD in their repositories and notify them whenever the On-hold SATDs are ready to be fixed (i.e. the referenced issues are resolved). The bot can automatically detect On-hold SATD comments from source code using machine learning techniques and discover referenced issues. When the referenced issues are resolved, developers will be notified by FixMe bot. The evaluation conducted with 11 participants shows that our FixMe bot can support them in dealing with On-hold SATD. FixMe is available at https://www.fixmebot. app/ and FixMe's VDO is at https://youtu.be/YSz9kFxN_YQ.
Lab-Scale Alkaline Water Electrolyzer for Bridging Material Fundamentals with Realistic Operation

Hydrogen produced by water electrolysis with renewable electricity is a reliable, affordable and environmental friendly energy carrier for future energy supply and storage. Alkaline water electrolysis is a well matured technique and proved to be suitable for large-scale applications. Materials development for alkaline water electrolyzers is still of interest for academia and industry to address the issues of low compatibility to renewable power sources. A lab-scale system for alkaline water electrolysis was developed, aiming to advance materials development and to bridge the intrinsic properties of materials with their performance under realistic operating conditions. As the smallest pressure-type electrolyzer, it is capable of working at 30 bar and 80 °C with continuous liquid electrolyte circulation. Experimental studies investigate the influence of temperature, pressure, and intrinsic properties of materials on voltage efficiency and hydrogen purity. With appropriate analysis, links between material sp.
A Digital Smart City for Emerging Mobility Systems

The increasing demand for emerging mobility systems with connected and automated vehicles has imposed the necessity for quality testing environments to support their development. In this paper, we introduce a Unity-based virtual simulation environment for emerging mobility systems, called the Information and Decision Science Lab's Scaled Smart Digital City (IDS 3D City), intended to operate alongside its physical peer and its established control framework. By utilizing the Robot Operation System, AirSim, and Unity, we constructed a simulation environment capable of iteratively designing experiments significantly faster than it is possible in a physical testbed. This environment provides an intermediate step to validate the effectiveness of our control algorithms prior to their implementation in the physical testbed. The IDS 3D City also enables us to demonstrate that our control algorithms work independently of the underlying vehicle dynamics, as the vehicle dynamics introduced by AirSim operate at a different scale than our scaled smart city. Finally, we demonstrate the behavior of our digital environment by performing an experiment in both the virtual and physical environments and comparing their outputs.
On the Out-of-distribution Generalization of Probabilistic Image Modelling

Out-of-distribution (OOD) detection and lossless compression constitute two problems that can be solved by the training of probabilistic models on a first dataset with subsequent likelihood evaluation on a second dataset, where data distributions differ. By defining the generalization of probabilistic models in terms of likelihood we show that, in the case of image models, the OOD generalization ability is dominated by local features. This motivates our proposal of a Local Autoregressive model that exclusively models local image features towards improving OOD performance. We apply the proposed model to OOD detection tasks and achieve state-of-the-art unsupervised OOD detection performance without the introduction of additional data. Additionally, we employ our model to build a new lossless image compressor: NeLLoC (Neural Local Lossless Compressor) and report state-of-the-art compression rates and model size.
The Effectiveness of Health Education in Improving Knowledge about Hypoglycemia and Insulin Pen Use among Outpatients with Type 2 Diabetes Mellitus at a Primary Care Hospital in Vietnam

Background Patients with type 2 diabetes mellitus (T2DM) who have limited knowledge about hypoglycemia and insulin pen use are likely to have hypoglycemia and other complications. Objective This study aimed to evaluate the effectiveness of health education on knowledge about hypoglycemia and insulin pen use among outpatients with T2DM at a primary care hospital in Vietnam. Methods A pretest–posttest study was conducted among 80 patients with T2DM at District 11 Hospital in Ho Chi Minh City, Vietnam. At baseline, patients were interviewed through a predefined, structural questionnaire to assess their knowledge about hypoglycemia and insulin pen use. After that, patients underwent an individual health education session about hypoglycemia and insulin pen. One month and two months after this intervention, knowledge about hypoglycemia and insulin pen use were recorded again. Results The majority were males (65.0%) and the mean age was 59.6 (standard deviation 8.1, range 35-75) years. Very few patients had good knowledge and proper insulin pen use, with percentages ranging from 13.8% to 60%. There was a significant improvement of knowledge and practice after the intervention. Such improvement remained high one month and two months after the intervention. Conclusions The health education intervention is effective in improving knowledge and practice in this population. There is a pressing need for such intervention at primary care hospitals to optimize treatment for patients with T2DM, possibly focusing on those who had characteristics to have the best effectiveness found in this study.
Tibial Tubercle Apophyseal Stage to Determine Skeletal Age in Pediatric Patients Undergoing ACL Reconstruction: A Validation and Reliability Study

Background: Anterior cruciate ligament (ACL) injuries demand individualized treatments based on an accurate estimation of the child’s skeletal age. Wrist radiographs, which have traditionally been used to determine skeletal age, have a number of limitations, including cost, radiation exposure, and inconvenience. Purpose: To evaluate the reliability and validity of a radiographic staging system using tibial apophyseal landmarks as hypothetical proxies for skeletal age to use in the preoperative management of pediatric ACL tears. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: The study included children younger than 16 years of age who underwent ACL reconstruction between July 2008 and July 2018 and received both skeletal age radiography and knee radiography within 3 months of each other. Skeletal age was calculated from hand and wrist radiographs using the Greulich and Pyle atlas. Tibial apophyseal staging was categorized into 4 stages: cartilaginous stage (stage 1), apophyseal stage (stage 2), epiphyseal stage (stage 3), and bony/fused stage (stage 4). Data were collected by 2 independent assessors. The analysis was repeated 1 month later with the same assessors. We calculated descriptive statistics, measures of agreement, and the correlation between skeletal age and apophyseal stage. Results: The mean chronological age of the 287 patients included in the analysis was 12.9 ± 1.9 years; 164 (57%) of the patients were male. The overall Spearman r between skeletal age and tibial apophyseal staging was 0.69 (0.77 in males; 0.60 in females). The interrater reliability for the tibial apophyseal staging was substantial (Cohen κ = 0.66), and the intrarater reliability was excellent (Cohen κ = 0.82). The interrater reliability for skeletal age was excellent (intraclass correlation coefficient [ICC] = 0.93), as was the intrarater reliability (ICC = 0.97). Conclusion: The observed correlation between skeletal age and tibial apophyseal staging as well as observed intra- and interrater reliabilities demonstrated that tibial apophyseal landmarks on knee radiographs may be used to estimate skeletal age. This study supports the validity of knee radiographs in determining skeletal age and provides early evidence in certain clinical presentations to simplify the diagnostic workup and operative management of pediatric knee injuries, including ACL tears.
Core motifs predict dynamic attractors in combinatorial threshold-linear networks

Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. In prior work, we have developed a detailed mathematical theory relating stable and unstable fixed points of CTLNs to graph-theoretic properties of the underlying network. Here we find that a special type of fixed points, corresponding to core motifs, are predictive of both static and dynamic attractors. Moreover, the attractors can be found by choosing initial conditions that are small perturbations of these fixed points. This motivates us to hypothesize that dynamic attractors of a network correspond to unstable fixed points supported on core motifs. We tested this hypothesis on a large family of directed graphs of size n = 5, and found remarkable agreement. Furthermore, we discovered that core motifs with similar embeddings give rise to nearly identical attractors. This allowed us to classify attractors based on structurally-defined graph families. Our results suggest that graphical properties of the connectivity can be used to predict a network’s complex repertoire of nonlinear dynamics.
Commonsense Reasoning: Benchmarks and Resources

Commonsense knowledge is the basic level of practical knowledge. It concerns everyday situations and events in the world and is commonly shared among most people. For example, it’s ok to keep the closet door open, but it’s not ok to keep the fridge door open as the food inside might go bad. There is some exceptional cases like when there is no food in the refrigerator, but this statement is considered as commonsense because it’s agreed by most people. Humans need commonsense knowledge to live and interact with each other in a reasonable and safe way, and Artificial Intelligence (AI) needs commonsense to understand humans better.
Rethinking Crowdsourcing Annotation: Partial Annotation With Salient Labels for Multilabel Aerial Image Classification

Annotated images are required for supervised model training and evaluation in aerial image classification. Manually annotating images is arduous and expensive, especially for aerial images, which often cover a large land area with multiple labels. A recent trend for conducting such annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers [e.g., annotation volunteers for Open Street Map (OSM)] online from scratch. However, for crowdsourcing image annotations, the quality cannot be guaranteed, and incompleteness and incorrectness are two major concerns. To address such concerns, we have a rethinking of crowdsourcing annotations: our simple hypothesis is that if annotators only partially annotate multilabel images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show that a multilabel aerial image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our contributions are twofold: an active learning way is proposed to acquire salient labels for multilabel aerial images and a novel adaptive temperature associated model (ATAM) specifically using partial annotations is proposed for multilabel aerial image classification. When tested on practical crowdsourcing aerial data, the OSM dataset, the proposed ATAM can achieve higher accuracy than state-of-the-art classification methods trained on fully annotated images. The proposed idea is promising for crowdsourcing aerial image annotation. Our code will be publicly available.