Miracle-dz/UniFAR-FTUs huggingface dataset:
The chemistry of an insect ear: ionic composition of a liquid-filled ear and haemolymphs of Neotropical katydids

The purpose of this study is to examine and to compare the ionic composition of the haemolymph and the ear fluid of seven species of katydids (Orthoptera: Tettigoniidae) with the aim of providing from a biochemical perspective a preliminary assessment for an insect liquid contained in the auditory organ of katydids with a hearing mechanism reminiscent of that found in vertebrates. A multi-element trace analysis by inductively coupled plasma optical-emission spectrometry was run for 16 elements for the ear liquid of seven species and the haemolymph of six of them. Based on the obtained results, it can be recognized that the ionic composition is variable among the studied insects, but sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+) are the most prominent of the dissolved inorganic cations. However, the ion concentrations between the two fluids are considerably different and the absence or low concentration of Ca2+ is a noticeable feature in the inner ear liquid. A potential relationship between the male courtship song peak frequency and the total ion (Na+, K+, Mg2+ and Ca2+) concentration of the inner ear liquid is also reported.
Application of the Stochastic Field Method to Multi‐Fluid Flow

The Stochastic Field Method was originally derived in the field of combustion and firstly applied to a cavitation problem by our group [1]. Randomly distributed variables exist in both combustion and cavitating problems. Calculating strong nonlinear processes using mean values as input, leads to unphysical results. The consideration of the variable's probability density function (PDF) becomes mandatory. The Stochastic Field Method approximates this PDF by samples similar to methods of Lagrangian particles where imaginary particles are introduced to the flow. In contrast to Lagrangian particles methods, these samples are represented by Eulerian fields described by stochastic partial differential equations. This pure Eulerian interpretation makes the method attractive for CFD‐codes: A coupling of an Eulerian and a Lagrangian perspective becomes obsolete which results in efficient computation times ‐ especially in the presence of a vast number of bubbles. In contrast to other Eulerian methods, the calculation of an arbitrary PDF is possible at low computational cost. The representation of samples as fields allows the visualization of PDFs within every computational cell. The first implementation into a commercial CFD‐Code is presented. In addition, industrial examples, like cavitation in an automotive injection nozzle, are shown.
Identification of PP2A-B55 targets uncovers regulation of emerin during nuclear envelope reassembly in Drosophila

Mitotic exit requires the dephosphorylation of many proteins whose phosphorylation was needed for mitosis. Protein phosphatase 2A with its B55 regulatory subunit (PP2A-B55) promotes this transition. However, the events and substrates that it regulates are incompletely understood. We used proteomic approaches in Drosophila to identify proteins that interact with and are dephosphorylated by PP2A-B55. Among several candidates, we identified emerin (otefin in Drosophila). Emerin resides in the inner nuclear membrane and interacts with the DNA-binding protein barrier-to-autointegration factor (BAF) via a LEM domain. We found that the phosphorylation of emerin at Ser50 and Ser54 near its LEM domain negatively regulates its association with BAF, lamin and additional emerin in mitosis. We show that dephosphorylation of emerin at these sites by PP2A-B55 determines the timing of nuclear envelope reformation. Genetic experiments indicate that this regulation is required during embryonic development. Phosphoregulation of the emerin–BAF complex formation by PP2A-B55 appears as a key event of mitotic exit that is likely conserved across species.
Neural ordinary differential equations with irregular and noisy data

Measurement noise is an integral part of collecting data of a physical process. Thus, noise removal is necessary to draw conclusions from these data, and it often becomes essential to construct dynamical models using these data. We discuss a methodology to learn differential equation(s) using noisy and irregularly sampled measurements. In our methodology, the main innovation can be seen in the integration of deep neural networks with the neural ordinary differential equations (ODEs) approach. Precisely, we aim at learning a neural network that provides (approximately) an implicit representation of the data and an additional neural network that models the vector fields of the dependent variables. We combine these two networks by constraints using neural ODEs. The proposed framework to learn a model describing the vector field is highly effective under noisy measurements. The approach can handle scenarios where dependent variables are unavailable at the same temporal grid. Moreover, a particular structure, e.g. second order with respect to time, can easily be incorporated. We demonstrate the effectiveness of the proposed method for learning models using data obtained from various differential equations and present a comparison with the neural ODE method that does not make any special treatment to noise. Additionally, we discuss an ensemble approach to improve the performance of the proposed approach further.
Fractures in Childhood and Young Adulthood According to Maternal Smoking in Late Pregnancy. A Danish Cohort Study

Abstract Background Fractures account for the most frequent cause of hospitalization during childhood and numbers have increased over time. Of all fractures in childhood and young adulthood, 66% are recurrent fractures, suggesting that some people are predestined for fractures. The aim of this study was to investigate the association between maternal smoking during late pregnancy and the risk of fractures in the children. Methods The study included 11,082 mothers and their children from the cohort “Healthy Habits for Two” born between 1984 and 1987. Information about maternal smoking during pregnancy came from questionnaires filled out in pregnancy, while information about fractures was derived from the Danish National Patient Registry. Over a follow-up of 24 years (1994–2018), Cox regression with multiple failures was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for fractures in childhood and young adulthood according to maternal smoking in late pregnancy. Information about body mass index (BMI) and smoking status in young adulthood was included as time variant covariates. Results During an age span of 8–32 years, 6,420 fractures were observed. Of the mothers, 39.1% smoked during late pregnancy. Compared to children of mothers who did not smoke, children of mothers who smoked 1–9 cigarettes per day and 10+ cigarettes per day had an increased risk of fractures (HR 1.14 [CI: 1.06; 1.21] and HR 1.14 [CI: 1.07; 1.22], respectively). After adjusting for BMI and smoking status in young adulthood, the findings were slightly strengthened, showing an increased risk of fractures of 23 and 25% in children of mothers smoking 1–9 cigarettes per day and 10+ cigarettes per day, respectively. Conclusion Maternal smoking during late pregnancy was associated with a higher risk of fractures in the child. This result indicates that exposure to cigarette smoke in utero may play a role in lifelong bone health.
C0-semigroups of holomorphic Carathéodory isometries in reflexive TRO

We refine earlier results concerning the structure of strongly continuous one-parameter semigroups ( C _0-SGR) of holomorphic Carathéodory isometries of the unit ball in infinite-dimensional reflexive TROs (ternary rings of operators) We achieve finite algebraic formulas for them in terms of joint boundary fixed points and Möbius charts.
Smarter Atomic Smart Pointers: Safe and Efficient Concurrent Memory Management (Abstract)

We present a technique for concurrent memory management that combines the ease-of-use of automatic memory reclamation, and the efficiency of state-of-the-art deferred reclamation algorithms. First, we combine ideas from referencing counting and hazard pointers in a novel way to implement automatic concurrent reference counting with wait-free, constant-time overhead. Second, we generalize our previous algorithm to obtain a method for converting any standard manual SMR technique into an automatic reference counting technique with a similar performance profile. We have implemented the approach as a C++ library and compared it experimentally to existing atomic reference-counting libraries and state-of-the-art manual techniques. Our results indicate that our technique is faster than existing reference-counting implementations, and competitive with manual memory reclamation techniques. More importantly, it is significantly safer than manual techniques since objects are reclaimed automatically.
Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation

Review information has been demonstrated beneficial for the explainable recommendation. It can be treated as training corpora for generation-based methods or knowledge bases for extraction-based models. However, for generation-based methods, the sparsity of user-generated reviews and the high complexity of generative language models lead to a lack of personalization and adaptability. For extraction-based methods, focusing only on relevant attributes makes them invalid in situations where explicit attribute words are absent, limiting the potential of extraction-based models. To this end, in this paper, we focus on the explicit and implicit analysis of review information simultaneously and propose novel a Topic-enhanced Graph Neural Networks (TGNN) to fully explore review information for better explainable recommendations. To be specific, we first use a pre-trained topic model to analyze reviews at the topic level, and design a sentence-enhanced topic graph to model user preference explicitly, where topics are intermediate nodes between users and items. Corresponding sentences serve as edge features. Thus, the requirement of explicit attribute words can be mitigated. Meanwhile, we leverage a review-enhanced rating graph to model user preference implicitly, where reviews are also considered as edge features for fine-grained user-item interaction modeling. Next, user and item representations from two graphs are used for final rating prediction and explanation extraction. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed TGNN with both recommendation accuracy and explanation quality.
Searching for Reliable Facts over a Medical Knowledge Base

This work presents CoreKB, a Web platform for searching reliable facts over gene expression-cancer associations Knowledge Base (KB). It provides search capabilities over an RDF graph using natural language queries, structured facets, and autocomplete. CoreKB is designed to be intuitive and easy to use for healthcare professionals, medical researchers, and clinicians. The system offers the user a comprehensive overview of the scientific evidence supporting a medical fact. It provides a quantitative comparison between the possible gene-cancer associations a particular fact can reflect.
Increasing Available Attempts: Changes in Student Correctness on Formative Introductory Physics Problems

Student submission data from formative graded physics problems were analyzed to detect effects from increasing the number of available attempts. The problems were used in two subsequent runs of MIT's general requirement introductory electricity and magnetism course, 8.02, with 190 students in the 2021 course and 203 in the 2022 course. Students completed the problems asynchronously in interactive online lessons as part of a blended learning design. The problems awarded full credit on any attempt and gave correctness as submission feedback. A number of available attempts was set for each problem by the course designers, varying across problems. Between 2021 and 2022, this number was increased for some problems but not others, creating a natural experiment. Qualitative effects were evaluated relative to a control group of unmodified problems and were found to differ between constructed response and selected response problems. Constructed response problems saw a significant decrease in first-attempt success rate and increase in rate of abandoning with attempts remaining, despite an insignificant increase in overall success rate. Selected response problems saw a significant increase in overall success but negligible changes in first-attempt success and abandoning. For both types, there was a significant increase in the overall number of attempts used. These results suggest that increasing attempts for constructed and selected response problems may encourage undesirable approaches to answering the problems, even in a formative context.
Ultrashort pulse point-by-point written aperiodic fiber Bragg gratings for suppression of OH-emission lines

We demonstrate the direct inscription of aperiodic fiber Bragg gratings (AFBGs) for their use as in-fiber filter elements. The modifications are induced by focusing ultrashort laser pulses with an oil-immersion objective into the fiber core. We apply an advanced point-by-point inscription technique for flexible period adaptation. The fabricated AFBGs are targeted on the suppression of 10 lines in a single grating and simulations based on the specific design show excellent agreement. Furthermore, we discuss the application in astronomy as filters for the suppression of OH emission lines.
Learning from Crowds with Annotation Reliability

Crowdsourcing provides a practical approach for obtaining annotated data to train supervised learning models. However, since the crowd annotators may have different expertise domain and cannot always guarantee the high-quality annotations, learning from crowds generally suffers from the problem of unreliable results of introducing some noises, which makes it hard to achieve satisfying performance. In this work, we investigate the reliability of annotations to improve learning from crowds. Specifically, we first project annotator and data instance to factor vectors and model the complex interaction between annotator expertise and instance difficulty to predict annotation reliability. The learned reliability can be used to evaluate the quality of crowdsourced data directly. Then, we construct a new annotation, namely soft annotation, which serves as the gold label during the training. To recognize the different strengths of annotators, we model each annotator's confusion in an end-to-end manner. Extensive experimental results on three real-world datasets demonstrate the effectiveness of our method.
DICE: a Dataset of Italian Crime Event news

Extracting events from news stories as the aim of several Natural Language Processing (NLP) applications (e.g., question answering, news recommendation, news summarization) is not a trivial task, due to the complexity of natural language and the fact that news reporting is characterized by journalistic style and norms. Those aspects entail scattering an event description over several sentences within one document (or more documents), applying a mechanism of gradual specification of event-related information. This implies a widespread use of co-reference relations among the textual elements, conveying non-linear temporal information. In addition to this, despite the achievement of state-of-the-art results in several tasks, high-quality training datasets for non-English languages are rarely available. This paper presents our preliminary study to develop an annotated Dataset for Italian Crime Event news (DICE). The contribution of the paper are: (1) the creation of a corpus of 10,395 crime news; (2) the annotation schema; (3) a dataset of 10,395 news with automatic annotations; (4) a preliminary manual annotation using the proposed schema of 1000 documents. The first tests on DICE have compared the performance of a manual annotator with that of single-span and multi-span question answering models and shown there is still a gap in the models, especially when dealing with more complex annotation tasks and limited training data. This underscores the importance of investing in the creation of high-quality annotated datasets like DICE, which can provide a solid foundation for training and testing a wide range of NLP models.
AutoTransfer: Instance Transfer for Cross-Domain Recommendations

Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.
An Effective Framework for Enhancing Query Answering in a Heterogeneous Data Lake

There has been a growing interest in cross-source searching to gain rich knowledge in recent years. A data lake collects massive raw and heterogeneous data with different data schemas and query interfaces. Many real-life applications require query answering over the heterogeneous data lake, such as e-commerce, bioinformatics and healthcare. In this paper, we propose LakeAns that semantically integrates heterogeneous data schemas of the lake to enhance the semantics of query answers. To this end, we propose a novel framework to efficiently and effectively perform the cross-source searching. The framework exploits a reinforcement learning method to semantically integrate the data schemas and further create a global relational schema for the heterogeneous data. It then performs a query answering algorithm based on the global schema to find answers across multiple data sources. We conduct extensive experimental evaluations using real-life data to verify that our approach outperforms existing solutions in terms of effectiveness and efficiency.
TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social Networks

Link prediction has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network (TDAN) based on transfer learning to handle the challenge. TDAN exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types. TDAN mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that TDAN can outperform the state-of-the-art models.
Look Ahead: Improving the Accuracy of Time-Series Forecasting by Previewing Future Time Features

Time-series forecasting has been actively studied and adopted in various real-world domains. Recently there have been two research mainstreams in this area: building Transformer-based architectures such as Informer, Autoformer and Reformer, and developing time-series representation learning frameworks based on contrastive learning such as TS2Vec and CoST. Both efforts have greatly improved the performance of time series forecasting. In this paper, we investigate a novel direction towards improving the forecasting performance even more, which is orthogonal to the aforementioned mainstreams as a model-agnostic scheme. We focus on time stamp embeddings that has been less-focused in the literature. Our idea is simple-yet-effective: based on given current time stamp, we predict embeddings of its near future time stamp and utilize the predicted embeddings in the time-series (value) forecasting task. We believe that if such future time information can be previewed at the time of prediction, they can be utilized by any time-series forecasting models as useful additional information. Our experimental results confirmed that our method consistently and significantly improves the accuracy of the recent Transformer-based models and time-series representation learning frameworks. Our code is available at: https://github.com/sunsunmin/Look_Ahead
A Preference Judgment Tool for Authoritative Assessment

Preference judgments have been established as an effective method for offline evaluation of information retrieval systems with advantages to graded or binary relevance judgments. Graded judgments assign each document a pre-defined grade level, while preference judgments involve assessing a pair of items presented side by side and indicating which is better. However, leveraging preference judgments may require a more extensive number of judgments, and there are limitations in terms of evaluation measures. In this study, we present a new preference judgment tool called JUDGO, designed for expert assessors and researchers. The tool is supported by a new heap-like preference judgment algorithm that assumes transitivity and allows for ties. An earlier version of the tool was employed by NIST to determine up to the top-10 best items for each of the 38 topics for the TREC 2022 Health Misinformation track, with over 2,200 judgments collected. The current version has been applied in a separate research study to collect almost 10,000 judgments, with multiple assessors completing each topic. The code and resources are available at https://judgo-system.github. io.
Numerical modeling and optimization study for the geometry of film cooling holes

APA Citation Alshehaby, M. (2019). Numerical modeling and optimization study for the geometry of film cooling holes [Master’s thesis, the American University in Cairo]. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/535 MLA Citation Alshehaby, Mohammad Mahmoud. Numerical modeling and optimization study for the geometry of film cooling holes. 2019. American University in Cairo, Master's thesis. AUC Knowledge Fountain. https://fount.aucegypt. edu/etds/535