Miracle-dz/UniFAR-FTUs huggingface dataset:
Matching Biomedical Knowledge Graphs with Neural Embeddings

Knowledge graphs are data structures which became essential to organize biomedical data produced at an exponential rate in the last few years. The broad adoption of this method of structuring and describing data resulted in the increased interest to develop data mining approaches which took advantage of these information structures in order to improve scientific knowledge. However, due to human idiosyncrasy and also the impossibility to isolate knowledge domains in separate pieces, knowledge graphs constructed by different individuals often contain equivalent concepts described differently. This obstructs the path to an integrated analysis of data described by multiple knowledge graphs. Multiple knowledge graph matching systems have been developed to address this challenge. Nevertheless, the performance of these systems has stagnated in the last four years, despite the fact that they were provided with highly tailored algorithms and external resources to tackle this task. In this dissertation, we present two novel knowledge graph matching approaches employing neural embeddings: one using plain embedding similarity based on word and graph models; the other one using a more complex word-based model which requires training data to refine embeddings. The proposed methodology aims to integrate these approaches in the regular matching process, using the AgreementMakerLight system as a foundation. These new components enable the extension of the system’s current matching algorithms, discovering new mappings, and developing a more generalizable and less depen-dent on external biomedical ontologies matching procedure. This new methodology was evaluated on three biomedical ontology matching test cases provided by the Ontology Alignment Evaluation Initiative. The results showed that despite both embedding approaches don’t exceed state of the art results, they still produce better results than any other matching systems which do not make use of external ontologies and also surpass some that do benefit from them. This shows that Neural Embeddings are a valuable technique to tackle the challenge of biomedical knowledge graph matching.
A Survey About Media Content Consumption in Social Network Platforms

Social networks have become a pervasive instrument of information consumption and production. With their growing availability, they have become ubiquitous tools to share and consume information in many different content formats. In this paper, we survey how the different content formats and their sources influence social network's usage. The results show that the importance of different media types is perceived differently depending on whether users are producing of consuming media. They also show that different sources of content are given different importance by Facebook users, and that sources also impact the importance of the media type of the information consumed.
Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021

Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Covid-adapted generic 55k corpus from the Directorate General of Translation, were applied. Fine-tuning, mixed fine-tuning and combined dataset approaches were compared with models trained on an extended in-domain dataset. As part of this study, an English-Irish dataset of Covid related data, from the Health and Education domains, was developed. The highest-performing model used a Transformer architecture trained with an extended in-domain Covid dataset. In the context of this study, we have demonstrated that extending an 8k in-domain baseline dataset by just 5k lines improved the BLEU score by 27 points.
A Comprehensive Cluster Map for Construction Cluster

Construction industry is one of the most important industries in the world that has a significant role in the development of countries. Therefore, in the recent decade, many countries accomplish extensive researches for growing their share in an international market. One of the economic approaches, which considered for this reason in several industries in the recent years, is the industrial cluster. However, because of the unique nature of construction industry, many dimensions of this strategy have not been clarified within construction industry. One of these dimensions, that have important role in a comprehensive perception of industrial cluster approach in the construction industry, is the construction cluster map. In this article, a comprehensive map for the construction cluster has been established by the use of grounded theory that illustrates vividly, the actors and activity that contribute to construction industry.
Elderly with Constipation

Effects of administration of 4G-ƒÀ-D-galactosylsucrose (lactosucrose: LS) on fecal microflora, putrefactive products, short-chain fatty acids, weight, moisture and pH, and subjective sensation of defecation were investigated in 13 elderly patients with constipation. The LS mixture used in the present study consisted of LS (59.00, lactose (22.7%), sucrose (8.4%), fructose (1.6%), glucose (6.8%), and non-identified polysaccharides (0.8%). Subjects were fed 0.32 g of LS mixture per kg body weight per day (0.18 g of LS per kg body weight per day) for 3 weeks after a one-week control period. The results obtained were as follows: 1. The number and percentage of Bifcdobacterium remarkably increased. The frequency of occurrence of Bifidobacterium increased, while that of lecithinase-negative Clostridium decreased. 2. The concentrations of pcresol, indole, skatole and ammonia in the feces (nmol or ƒÊ mol/g wet feces) were decreased significantly, and the total fecal excretion of pcresol and skatole (ƒÊ mol/week) was also diminished significantly. 3. LS administration caused significant increases in the concentration (ƒÊ mol/g wet feces) and the total fecal excretion (mmol/week) of acetic and n-butyric acids. 4. Fecal moisture (%) increased significantly, whereas fecal pH decreased. LS also tented to cause a gain in fecal weight (g/
Strategic classification made practical: reproduction

The reproduction of the original paper as well as the extended implementation were successful. We were able to reproduce the original results and examine the performance of the proposed model in an environment where strategic and non-strategic users both present. Linear models seem to struggle with different proportions of strategic users, while the non-linear model (RNN) achieves good performance regardless of the proportion of strategic users.
Exploring the utility of social media data for urban flood impact assessment in data scarce cities

The growing amount of social media data is a valuable and rapidly available information source to inform flood response and recovery. In this study, a workflow framework is developed to assess urban flood impacts by extracting and analysing social media data, as well as identifying the intensive public response areas, using the case of 2020 China Chengdu rainstorm-induced flooding. A crawler-algorithm is applied to extract and filter the social media data from the commonly 10 used social platforms, namely Weibo (static data) and Tiktok (dynamic data). Based on the spatiotemporal analysis and the identified 232 flood sites with geological locations, the study shows that, social media activities and precipitation have a significant positive correlation temporally. The temporal evolution analysis of social media topics reveals the process of flooding enabling quickly to determine the severely affected areas. Spatially, social media data can give spatial flood information and social media activities are generally associated with the demographical distribution of users. Based on a 15 flood simulation, the framework can generate reliable data source of urban flooding from social media, which can enhance flood risk modelling with the aid of hydrodynamic model. This study demonstrates the utility of social media data for urban flood assessment. study the using rainstorm-induced flooding. We presented an efficient workflow to collect, process and identify unstructured flood related data in near real-time during a storm event. 232 flood sites with geological locations are identified in Chengdu 2020 storm. Based on identified social media database, the spatiotemporal analysis of the urban flooding process and general public response were conducted to support risk management. This study shows that social media data can provide valuable spatial and timely information on public responses towards urban flooding for use by local governments in emergency management.
Optimal PMU placement considering state estimation uncertainty and voltage controllability

The development of monitoring technologies in smart grid is undergoing an essential transition from periodical magnitude-based measurement by supervisory control and data acquisition (SCADA) to real-time phasor measurement unit (PMU)-based wide area monitoring and control. The implementation of PMUs can substantially facilitate grid-wide state estimation and operation control, which is dependent on an effective PMU placement strategy. In this study, the optimal PMU placement (OPP) problem is investigated with respect to zonal voltage controllability and voltage magnitude estimation in cooperation with SCADA. In particular, the OPP is formulated as a multi-objective minimal Ncut partitioning problem, in which various operating scenarios are represented by probabilistic load flows to achieve a general solution for planning purpose. Spectral clustering is employed to efficiently find the optimal clustering boundaries, which indicate the resulting placement layout. The proposed placement scheme is tested on New England 39-bus and IEEE 118-bus systems. Simulation results demonstrate the effectiveness of the proposed approach.
Locality of Topographic Ground Truth Data for Salt Marsh Lidar DEM Elevation Bias Mitigation

Lidar-derived digital elevation models (DEMs) are crucial for modeling salt marsh evolution, forecasting inundation depth, frequency, and duration, and simulating sea level rise. Advances in lidar acquisition and data processing techniques have led to increased accuracy; however, in densely vegetated coastal salt marsh areas, lidar-derived DEMs are generally unreliable without adjustment. In this article, we investigate the need for local topographic ground truth data to train random forest (RF) DEM adjustment models for two similar northern Gulf of Mexico salt marshes. Two RTK-GNSS field surveys were conducted to acquire ground truth topographic elevations near St. Marks, Florida, USA (n = 377) and Pascagoula, MS, USA (n = 610). These elevations, along with Sentinel-2A MSI reflectance values and lidar DEM elevations, were used to validate and train local and combined RF salt marsh DEM adjustment models. The local RF models achieved mean absolute error values of 0.054 m and 0.045 m in the leave-one-out cross-validation for St. Marks and Pascagoula, respectively. Elevation bias predictions using remote RF models were far worse and those using the combined RF model were marginally worse. Using the local RF predictions to mitigate the bias in the lidar DEMs improved their accuracy by 69.1% for St. Marks and 90.9% for Pascagoula. The DEM elevation was identified as the most important predictor. This evidence suggests that local ground truth data are necessary for mitigating bias in salt marsh lidar DEMs although it remains to be seen if increasing the data set size and incorporating additional hydrologic predictor variables could narrow the accuracy gap.
Rapid Mixing of the Switch Markov Chain for 2-Class Joint Degree Matrices

The switch Markov chain has been extensively studied as the most natural Markov Chain Monte Carlo approach for sampling graphs with prescribed degree sequences. In this work we study the problem of uniformly sampling graphs for which, in addition to the degree sequence, joint degree constraints are given. These constraints specify how much edges there should be between two given degree classes (i.e., subsets of nodes that all have the same degree). Although the problem was formalized over a decade ago, and despite its practical significance in generating synthetic network topologies, small progress has been made on the random sampling of such graphs. In the case of one degree class, the problem reduces to the sampling of regular graphs (i.e., graphs in which all nodes have the same degree), but beyond this very little is known. We fully resolve the case of two degree classes, by showing that the switch Markov chain is always rapidly mixing. We do this by combining a recent embedding argument developed by the authors in combination with ideas of Bhatnagar, Randall, Vazirani and Vigoda (2006) introduced in the context of sampling bichromatic matchings.
Evaluating Sampling-based Filler Insertion with Spontaneous TTS

Inserting fillers (such as “um”, “like”) to clean speech text has a rich history of study. One major application is to make dialogue systems sound more spontaneous. The ambiguity of filler occurrence and inter-speaker difference make both modeling and evaluation difficult. In this paper, we study sampling-based filler insertion, a simple yet unexplored approach to inserting fillers. We propose an objective score called Filler Perplexity (FPP). We build three models trained on two single-speaker spontaneous corpora, and evaluate them with FPP and perceptual tests. We implement two innovations in perceptual tests, (1) evaluating filler insertion on dialogue systems output, (2) synthesizing speech with neural spontaneous TTS engines. FPP proves to be useful in analysis but does not correlate well with perceptual MOS. Perceptual results show little difference between compared filler insertion models including with ground-truth, which may be due to the ambiguity of what is good filler insertion and a strong neural spontaneous TTS that produces natural speech irrespective of input. Results also show preference for filler-inserted speech synthesized with spontaneous TTS. The same test using TTS based on read speech obtains the opposite results, which shows the importance of using spontaneous TTS in evaluating filler insertions. Audio samples: www.speech. kth.se/tts-demos/LREC22
Scaling Laws vs Model Architectures : How does Inductive Bias Inﬂuence Scaling? An Extensive Empirical Study on Language Tasks

There have been a lot of interest in the scal-001 ing properties of Transformer models (Kaplan 002 et al., 2020). However, not much has been 003 done on the front of investigating the effect 004 of scaling properties of different inductive bi-005 ases and model architectures. Do model ar-006 chitectures scale differently? If so, how does 007 inductive bias affect scaling behaviour? How 008 does this inﬂuence upstream (pretraining) and 009 downstream (transfer)? This paper conducts 010 a systematic study of scaling behaviour of ten 011 diverse model architectures such as Transform-012 ers, Switch Transformers, Universal Trans-013 formers, Dynamic convolutions, Performers, 014 and recently proposed MLP-Mixers. Via ex-015 tensive experiments, we show that (1) archi-016 tecture is an indeed an important considera-017 tion when performing scaling and (2) the best 018 performing model can ﬂuctuate at different 019 scales. We believe that the ﬁndings outlined in 020 this work has signiﬁcant implications to how 021 model architectures are currently evaluated in 022 the community. 023
Longitudinal follow-up of liver metastases from colorectal cancer using artiﬁcial intelligence

The management of patients with colorectal cancer, second most common cause of cancer deaths, is a major public health issue [1]. Half of patients with colorectal cancer develop a distant recurrence. The liver, via the development of liver metastases, is the most common site of spread, accounting for 15-25% of patients at diagnosis and a further 18-25% within 5 years [2]. The objective is to find a management adapted to each patient by integrating individual (age, comorbidity, stage.), tumoral (number, size, position.) and collective data. With an estimated 5-year survival rate between 37% and 58%, liver resection consists of the complete removal of lesions, leaving at least 30% of the parenchyma. When surgery is not an option, the treatment regimen consists of palliative oncological treatments. Computed Tomography (CT) image analysis is a crucial step in assessing the response to chemotherapy treatments.
A different analysis applied to a mathematical model on output of exhaled nitric oxide

The relatively recent detection of nitric oxide (NO) in the exhaled breath has prompted a great deal of experimentation in an effort to understand the pulmonary exchange dynamics. There has been very little progress in theoretical studies to assist in the interpretation of the experimental results. We have developed a two-compartment model of the lungs in an effort to explain several fundamental experimental observations. The model consists of a nonexpansile compartment representing the conducting airways and an expansile compartment representing the alveolar region of the lungs. Each compartment is surrounded by a layer of tissue that is capable of producing and consuming NO. Beyond the tissue barrier in each compartment is a layer of blood representing the bronchial circulation or the pulmonary circulation, which are both considered an inﬁnite sink for NO. All parameters were estimated from data in the literature, including the production rates of NO in the tissue layers, which were estimated from experimental plots of the elimination rate of NO at end exhalation (E NO ) vs. the exhalation ﬂow rate (V˙ E ). The model is able to simulate the shape of the NO exhalation proﬁle and to successfully simulate the following experimental features of endogenous NO exchange: 1 ) an inverse relationship between exhaled NO concentration and V˙ E , 2 ) the dynamic relationship between the phase III slope and V˙ E , and 3 ) the positive relationship between E NO and V˙ E . The model predicts that these relationships can be explained by signiﬁcant contributions of NO in the exhaled breath from the nonexpansile airways and the expansile alveoli. In addition, the model predicts that the relationship between E NO and V˙ E can be used as an index of the relative contributions of the airways and the alveoli to exhaled NO.
Microsoft Word-90329935-file00.docx

During the process of drug discovery the pharmaceutical industry is faced with numerous challenges. One challenge is the successful prediction of the major routes of human clearance of new medications. For compounds cleared by metabolism, accurate predictions help provide an early risk assessment of their potential to exhibit significant inter-patient differences in pharmacokinetics via routes of metabolism catalyzed by functionally polymorphic enzymes and/or clinically significant metabolic drug-drug interactions (DDIs). This review details the most recent and emerging in vitro strategies utilized by drug metabolism and pharmacokinetic (DMPK) scientists to better determine rates and routes of metabolic clearance and how to translate these parameters to estimate the proportion these routes contribute to overall clearance, commonly referred to as fraction metabolized (fm). The enzymes covered in this review include cytochrome P450s together with other enzymatic pathways whose involvement in metabolic clearance have become increasingly important as efforts to mitigate cytochrome P450 clearance are successful. Advances in the prediction of the fraction metabolized include newly developed methods to differentiate CYP3A4 from the polymorphic enzyme CYP3A5, scaling tools for UDP-glucuronosyltranferase (UGT) and estimating fraction metabolized for substrates of aldehyde oxidase. 3 This article has not been copyedited and formatted. The final version may differ from this version. DMD Fast Forward. Published on October 8, 2014 as DOI: 10.1124/dmd.114. 058750 at A PE T Jornals on M ay 7, 2022 dm d.aspurnals. org D ow nladed from
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

We present a new veriﬁcation algorithm to efﬁciently and incrementally verify arbitrarily layered network data planes that are implemented using packet encapsulation. Inspired by work on model checking of pushdown systems for recursive programs, we develop a veriﬁcation algorithm for networks with packets consisting of stacks of headers. Our algorithm is based on a new technique that lazily “repairs” a decomposed stack of header sets on demand to account for cross-layer dependencies. We demonstrate how to integrate our approach with existing fast incremental data plane veriﬁers and have implemented our ideas in a tool called K ATRA . Evaluating K ATRA against an alternative approach based on equipping existing incremental veriﬁers to emulate ﬁnite header stacks, we show that K ATRA is between 5x-32x faster for packets with just 2 headers (layers), and that its performance advantage grows with both network size and layering.
Solution Structure and Function of the “Tandem Inactivation Domain” of the Neuronal A-type Potassium Channel Kv1.4* □ S

Cumulative inactivation of voltage-gated (Kv) K (cid:1) channels shapes the presynaptic action potential and determines timing and strength of synaptic transmis-sion. Kv1.4 channels exhibit rapid “ball-and-chain”-type inactivation gating. Different from all other Kv (cid:2) subunits, Kv1.4 harbors two inactivation domains at its N terminus. Here we report the solution structure and function of this “tandem inactivation domain” using NMR spectroscopy and patch clamp recordings. Inactivation domain 1 (ID1, residues 1–38) consists of a flexible N terminus anchored at a 5-turn helix, whereas ID2 (res-idues 40–50) is a 2.5-turn helix made up of small hydro- phobic amino acids. Functional analysis suggests that only ID1 may work as a pore-occluding ball domain, whereas ID2 most likely acts as a “docking domain” that attaches ID1 to the cytoplasmic face of the channel. Deletion of ID2 slows inactivation considerably and largely impairs cumulative inactivation. Together, the concerted action of ID1 For NMR Kv1.4N-(1–75) protein was dissolved in a solution contain- ing 50 m M sodium phosphate (90% H 2 O, 10% D 2 O (v/v), pH 4.4), 10 m M dithiothreitol, and 1 m M protease inhibitors. The final protein concen- tration of Kv1.4N-(1–75) was 1 m M . For 1 H chemical shift referencing, 2,2-dimethyl-2-silapentane-5-sulfonate was added as an internal standard. NOESY-HSQC (cid:2) for T 2 15 N relaxation to the reduced
Experimental Evaluations of Parallel Tempering on an Ising Machine

: Ising machines have recently attracted much attention because they are expected to solve combinatorial optimization problems e ﬃ ciently. We focus on an Ising machine whose algorithm is based on parallel tempering (PT), and experimentally evaluate the performance of the Ising machine for MIN-CUT problems. Experimental results show that the Ising machine outperforms a famous graph partitioning solver in terms of the quality of solution and the time-to-target-solution.