Miracle-dz/UniFAR-FTUs huggingface dataset:
Report on a review of the Oregon coast and Southern Oregon Northern Californai Coastal spring Chinook salmon ESU configuration

On September 24, 2019, the National Marine Fisheries Service (NMFS) received a petition (OCC petition) from the Native Fish Society, Center for Biological Diversity, and Umpqua Watersheds to identify an Oregon Coast (OC) spring-run Chinook salmon Evolutionarily Significant Unit (ESU) and list that ESU as threatened or endangered under the Endangered Species Act (ESA). On April 13, 2020, NMFS announced a “90-day” finding on the petition, determining that the petitioned action may be warranted (85 FR 20476). On May 4, 2020, NMFS received a petition from Mr. Richard K. Nawa of Selma, Oregon, to evaluate Southern Oregon and Northern California Coastal (SONCC) spring-run Chinook salmon for listing under the ESA (SONCC petition). On May 1, 2020, the NMFS West Coast Region (WCR) requested that the Northwest Fisheries Science Center (NWFSC) conduct an analysis and review of the OCC petition’s claim that Oregon Coast spring-run Chinook salmon should be considered a separate ESU, and, if so, provide a description of the demographic risks to any new ESU described as a result of that evaluation. To conduct this evaluation, the NWFSC set up a review panel consisting of NWFSC and Southwest Fisheries Science Center (SWFSC) experts. On May 14th, 2020, the WCR requested that the review panel concurrently evaluate the SONCC petition’s request that SONCC spring-run Chinook salmon should be considered a separate ESU. On March 16, 2021, NMFS announced a 90-day finding on the SONCC petition, finding the action may be warranted (86 FR 14407).
Audio-Visual Speech Super-Resolution

In this paper, we present an audio-visual model to perform speech super-resolution at large scale-factors (8 × and 16 × ). Previous works attempted to solve this problem using only the audio modality as input, and thus were limited to low scale-factors of 2 × and 4 × . In contrast, we propose to incorporate both visual and auditory signals to super-resolve speech of sampling rates as low as 1kHz. In such challenging situations, the visual features assist in learning the content, and improves the quality of the generated speech. Further, we demonstrate the applicability of our approach to arbitrary speech signals where the visual stream is not accessible. Our “pseudo-visual network” precisely synthesizes the visual stream solely from the low-resolution speech input. Extensive experiments illustrate our method’s remarkable results and beneﬁts over state-of-the-art audio-only speech super-resolution approaches. Our project website can be found at http://cvit. iiit.ac. in/research/projects/cvit-projects/audio-visual-speech-super-resolution
Market strategies and core capabilities in the building industry

The objective of this study is to show that the core competence approach has validity when applied to construction. The focus is on the analysis of the fit between market strategies of firms in the building industry and their core capabilities. Certain strategies require specific core capabilities and key external and internal conditions supporting these capabilities. The resource-based approach focuses on the conditions in a firm, the internal capabilities, and supports a core capability that is demanded. The network literature will be related to external conditions that support a core capability of a firm active in a certain market. In a multiple case study, firms are analysed that are involved in different stages of the building process. The case studies demonstrate firms with consistent patterns of market strategies, core capabilities, and supporting internal and external conditions. The specific core capability of the firm under investigation determines the types of external and internal conditions that are critical.
Information and Communication Security Mechanisms For Microservices-based Systems

Security has become paramount in modern software services as more and more security breaches emerge, impacting final users and organizations alike. Trends like the Microservice Architecture bring new security challenges related to communication, system design, development, and operation. The literature presents a plethora of security-related solutions for microservices-based systems, but the spread of information difficult practitioners' adoption of novel security related solutions. In this study, we aim to present a catalogue and discussion of security solutions based on algorithms, protocols, standards, or implementations; supporting principles or characteristics of information security, considering the three possible states of data, according to the McCumber Cube. Our research follows a Systematic Literature Review, synthesizing the results with a meta-aggregation process. We identified a total of 30 primary studies, yielding 75 security solutions for the communication of microservices.
Radiomic combination of spatial and temporal features extracted from DCE-MRI for prostate cancer detection *

Multi-parametric MRI is part of the standard prostate cancer (PCa) diagnostic protocol. Recent imaging guidelines (PI-RADS v2) downgraded the value of Dynamic Contrast-Enhanced (DCE)-MRI in the diagnosis of PCa. A purely qualitative analysis of the DCE-MRI time series, as it is generally done by radiologists, might indeed overlook information on the microvascular architecture and function. In this study, we investigate the discriminative power of quantitative imaging features derived from texture and pharmacokinetic analysis of DCE-MRI. In 605 regions of interest (benign and malignant tissue) delineated in 80 patients, we found through independent cross-validation that a subset of quantitative spatial and temporal features extracted from DCE-MRI and incorporated in machine learning classifiers obtains a good diagnostic performance (AUC = 0.80-0.86) in distinguishing malignant from benign regions.Clinical Relevance— These findings highlight the underlying potential of quantitative DCE-derived radiomic features in identifying PCa by MRI.
Analysis of tumour microstructure estimation from conventional diffusion MRI and application to skull-base chordoma*

Skull-base chordoma (SBC) is a rare tumour whose molecular and radiological characteristics are still being investigated. In neuro-oncology microstructural imaging techniques, like diffusion-weighted MRI (DW-MRI), have been widely investigated, with the apparent diffusion coefficient (ADC) being one of the most used DW-MRI parameters due to its ease of acquisition and computation. ADC is a potential biomarker without a clear link to microstructure. The aim of this work was to derive microstructural information from conventional ADC, showing its potential for the characterisation of skull-base chordomas. Sixteen patients affected by SBC, who underwent conventional DW-MRI were retrospectively selected. From mono-exponential fits of DW-MRI, ADC maps were estimated using different sets of b-values. DW-MRI signals were simulated from synthetic substrates , which mimic the cellular packing of a tumour tissue with well-defined microstructural features. Starting from a published method, an error-driven procedure was evaluated to improve the estimates of microstructural parameters obtained through the simulated signals. A quantitative description of the tumour microstructure was then obtained from the DW-MRI images. This allowed successfully differentiating patients according to histologically-verified cell proliferation information.Clinical Relevance — The impact on cancer management derives from the expected improvement of radiation treatment quality tailored to a patient-specific non-invasive description of tumour microstructure.
Automatic Recognition of Ocular Surface Diseases on Smartphone Images Using Densely Connected Convolutional Networks

Ocular surface disorder is one of common and prevalence eye diseases and complex to be recognized accurately. This work presents automatic classification of ocular surface disorders in accordance with densely connected convolutional networks and smartphone imaging. We use various smartphone cameras to collect clinical images that contain normal and abnormal, and modify end-to-end densely connected convolutional networks that use a hybrid unit to learn more diverse features, significantly reducing the network depth, the total number of parameters and the float calculation. The validation results demonstrate that our proposed method provides a promising and effective strategy to accurately screen ocular surface disorders. In particular, our deeply learned smartphone photographs based classification method achieved an average automatic recognition accuracy of 90.6%, while it is conveniently used by patients and integrated into smartphone applications for automatic patient-self screening ocular surface diseases without seeing a doctor in person in a hospital.
Modelling and identification of iron losses in nonoriented steel laminations using Preisach theory

A numerical method for the evaluation of magnetic iron losses in steel laminations used in electrical machines is described. The magnetodynamic model for the magnetic field strength H, derived from the Maxwell equations, is coupled with the Preisach theory, describing the hysteresis behaviour. The resulting highly nonlinear and transient boundary value problem for H is solved numerically by a modified finite-element, finite-difference method. The direct identification of the material parameters, for which the authors invoke the Everett theory, is discussed in detail. The model is validated by the comparison of numerical experiments and measurements for the B-H loops. For the material considered the rate-dependent Preisach model is found to be more reliable than the classical Preisach model.
Effects of intermediate load on damping of synchronous generator

The transfer-function block-diagram model of a single-machine infinite-bus power system, originally developed by Heffron and Phillips, has been a popular analytical tool amongst power system engineers for explanation and assessment of synchronous generator dynamic behaviors. Since this model simply accounts for the generator field circuit with none of the damper circuits, it may not always give a realistic transient response. Moreover, the model considers only a grid-system load without local and intermediate loads. Hence, effects of these loads together with the damper circuits on electromechanical damping have not yet been completely studied. In this paper, the Heffron-Phillips's model has been advanced to incorporate an intermediate load plus one additional damper circuit in the q-axis. The upgraded model demonstrates a great influence of the intermediate load together with the q-axis damper circuit on the electromechanical damping and the dynamic interaction between the field and damper flux linkages. The study shows the key contributions of load to rise and fall of the damping. It appears that the electromechanical damping can be improved with regard to the unity power-factor load through increasing in the natural damping and decreasing in the automatic voltage regulator (AVR) negative damping torques. Nevertheless, the damping is mostly declined, when the load power factor is poor. Moreover, it is markedly changed in relation to various locations of load. The damping characteristics of synchronous generator are investigated using the eigenvalue and frequency response methods. Copyright © 2006 John Wiley & Sons, Ltd.
Learned Parameters and Increment for Iterative Photoacoustic Image Reconstruction via Deep Learning

Photoacoustic (PA) tomography is a relatively new medical imaging technique that combines traditional ultrasound imaging and optical imaging, which has great application prospects in recent years. To reveal the light absorption coefficient of biological tissues, the images are reconstructed from PA signals by reconstruction algorithms. However, traditional model-based reconstruction method requires a huge number of iterations to obtain relatively good experimental results, which is quite time-consuming. In this paper, we propose to use deep learning method to replace brute parameter adjustment in model-based reconstruction, and speed up the rate of convergence by building convolutional neural networks (CNN). The parameters we defined in our model can be learned automatically. Meanwhile, our method can optimize the increment of gradient in each step of iteration. The numerical experiment validates our method, showing that only three iterations are needed to obtain the satisfactory image quality, which normally requires 10 iterations for tradition method. It demonstrated that efficiency of photoacoustic reconstruction can be greatly improved by our proposed method, compared with traditional model-based methods.
Evaluation of the relationship between amitriptyline dose ingested and ADORA criteria in amitriptyline intoxication: A retrospective observational study

DOI: 10.4328/ACAM.20739 Received: 2021-06-08 Accepted: 2021-08-19 Published Online: 2021-09-13 Printed: 2021-10-01 Ann Clin Anal Med 2021;12(10):1171-1174 Corresponding Author: Serdar Özdemir, Department of Emergency Medicine, University of Health Sciences, Umraniye Training and Research Hospital, Istanbul, Turkey. E-mail: dr.serdar55@hotmail.com P: +90 505 267 32 92 Corresponding Author ORCID ID: https://orcid.org/0000-0002-6186-6110 Abstract Aim: Amitriptyline exerts its effects on the central nervous system by inhibiting the reuptake of norepinephrine, dopamine, and serotonin. Amitriptyline’s toxic effects occur 30 min after the ingestion of an excessive dose and peak within 2-6 h. While amitriptyline has a primarily anticholinergic effect at low doses, at high doses, it produces a potent depressive effect on the central nervous system, accompanied by cardiotoxicity, seizures, and hypotensive symptoms. The Antidepressant Overdose Risk Assessment (ADORA) criteria are used to evaluate clinical symptoms. In this study, we investigated the relationship between certain clinical and demographic parameters and the ADORA criteria in patients presenting to the emergency department with tricyclic antidepressant agent poisoning. Material and Methods: The study included 18 patients admitted to the hospital between January 2016 and December 2019 following attempted suicide by amitriptyline overdose. Clinical and demographic data, ADORA risk classification, and ingested doses were recorded on a case-by-case basis. The relationship between ADORA risk classification, ingested dose, and other clinical parameters was analyzed. Results: Of the patients included in the study, two were male (10.5%) and 16 were female (84.2%). The minimum ingested dose was 100 mg, maximum 525 mg, and median 300 mg. According to the ADORA criteria, 12 cases (66.7%) were classified as low-risk and six cases (33.3%) as high-risk. All the high-risk patients were hospitalized and treated (p<0.0001). The relationship between the amitriptyline dose ingested and ADORA classification was statistically significant (p=0.02). The sensitivity and specificity of this classification system for amitriptyline dose ingested were 100% and 60%, respectively, at a cut-off value of 285 mg and 60% and 100%, respectively, at a cut-off value of 350 mg. Discussion: The ADORA risk classification system positively correlated with the hospitalization rate. Patients having ingested ≥350 mg of amitriptyline were classified as high-risk by the ADORA system with high specificity.
Optical solitons with nonlinear dispersion in polynomial law medium

Q. ZHOU, X. XIONG, Q. ZHU, Y. LIU, H. YU, P. YAO, A. BISWAS, M. BELIC School of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, 430212, P.R. China Department of Mathematical Sciences, Delaware State University, Dover, DE 19901-2277, USA Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah-21589, Saudi Arabia Science Program, Texas A & M University at Qatar, Doha, Qatar
Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

Graph Similarity Computation (GSC) is essential to wide-ranging graph applications such as retrieval, plagiarism/anomaly detection, etc. The exact computation of graph similarity, e.g., Graph Edit Distance (GED), is an NP-hard problem that cannot be exactly solved within an adequate time given large graphs. Thanks to the strong representation power of graph neural network (GNN), a variety of GNN-based inexact methods emerged. To capture the subtle difference across graphs, the key success is designing the dense interaction with features fusion at the early stage, which, however, is a trade-off between speed and accuracy. For Slow Learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features. To further improve the speed without much accuracy drop, we introduce an efﬁcient GSC solution by distilling the knowledge from the slow early-fusion model to the student one for Fast Inference . Such a student model also enables the ofﬂine collection of individual graph embeddings, speeding up the inference time in orders . To address the instability through knowledge transfer, we decompose the dynamic joint embedding into the static pseudo individual ones for precise teacher-student alignment. The experimental analysis on the real-world datasets demonstrates the superiority of our approach over the state-of-the-art methods on both accuracy and efﬁciency. Particularly, we speed up the prior art by more than 10x on the benchmark AIDS data.
LaLaLoc++: Supplementary Material

Due to the characteristics of the datasets used, namely Structured3D [5] and the Zillow Indoor Dataset [1], there are some additional tacit assumptions about the structure of the indoor scenes within which localisation is performed. Firstly, 3D room geometry is generated through extrusion of 2D polygons. Therefore, this results in all walls being vertical. In addition, it means that all floors and ceilings consist of a single horizontal plane each. Secondly, camera heights fall within a range of 1.4-1.8m in Structured3D, and within a range of 0.7-2.1m in the Zillow Indoor Dataset.
An ensemble approach for aspect term extraction in Turkish texts

Öz Today, as a result of the inadequacies of the standard sentiment analysis, aspect-based sentiment analysis (ABSA) studies have great attracting interest. ABSA reveals detailed sentiment and opinion about every term/attribute in a text. The most important sub-stage of the ABSA method is the process of extracting the aspect terms from a text. This process becomes more difficult in texts with agglutinative language structures such as Turkish. In this study, we proposed an ensemble approach that uses statistical (TF-IDF), topic modeling (LDA and NMF), and rule-based methods together to extract aspect terms from Turkish user comments. The proposed method strategically combines the candidate aspect term obtained by different methods and determines the final aspect term lists. The proposed method has been tested on the SemEval-2016 ABSA benchmarking dataset, which consists of Turkish restaurant reviews. The experimental results of the proposed method were compared with previous studies on the same dataset. Günümüzde standart duygu analizinin yetersiz kalması sonucunda, hedef tabanlı duygu analizi (HTDA) çalışmaları büyük ilgi görmüştür. HTDA, bir metindeki her terim/nitelik hakkında ayrıntılı duygu ve düşüncelerin ortaya çıkarılmasını sağlar. HTDA yönteminin en önemli alt aşaması, bir metinden hedef terimlerinin çıkarılması işlemidir. Türkçe gibi sondan eklemeli dil yapılarına sahip metinlerde bu süreç daha da zorlaşmaktadır. Bu çalışmada, Türkçe kullanıcı yorumlarından hedef terimlerini çıkarmak için istatistiksel (TF-IDF), konu modelleme (LDA ve NMF) ve kural-tabanlı yöntemleri bir arada kullanan bir topluluk yaklaşımı önerilmiştir. Önerilen yöntem, farklı yöntemlerle elde edilen aday hedef terim kümelerini stratejik olarak birleştirir ve nihai hedef terimleri listesini belirler. Önerilen yöntem, Türkçe restoran yorumlarından oluşan SemEval-2016 HTDA kıyaslama veri seti üzerinde test edilmiştir. Önerilen yöntemin deneysel sonuçları aynı veri kümesi üzerinde yapılan önceki çalışmalarla karşılaştırılmıştır. Anahtar kelimeler: Aspect term extraction, Aspect based sentiment analysis, Turkish texts, Sentiment analysis
J. Gen. Appl. Microbiol., 51, 197–201 (2005)

zation in wastewater treatment plants is the fluorescence in-situ hybridization (FISH) methodology. Although it has been commonly used in activated sludge (see e.g. Juretschko et al., 1998) and trickling filters (see e.g. Okabe et al., 1999) almost no evidence on its application to low-cost systems as constructed wetlands is found in the literature (Flood et al., 1999; Silyn-Roberts and Lewis, 2001). Moreover, studies on these systems are usually carried out during the growing season and no data is usually available on the winter period, which is the time of year for which low-cost treatment systems are designed as they have to maintain their removal efficiency even in harsh conditions. In the present study we have employed FISH in different units of a low-cost wastewater treatment system, formed by a stabilization pond followed by in-series free-water surface and sub-surface constructed wetlands. The aim of this study was to describe the winter microbial community composition in each of these units and their relation with the wastewater characteristics. The system is a mixed constructed wetland where water passes consecutively through a stabilization pond (SP), a free water surface system (FWS) planted with Typha latifolia and a sub-surface wetland (SSF) planted with willows (Salix atrocinerea) (Fig. 1). Further details on the process are presented elsewhere (Ansola et al., 2003). The studied plant was located in Bustillo de Cea, in the northwest of León (Spain). It was designed for rural domestic wastewater treatment with an average flow of 56.3 m/d during the study period. Total area of the system was 890 m and the theoretical hydraulic retention time for each unit was 4.21, 3.53 and 3.16 days for the SP, FWS and SSF respectively. Despite the fact that the SP was designed to allow phytoplankton growth, a thick layer of the floating macrophyte Lemna minor was observed during the study. Four samples were taken bi-weekly between November 2001 and February 2002. One liter of water was collected in each sampling point and then cooled and carried to the laboratory where samples were centrifuged and purified following Manz et al. (1994). Each sample was then divided in two parts; the first one was fixed with a 3% paraformaldehyde (PFA) solution for Gram-negative bacteria, and the second one was fixed by addition of ethanol to a final concentration of 50% (v/v) for Gram-positive bacteria, both for 16 h at 4°C. After this, the aliquots were stored in a 1 : 1 mixture of phosphate-buffered saline (PBS) and 96% ethanol at 20°C (Manz et al., 1994). The hybridization step was done according to Manz Characterization of bacterial communities of a constructed wetland in cold conditions
Position Weight with Muti-Head Attention for Aspect-Based Sentiment Classification

: The purpose of aspect based sentiment classification (ABSC) is to predict emotional tendencies of a sentence or article in certain aspects. Most of the existing methods use coarse-grained attention mechanisms and do not notice the relationship between emotional orientation and contextual content. In this paper, we proposed a model called PW-MHA in which a pre-trained BERT is applied. PW-MHA uses the multi-head attention mechanism to capture the relationships between internal words. Position weights are used for the classification on the idea that a closer context word is more likely to be the importance word for the target. We evaluated the PW-MHA model on three datasets: laptops and restaurants from SemEval2014, and the ACL 14 twitter dataset. It is shown that the PW-MHA has achieved competitive results on the given datasets.