Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychopathology in 9/11 Responders Exposed to Hurricane Sandy: Examining the Interactive Effects of Exposure and Resilience

: World Trade Center (WTC) disaster responders were exposed to psychological trauma due to the September 11, 2001 terrorist attacks. As a result, many continue to suffer from symptoms of post-traumatic stress disorder (PTSD) and psychological distress which can worsen after exposure to subsequent traumatic events such as Hurricane Sandy. Preexisting data were obtained from a study group of anonymous WTC responders both before and after Hurricane Sandy. Statistical analyses concluded that higher levels of hurricane exposure were associated with increased symptoms of PTSD and distress following Sandy. It was also determined that 9/11 responders with high hurricane exposure levels who possessed increased adaptive coping skills prior to Sandy had lower distress symptoms post-Sandy. The results highlight the importance of adaptive coping skills in promoting resilience to psychological distress. Therefore, coping skills are an important therapeutic target for individuals at risk for exposure to trauma, such as disaster responders.
Audiovestibular Impairment in Patients with Systemic Autoimmune Rheumatic Diseases of Different Pathogenesis

Background: Hearing impairment (HI) is classified into conductive, sensorineural, and mixed types. Sensorineural hearing loss in autoimmune diseases (AD), although not uncommon, is underdiagnosed. Autoimmune diseases may affect the vestibulocochlear system at variable degrees which is usually underdiagnosed due to unidentified specific antigens. Objectives: to assess audiovestibular system in a cohort of patients to aid in early recognition and management of a vital overlooked aspect of such comorbidity. Subjects and Methods: A total of ninety-eight adult patients complaining of different systemic autoimmune rheumatic diseases followed-up at outpatient rheumatology clinics were enrolled in this cross-sectional study. Patients were categorized into three sub-groups: rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Behçet’s disease (BD). Clinical, laboratory, audiological and vestibular assessment of disease activity among different groups was done. Results: Pure tone audiometry ( PTA) values showed significantly increased prevalence of hearing loss in our AD patients compared to healthy control group (P <0.000). Regarding vestibular assessment, pathological saccades were discovered using the vHIT, particularly in horizontal canals. The patient’s gain values were slightly lower than the control group; however, gain and gain asymmetry differed significantly in a few canals. Disease duration significantly correlated with hearing thresholds at high frequency in RA and SLE patients. DAS-28 in RA patients significantly correlated with hearing thresholds, however SLEDAI in SLE didn’t reveal any correlation. Conclusions: Early audiovestibular screening in patients with autoimmune rheumatic diseases should be considered, especially in those with long disease duration as neglected impairment is prevalent.
The Social Cost of Carbon under Climate Volatility Risk

We calculate the social cost of carbon (SCC) under stochastic climate volatility resulting from uncertainty about future climate risk regimes where weather extremes are becoming more frequent and intense. Using a stochastic dynamic integrated climateeconomy model where representative agents are endowed with Duffie-Epstein recursive preferences, we find that climate volatility risks substantially increase the SCC both in the business-as-usual and optimal abatement policy scenario. We also show that switching to a regime with more intense disasters increases the SCC more than a switch to a regime with more frequent disasters for equal expected value. Overall we show that stochastic volatility has a major impact on the SCC. JEL Codes: G12, G13, Q51, Q54
Variational Shape Reconstruction via Quadric Error Metrics

Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the input points enriched with quadric error metrics, where the generator of each cluster is the optimal 3D point for the sum of its quadric error metrics. This approach favors the placement of generators on sharp features, and tends to equidistribute the error among clusters. We reconstruct the output surface mesh from the adjacency between clusters and a constrained binary solver. We combine our clustering process with an adaptive refinement driven by the error. Compared to prior art, our method avoids dense reconstruction prior to simplification and produces immediately an optimized mesh.
Numerical Simulation of the Flow Field and Pollutant Dispersion around a Hill under Different Thermal Stratiﬁcations

This paper applied a commercial computational ﬂuid dynamics code, STAR-CD, with the renormali- zation group k– « turbulence model to simulate the ﬂow and dispersion of contaminants released from a source on the windward side of a hill under different thermal stratiﬁcations. In the wake region, the inﬂuence of atmospheric stratiﬁcation on the ﬂow ﬁeld is inconspicuous under neutral and unstable conditions because of the effect of mechanical disturbance. However, this inﬂuence becomes slightly conspicuous under stable conditions. When atmospheric stratiﬁcation is stable, in the range of z / H , 1.0 (where z is height above the surface and H is height of the hill), the velocity deﬁcits are smaller than those under neutral and unstable conditions. The maximum turbulence kinetic energy (TKE) appears in the wake regions, and the variation in TKE is signiﬁcantly lower than that under neutral and unstable con- ditions. When atmospheric stratiﬁcation is unstable, the vertical and horizontal spread of the plume is slightly greater than that under neutral and stable conditions and the maximum concentration is less than that under neutral conditions. When the Froude number is large ( ; 11; Brunt–V ä is ä l ä frequency 5 0.52), atmospheric stratiﬁcation is slightly stable, the structure of ﬂow around the hill is generally similar to that under neutral conditions, and the high-concentration regions are large on the windward side of the hill. Smaller high-concentration regions just appear on the windward side of the hill under unstable conditions. The pollutant concentrations in the wake region of the hill increase as a result of the effect of thermal stability, and the vertical spreading range of the plume along the downwind axis ( x axis) is larger than that under neutral and stable conditions.
Exploiting Commonsense Knowledge about Objects for Visual Activity Recognition

Situation recognition is the task of recognizing the activity depicted in an image, including the people and objects involved. Previous models for this task typically train a classifier to identify the activity using a backbone image feature extractor. We propose that common-sense knowledge about the objects depicted in an image can also be a valuable source of information for activity identification. Previous NLP research has argued that knowledge about the prototypical functions of physical objects is important for language understanding, and NLP techniques have been developed to ac-quire this knowledge. Our work investigates whether this prototypical function knowledge can also be beneficial for visual situation recognition. We build a framework that incorporates this type of commonsense knowledge in a transformer-based model that is trained to predict the action verb for situation recognition. Our experimental results show that adding prototypical function knowledge about physical objects does improve performance for the visual activity recognition task.
A Fully Automated and Scalable Approach for Indoor Temperature Forecasting in Buildings Using Artificial Neural Networks

Improving the performance of buildings is a core pillar to attaining future energy and environmental goals in different countries, considering that the building sector is a major contributor in terms of both energy consumption and carbon emissions. These ambitious goals and the call for smarter, energy-efficient, and flexible buildings have called for innovative and scalable energy and indoor thermal comfort modeling and prediction approaches. This work presents a fully automated and scalable solution us-ing Artificial Neural Networks to forecast indoor room temperatures in buildings. A case study of an 8500 m 2 university building in Denmark was considered for testing and evaluating the proposed approach. An extensive dataset was constructed with sensor data from 76 rooms that contain both readings on indoor temperature, CO 2 concentrations, and actuating signals on radiator valves and dampers, as well as outdoor ambient conditions. Using this dataset, a well-performing architecture is identified, which provides accurate temperature predictions in the various rooms of the building for prediction horizons of 24 hours.
Attribute Controlled Dialogue Prompting

Prompt-tuning has become an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks. However, both discrete prompting and continuous prompting assume fixed prompts for all data samples within a task, neglecting the fact that inputs vary greatly in some tasks such as open-domain dialogue generation. In this paper, we present a novel, instance-specific prompt-tuning algorithm for dialogue generation. Specifically, we generate prompts based on instance-level control code, rather than the conversation history, to explore their impact on controlled dialogue generation. Experiments on popular open-domain dialogue datasets, evaluated on both automated metrics and human evaluation, demonstrate that our method is superior to prompting baselines and comparable to fine-tuning with only 5%-6% of total parameters.
Rating, ranking or both? A joint application of two probabilistic models for the measurement of values

Following the debate between the proponents of the ranking and rating methods for the measurement of values, this article considers the hypothesis that a person’s responses in ranking and rating tasks are governed by the same unidimensional latent trait. The hypothesis was tested through a probabilistic modeling approach. The results of a joint application of the rating scale and ranking models indicated that this latent common trait exists and seems to have a role in molding the relationship between the latent variable and the observable responses in both ranking and rating formats.
A Comparative Study of Labor Outcomes after Induction with Oxytocin and Oral Misoprostol in Pregnant Women with Premature Rupture of Membrane

Introduction: In term women with PROM, oxytocin, and prostaglandins both work well to induce birth. 5 The decision to use a standard procedure is still debatable. The approach that is typically used is oxytocin. However, it must be given intravenously while the infusion and contraction rates are carefully observed. The benefit of misoprostol taken orally, specifically in relation to pre-labor membrane rupture, is the avoidance of repeated vaginal inspections, which lowers the risk of sepsis for both the mother and the infant. Aims/ objective: To compare the efficacy and safety of oral misoprostol and intravenous oxytocin for induction of labor in women with PROM. Materials and Method: Women who were diagnosed with PROM were included in the study with 48 women in each group. In the misoprostol group, patients were given 50 micrograms of oral misoprostol every 4 hours until the delivery. Patients enrolled in the oxytocin group were given intravenous infusion of low dose regimen of oxytocin at dose rate of 1 to 2mU/min and the rate was increased incrementally by 1 to 2 mU at 30 mins interval to attain the goal of moderate to strong contractions The time between induction and delivery time was recorded. Any maternal complications were recorded. Neonatal outcomes were also assessed. Results: Time between induction and delivery was lower in women who were given oral misoprostol and the difference was statistically significant (p<0.05). Incidence of caesarean delivery was slightly less in misoprostol group but the difference was not statistically significant (p >0.05). Incidence of post-partum haemorrhage, cervical tear or perineal tear was less than 10%. Both the groups were similar with respect to maternal and neonatal complications (p>0.05). Conclusion: In case of premature rupture of membranes, oral misoprostol can be used successfully as an alternative to oxytocin infusion or prostaglandin vaginal pessaries/gel to induce labor. It may also lower postpartum morbidity and shorten hospital stays.
Analysis of speckle patterns in coherent small-angle X-ray scattering performed at the European XFEL

The coherent nature of X-ray Free Electron Laser (XFEL) radiation results in speckle formation during short acquisition times, enabling time-resolved dynamic light scattering experiments like XPCS. These experiments, conducted at a 4.5 MHz FEL repetition rate (available at the European XFEL), offer insights into nano or micro-scale sample dynamics, particularly for timescales between the micro and femtosecond regime. Speckle analysis experiments have the potential to visualize and understand molecular dynamics in various scientific fields such as protein mechanics in biology and strain propagation in solid-state physics, and the presence of resolvable speckles is essential for these studies. Interestingly, the physical size of speckles in reciprocal space is inversely proportional to the spot size of the probing beam. Quantifying this relation would allow for direct recovery of beam size without the need for additional measurements, hence optimizing the experimental setup. With experimental data taken using both dynamic (LUDOX® with dynamic timescale ≈ 1 μs) and static (Vycor) SiO2 samples at the Materials Imaging and Dynamics (MID) instrument at the European XFEL, this report details the progress made in investigating speckle phenomena in Small Angle X-ray Scattering (SAXS) with the help of X-ray scattering simulations written in Python. The goal of the experiments was to vary the beam size of the incident FEL either by changing its energy or applying nano-focusing optics to study the corresponding speckle patterns obtained from the samples. In addition to performing a SAXS analysis, the spatial intensity autocorrelation function of the scattering pattern was used to characterize speckle size. Results showed an inverse relation between the incident beam and speckle size, supported by simulated data. Additionally, the Rayleigh formalism was applied to describe the XFEL beam’s divergence using speckles.
Transition to the New Revenue Standard: A Study of Firms’ Decisions to Adopt ASC 606 Early

The Financial Accounting Standards Board has issued a new revenue standard through a series of updates since May 2014 (collectively, ASC 606). The standard allowed firms to adopt it a year early before the required effective date. This study provides evidence of the adoption effects of ASC 606 on firms’ financials and examines the relationship between the likelihood of early adopting ASC 606 and firm-specific characteristics. The results show that early adopters are distributed across 12 industries and 40% of them are concentrated in the computer programming and software industry. Early adopters in the software industry reported the largest cumulative adoption effects in dollars on retained earnings. Overall, the majority of the sample firms (83%) reported positive adoption effects on financial statements and the adoption of ASC 606 increased their retained earnings by 2.95% on average. The results also show that most early adopters chose the full retrospective method while the majority of industry-size-matched non-early adopting peers chose the modified retrospective method. Finally, the multivariate regression analyses suggest that early adopters are associated with decreased earnings in the year before adoption and more favorable adoption effects than non-early adopting peers.
Visually-Enhanced Phrase Understanding

Large-scale vision-language pre-training has exhibited strong performance in various visual and textual understanding tasks. Recently, the textual encoders of multi-modal pre-trained models have been shown to generate high-quality textual representations, which often outperform models that are purely text-based, such as BERT. In this study, our objective is to utilize both textual and visual encoders of multi-modal pre-trained models to enhance language understanding tasks. We achieve this by generating an image associated with a textual prompt, thus enriching the representation of a phrase for downstream tasks. Results from experiments conducted on four benchmark datasets demonstrate that our proposed method, which leverages visually-enhanced text representations, signiﬁcantly improves performance in the entity clustering task. 1
Computation of sediment discharge in rivers: the contributions by Levi and Studenitcnikov revisited

ABSTRACT In the past century, a considerable amount of theoretical and experimental work in all areas of fluvial hydraulics was carried out in Russia. The outcomes of this massive work have been successfully applied in numerous engineering projects in the former Soviet Union. Unfortunately, because of the language barriers and even more due to the geopolitical situation that dominated the last century, the exchange of scientific knowledge between Russia and western countries was greatly impaired. This paper highlights some interesting contributions by the Russian scientists Levi and Studenitcnikov. In particular, Levi’s formula for the sediment discharge and Studenitcnikov’s formula for the critical velocity are discussed and revisited in light of the Shields approach, currently dominant in the western literature. An application example is given considering prediction of local scour along elliptical guide banks.
An Engineering View on Emotions and Speech: From Analysis and Predictive Models to Responsible Human-Centered Applications

The substantial growth of Internet-of-Things technology and the ubiquity of smartphone devices has increased the public and industry focus on speech emotion recognition (SER) technologies. Yet, conceptual, technical, and societal challenges restrict the wide adoption of these technologies in various domains, including, healthcare, and education. These challenges are amplified when automated emotion recognition systems are called to function “in-the-wild” due to the inherent complexity and subjectivity of human emotion, the difficulty of obtaining reliable labels at high temporal resolution, and the diverse contextual and environmental factors that confound the expression of emotion in real life. In addition, societal and ethical challenges hamper the wide acceptance and adoption of these technologies, with the public raising questions about user privacy, fairness, and explainability. This article briefly reviews the history of affective speech processing, provides an overview of current state-of-the-art approaches to SER, and discusses algorithmic approaches to render these technologies accessible to all, maximizing their benefits and leading to responsible human-centered computing applications.
Ordinal dominance rank assignments: Protocol for the Amboseli Baboon Research Project

Here, we provide an overview of the protocol that the Amboseli Baboon Research Project (ABRP) uses to assign ordinal dominance ranks within baboon social groups. To begin, we describe the assignment of ordinal dominance ranks using a matrix-based method. Next, we discuss the process of assigning ordinal ranks based on the Elo method. Finally, we describe the overall high correlation between ordinal ranks produced by the two methods for baboons in the Amboseli ecosystem of southern Kenya
Highly nonlinear chalcogenide hybrid microstructured optical fibers with buffer layer and their potential performance of supercontinuum generation

We report here the design of a new chalcogenide hybrid microstructured optical fiber (HMOF) with a buffer layer around the core and its potential performance of tailoring chromatic dispersion and supercontinuum (SC) generation. The new chalcogenide HMOF has an AsSe2 core. The refractive index difference Δn between the AsSe2 core and cladding material is supposed to be 0.3. The fiber microstructure and the Δn between the core and buffer materials are designed in order to obtain broad anomalous dispersion regimes with near-zero and flattened chromatic dispersion profiles for broadband SC generation. Moreover, the suppression of chromatic dispersion fluctuation caused by fiber transverse geometry variation is investigated. By using the proposed chalcogenide buffer-embed HMOFs, the calculation shows that near-zero and flattened anomalous chromatic dispersion regimes from 4.5 μm can be obtained. When the variation of fiber structure occurs for ±1, ±5 and ±10 %, the chromatic dispersion fluctuation can be greatly suppressed. In addition, the calculation shows that a broad SC spectrum from 2.5 to more than 16.0 μm can be obtained when a 0.9-cmlong section of the new chalcogenide buffer-embed HMOF is pumped at 5.0 μm by a femtosecond laser with 1-kW peak power.
Skew-Symmetric Prolongations of Lie Algebras and Applications

We study the skew-symmetric prolongation of a Lie subalgebra g ⊆ so ( n ), in other words the intersection Λ 3 ∩ (Λ 1 ⊗ g ). We compute this space in full generality. Applications include uniqueness results for connections with skew-symmetric torsion and also the proof of the Euclidean version of a conjecture by Figueroa-O’Farrill and Papadopoulos concerning a class of Pl¨ucker-type embeddings. We also derive a classiﬁcation of the metric k-Lie algebras (or Filipov algebras), in positive signature and ﬁnite dimension. Next we study speciﬁc properties of invariant 4-forms of a given metric representation and apply these considerations to classify the holonomy representation of metric connections with vectorial torsion, that is with torsion contained in Λ 1 ⊆ Λ 1 ⊗ Λ 2 .
R EVIEW ON D ETECTION OF R ICE P LANT L EAVES D ISEASES USING D ATA A UGMENTATION AND T RANSFER L EARNING T ECHNIQUES

- The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning .