Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive Pulmonary Embolism Causing Cardiac Arrest Managed with Systemic Thrombolytic Therapy: A Case Report

Patient: Female, 66-year-old Final Diagnosis: Pulmonary embolism Symptoms: Cardiac arrest Medication: — Clinical Procedure: Thrombolytic therapy Specialty: Critical Care Medicine Objective: Rare disease Background: Approximately 290 000 cases of in-hospital cardiac arrest occur annually, the majority of which are due to cardiac or respiratory causes. Cardiac arrest due to acute pulmonary embolism (PE) is associated with a 90% incidence of mortality and, if identified, it can be treated with systemic thrombolytics. Here, we describe a case in which the outcome for such an event was favorable. Case Report: A 66-year-old woman was admitted with multiple rib and left ankle fractures due to accidental trauma. Before undergoing orthopedic surgery, she experienced a cardiac arrest with pulseless electrical activity, which was witnessed. She had refractory hypoxia and hypotension following intubation and a brief initial return of spontaneous circulation (ROSC) before a second cardiac arrest. A 100-mg bolus dose of systemic thrombolytic therapy was promptly administered, with rapid achievement of sustained ROSC. The results of a subsequent electrocardiogram, echocardiogram, and computed tomography scan further supported the diagnosis of acute PE with right heart strain. Supportive care in the Intensive Care Unit resulted in full neurological recovery and she was discharged to a physical rehabilitation facility 12 days after her cardiac arrest. Conclusions: Systemic thrombolytic therapy is beneficial for cardiac arrest due to acute PE.
The ideal-valued index of fibrations with total space a $G_{2}$ flag manifold

Using the cohomology of the $G_2$-flag manifolds $G_2/U(2)_{\pm}$, and their structure as a fiber bundle over the homogeneous space $G_2/SO(4)$, we compute their Borel cohomology and the $\mathbb{Z}_2$ Fadell-Husseini index of such fiber bundles, for the $\mathbb{Z}_2$ action given by complex conjugation. Considering the orthogonal complement of the tautological bundle $\gamma$ over $\widetilde{G}_{3}( \mathbb{R}^{7})$, we compute the $\mathbb{Z}_2$ Fadell-Husseini index of the pullback bundle of $s\gamma^{\perp}$ along the composition of the embedding between $G_2/SO(4)$ and $\widetilde{G}_{3}( \mathbb{R}^{7})$, and the fiber bundle $ G_2/U(2)_{\pm} \to G_2/SO(4)$. Here $s\gamma^{\perp}$ means the associated sphere bundle of the orthogonal bundle $\gamma^{\perp}$. Furthermore, we derive a general formula for the $n$-fold product bundle $(s\gamma^{\perp})^n$ for which we make the same computations.
2 2011 ASHRAE Handbook — HVAC Applications ( IP & SI )

Terminology. 42.2 METHODS . 42.4 Control Variables . 42.4 Supervisory Control Strategies . 42.5 Static Optimization . 42.5 Dynamic Optimization . 42.7 CONTROL STRATEGIES AND OPTIMIZATION FOR COOLING SYSTEMS . 42.10 Cooling Tower Fan Control Strategies . 42.10 Chilled-Water Reset with Fixed-Speed Pumping . 42.15 Chilled-Water Reset with Variable-Speed Pumping. 42.17 Sequencing and Loading Multiple Chillers . 42.22 Simplified Static Optimization of Cooling Plants . 42.30 Dynamic Optimization for Cooling using Discrete Storage . 42.39 Dynamic Optimization for Cooling using Thermal Mass or TABS . 42.44 Forecasting Diurnal Cooling and Whole-Building Electrical Demand Profiles . 42.52 Black Box Predictive Cooling Control Strategies . 42.54 CONTROL STRATEGIES FOR HEATING SYSTEMS . 42.55 CONTROL STRATEGIES FOR AIR-HANDLING UNITS . 42.59 CONTROL STRATEGIES FOR BUILDING ZONES . 42.61
Characteristics and Potential Production of Frozen Semen of Pasundan

Pasundan cattle are one of the Indonesian domestic resources of animal-genetic materials that need to be developed and conserved. The aims of this study were to evaluate the characteristics of sperm and the potential production of frozen semen of Pasundan bulls. Ten Pasundan bulls were divided into two groups based on their fresh sperm motilities. Pasundan bulls were grouped based on their sperm motilities into group A (70-79%) and group B (80-89%). Secondary data were collected and confirmed with the primary data. The semen volume, pH, color, consistency, mass movement, sperm concentration, motility, viability, membrane integrity, abnormality, and DNA integrity of fresh semen were evaluated. The semen was then diluted, frozen, and stored at -196°C. The results showed that the pH of the fresh semen in group B was lower (p<0.05) than group A. Sperm concentration per ejaculate showed no difference with a range of 4312.36x106 to 6303.52x106. The viability and DNA integrity of fresh semen were not different between group A (84.41±0.99%; 91.19±0.79%) and group B (86.35±2.16%; 92.58±0.35%). DNA integrity of frozen semen of group B (89.81±1.18%) was higher (p<0.05) than that of group A (86.83±0.60%). The sperm motility of frozen semen of group A (<40%) was lower compared to SNI number 4869-1:2017. The frozen semen production of Pasundan bulls was between 144.18 to 191.29 straws/ejaculate. In conclusion, only bulls from fresh-semen sperm motility 80%-89% (group B) are eligible to be used as a semen source for artificial insemination.
Reflecting stochastic dynamics of active-passive populations with applications in operations research and neuroscience

Stochastic dynamic models have been extensively used for the description of processes with uncertainties arising in the operations research, behavioral sciences, and many other application areas. A large class of the problems from these domains is characterized by the necessity to deal with several distinct groups of populations, which are usually labeled as"active"and"passive". Motivated by important applications of queueing networks and neuroscience, the main focus of the present work is on the analysis of reflecting stochastic dynamics of such mixed populations. We develop a general mathematical modeling framework to describe the reflecting stochastic dynamics for active-passive populations. The analysis of this model is carried out via a combination of low- and high-delity results obtained from the solution of the underlying coupled system of SDEs and from the simulations with a statistical-mechanics-based lattice gas model, where we employ a kinetic Monte Carlo procedure. We provide details of the queueing theory and neuronal models and discuss a relationship between reflecting SDEs and a model of queueing theory via a limit theorem. Furthermore, we present several representative numerical examples, and discuss an intrinsic interconnection between active and passive particles in the underlying stochastic process. Finally, possible extensions of the proposed methodology have been highlighted.
Electronic structure of cesium-based photocathode materials from density functional theory: performance of PBE, SCAN, and HSE06 functionals

The development of novel materials for vacuum electron sources in particle accelerators is an active field of research that can greatly benefit from the results of ab initio calculations for the characterization of the electronic structure of target systems. As state-of-the-art many-body perturbation theory calculations are too expensive for large-scale material screening, density functional theory offers the best compromise between accuracy and computational feasibility. The quality of the obtained results, however, crucially depends on the choice of the exchange–correlation potential, v xc. To address this essential point, we systematically analyze the performance of three popular approximations of v xc [PBE, strongly constrained and appropriately normed (SCAN), and HSE06] on the structural and electronic properties of bulk Cs3Sb and Cs2Te as representative materials of Cs-based semiconductors employed in photocathode applications. Among the adopted approximations, PBE shows expectedly the largest discrepancies from the target: the unit cell volume is overestimated compared to the experimental value, while the band gap is severely underestimated. On the other hand, both SCAN and HSE06 perform remarkably well in reproducing both structural and electronic properties. Spin–orbit coupling, which mainly impacts the valence region of both materials inducing a band splitting and, consequently, a band-gap reduction of the order of 0.2 eV, is equally captured by all functionals. Our results indicate SCAN as the best trade-off between accuracy and computational costs, outperforming the considerably more expensive HSE06.
Characterizing and Measuring the Similarity of Neural Networks with Persistent Homology

Characterizing the structural properties of neural networks is crucial yet poorly understood, and there are no well-established similarity measures between networks. In this work, we observe that neural networks can be represented as abstract simplicial complex and analyzed using their topological 'fingerprints' via Persistent Homology (PH). We then describe a PH-based representation proposed for characterizing and measuring similarity of neural networks. We empirically show the effectiveness of this representation as a descriptor of different architectures in several datasets. This approach based on Topological Data Analysis is a step towards better understanding neural networks and serves as a useful similarity measure.
Learning to infer in recurrent biological networks

A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most hypotheses of how the brain might learn these models assume that neurons in a population are conditionally independent given their common inputs. This simplification is likely not compatible with the type of local recurrence observed in the brain. Seeking an alternative that is compatible with complex inter-dependencies yet consistent with known biology, we argue here that the cortex may learn with an adversarial algorithm. Many observable symptoms of this approach would resemble known neural phenomena, including wake/sleep cycles and oscillations that vary in magnitude with surprise, and we describe how further predictions could be tested. We illustrate the idea on recurrent neural networks trained to model image and video datasets. This framework for learning brings variational inference closer to neuroscience and yields multiple testable hypotheses.
Agent-Level Maximum Entropy Inverse Reinforcement Learning for Mean Field Games

Mean ﬁeld games (MFG) facilitate the application of reinforcement learning (RL) in large-scale multi-agent systems, through reducing interplays among agents to those between an individual agent and the average effect from the population. However, RL agents are notoriously prone to unexpected behaviours due to the reward mis-speciﬁcation. Although inverse RL (IRL) holds promise for automatically acquiring suitable rewards from demonstrations, its extension to MFG is challenging due to the complicated notion of mean-ﬁeld-type equilibria and the coupling between agent-level and population-level dynamics. To this end, we propose a novel IRL framework for MFG, called Mean Field IRL (MFIRL), where we build upon a new equilibrium concept and the maximum entropy IRL framework. Crucially, MFIRL is brought forward as the ﬁrst IRL method that can recover the agent-level (ground-truth) reward functions for MFG. Experiments show the superior performance of MFIRL on sample efﬁciency, reward recovery and robustness against varying environment dynamics, compared to the state-of-the-art method.
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis

In recent years, several online platforms have seen a rapid increase in the number of review systems that request users to provide aspect-level feedback. Document-level Multi-aspect Sentiment Classification (DMSC), where the goal is to predict the ratings/sentiment from a review at an individual aspect level, has become a challenging and an imminent problem. To tackle this challenge, we propose a deliberate self-attention based deep neural network model, named as FEDAR, for the DMSC problem, which can achieve competitive performance while also being able to interpret the predictions made. As opposed to the previous studies, which make use of hand-crafted keywords to determine aspects in rating predictions, our model does not suffer from human bias issues since aspect keywords are automatically detected through a self-attention mechanism. FEDAR is equipped with a highway word embedding layer to transfer knowledge from pre-trained word embeddings, an RNN encoder layer with output features enriched by pooling and factorization techniques, and a deliberate self-attention layer. In addition, we also propose an Attention-driven Keywords Ranking (AKR) method, which can automatically discover aspect keywords and aspect-level opinion keywords from the review corpus based on the attention weights. These keywords are significant for rating predictions by FEDAR. Since crowdsourcing annotation can be an alternate way to recover missing ratings of reviews, we propose a LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context of multi-task learning, so that valuable human resources can focus on the most uncertain predictions. Our extensive set of experiments on five different open-domain DMSC datasets demonstrate the superiority of the proposed FEDAR and LEAD models. We further introduce two new DMSC datasets in the healthcare domain and benchmark different baseline models and our models on them. Attention weights visualization results and visualization of aspect and opinion keywords demonstrate the interpretability of our model and effectiveness of our AKR method.
METASTATIC CANCER Macrophages Polarization and Tumor Immunity in Metastatic Cancer : A Review

Objective: Tumour microenvironment plays an important role in the progression of cancer. This review focussed on current understanding on M1-M2 macrophage polarisation and the possible therapeutic intervention against metastatic cancer. Discussion: Macrophages exhibit a variety of responses according to varying stimuli, and express different functions depending upon the microenvironment surrounding them. Macrophages can be activated by a variety of stimuli and polarized to functionally different phenotypes. Two distinct subsets of macrophages have been proposed; classically activated (M1) macrophages and alternatively activated (M2) macrophages. M1 macrophages express a series of pro-inflammatory cytokines, chemokines, and effector molecules, such as IL-6, IL-12, IL-23, TNF-α, iNOS and MHCI/II. In contrast, M2 macrophages express a wide array of anti-inflammatory molecules, such as IL-10, TGF-β, and arginase1. Tumour associated macrophages (TAM) of M2 type secrete many cytokines, chemokines, and proteases, which promote tumor angiogenesis, growth, metastasis, and immunosuppression. M2 macrophages are responsible to drive cancer cells to blood and lymphatic circulation, thus worsen cancer prognosis. Conclusion: The polarization of macrophages towards an M1 response with minimal side effects may prove to be a powerful therapy against solid tumors. Thus, the development of effective strategies to tip the balance from M2 to M1 macrophages should be considered.
THE EFFECTS OF TALKER VARIABILITY AND FREQUENCY OF EXPOSURE ON THE ACQUISITION OF SPOKEN WORD KNOWLEDGE

Abstract Eighty Japanese learners of English as a foreign language encountered 40 target words in one of four experimental conditions (three encounters, six encounters, three encounters with talker variability, and six encounters with talker variability). A picture-naming test was conducted three times (pretest, immediate posttest, and delayed posttest) and elicited speech samples were scored in terms of form-meaning connection (spoken form recall) and word stress accuracy (stress placement accuracy and vowel duration ratio). Results suggested that frequency of exposure consistently promoted the recall of spoken forms, whereas talker variability was more closely related to the enhancement of word stress accuracy. These findings shed light on how input quantity (frequency) and quality (variability) affect different stages of lexical development and provide implications for vocabulary teaching.
Die Aggregation von Risiken im Kontext der Unternehmensplanung (The Aggregation of Risks in the Context of Corporate Planning)

<b>German Abstract:</b> Aussagen darüber zu treffen, welcher Risikosituation ein Unternehmen gegenüber steht und welche Auswirkungen dies auf den Unternehmenswert hat, erfordert eine zuverlässige Informationsbasis. Diese Basis wird geschaffen, wenn einzelne Risiken aggregiert werden und daraus z.B. Aussagen über das notwendige risikobedingte Eigenkapital getroffen werden. Als Aggregationsverfahren bietet sich hier die Monte-Carlo-Simulation an, die simultan die Wirkung aller Einzelrisiken auf die Unternehmensplanung (GuV und Bilanz) bestimmt. Die Ergebnisse der Risikoaggregation sind die Informationsgrundlage, um z. B. die Kapitalkostensätze eines Unternehmens oder den Wertbeitrag von Versicherungen berechnen zu können, kurz: die Risikoaggregation schafft die Verbindung von Risikomanagement, Controlling und wertorientierter Unternehmensführung. <br><br><b>English Abstract:</b> Making statements about the risk situation facing a company and the effects this has on the value of the company requires a reliable basis of information. This basis is created when individual risks are aggregated and statements are made about the necessary risk-related equity, for example. Monte Carlo simulation is a suitable aggregation method here, which simultaneously determines the effect of all individual risks on corporate planning (income statement and balance sheet). The results of risk aggregation provide the information basis for calculating, for example, the cost of capital of a company or the value contribution of insurance policies. In short, risk aggregation creates a link between risk management, controlling and value-oriented corporate management.
The Determinants and Consequences of Information Acquisition via EDGAR

Using a novel dataset that tracks all web traffic on the SEC’s EDGAR servers from 2008-2011, we examine the determinants and capital market consequences of investor information acquisition of SEC filings. The average user employs the database very few times per quarter and most users target specific filing types such as periodic accounting reports; a small subset of users employ EDGAR almost daily and access many filings. EDGAR activity is positively related with corporate events (particularly restatements, earnings announcements, and acquisition announcements), poor stock performance, and the strength of a firm’s information environment. EDGAR activity is related to, but distinct from, other proxies of investor interest such as trading volume, business press articles, and Google searches. Finally, information acquisition via EDGAR, both to obtain earnings news and to provide context for it, has a positive influence on market efficiency with respect to earnings news. Overall, our results provide a unique, user-based perspective on investor access of mandatory disclosures and its impact on price formation.
More than Connectedness — Heterogeneity of CEO Social Network and Firm Value

This paper examines through various channels the effects of CEO social network heterogeneity on firm value. We construct four measures of heterogeneity based on demographic attributes, intellectual backgrounds, professional experience, and geographical exposures of individuals in the CEO social network. We find that CEO social network heterogeneity leads to higher Tobin's Q of firms. Greater CEO social network heterogeneity also leads to: (i) more innovation, (ii) more foreign sales growth, (iii) higher investment sensitivity to Tobin’s Q, and (iv) better M&A performance. Overall, our results indicate that CEO social network heterogeneity is an aspect of CEO social capital and soft skills that deserves the attention of shareholders.
The same yet different: the effects of vividness in a laboratory asset market

We investigate the impact of vividness on trading behavior in an experimental asset market, by contrasting vivid with pallid presentation of information. Vivid information is characterized as emotionally engaging and proximate, while pallid information is purely factual. Our design enables us to isolate the effect of vividness by controlling the content of the information irrespective of the presentation. We find that vividness elevates investor attention, as measured through order-book composition and market variables. Interestingly, vividness can both distract as well as focus investors’ attention depending on the nature of the underlying information. We also find evidence that vividness has a catalyzing effect on existing behavioral biases. Independent of information content, vividness exacerbates difference of opinion amongst investors with heterogeneous priors. In addition, we find that strong market sentiment develops when a large proportion of overconfident investors receive prior-confirming information in a vivid manner. This results in the concurrence of high sentiment, returns and turnover.
Save Haven Currencies

We study high-frequency exchange rates over the period 1993-2008. Based on the recent literature on volatility and liquidity risk premia, we use a factor model to capture linear and non-linear linkages between currencies, stock and bond markets as well as proxies for market volatility and liquidity. We document that the Swiss franc and Japanese yen appreciate against the US dollar when US stock prices decrease and US bond prices and FX volatility increase. These safe haven properties materialise over different time granularities (from a few hours to several days) and non-linearly with the volatility factor and during crises. The latter effects were particularly discernible for the yen during the recent financial crisis.
Comparing Alternative Hedge Accounting Standards: Shareholders? Perspective

We study the economic consequences of alternative hedge accounting rules in terms of managerial hedging decisions and wealth effects for shareholders. The rules we consider include the "fair-value" and "cash-flow" hedge accounting methods prescribed by the recent SFAS No. 133. We illustrate that the accounting method used influences the manager's hedge decision. We show that under no-hedge accounting, the hedge choice is different from the optimal economic hedge the firm would make under symmetric and public, information. However, under a certain definition of fair-value hedge accounting, the hedging decision preserves the optimal economic hedge. We then demonstrate that long-term and future shareholders prefer a certain definition of fair-value hedge accounting to no-hedge accounting, while short-term shareholders prefer either approach depending on risk preferences and the level of uncertainty. We speculate about circumstances in which a manager would choose not to adopt fair-value hedge accounting when he has the option not to do so.
ZnO-Modified MoO3 Nano-Rods, -Wires, -Belts and -Tubes: Photophysical and Nonlinear Optical Properties

We report a versatile approach to obtain MoO3 nanostructures such as nanorods, nanowires, nanobelts, and nanotubes in thin film form on glass substrates, by incorporating ZnO, via RF magnetron sputtering and controlled subsequent oxidation. The nanostructures growth mechanism has been elucidated on the basis of strain field associated with defect-oriented partial screw dislocation induced by ZnO for the drastic variation of the morphology with respect to ZnO incorporating levels from initial tiny nanorods (pure MoO3) to larger nanorods (at 1%), then to aligned and tilted nanowire arrays (at 3 and 5% respectively), afterward to nanobelts (at 7%), and finally to nanotubes (at 10%). Novel properties of ZnO-incorporated MoO3 nanostructures like enhanced photoluminescence and optical limiting have been brought out. This study opens the door to the potentiality of ZnO-added MoO3 nanostructures to be used as luminescent transparent conducting materials, saturable absorbers, and optical limiters.