Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Uncertainty and Its Effects in the COVID-19 Era

We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty, in addition to idiosyncratic components. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. We also consider an extended version of the model, based on a latent state approach to accommodating outliers in volatility, to reduce the influence of extreme observations from the COVID period. The estimates we obtain yield very large increases in macroeconomic and financial uncertainty over the course of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but with both models, the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method).
The role of supply chain analytics capability and adaptation in unlocking value from supply chain relationships

Abstract The performance and survival of many supplier firms depend on key buyer–supplier relationships (BSRs). A difficult task in managing such relationships is creating value when there is a lack of justice. This study examines whether supply chain analytics capability (SCAC) and adaptation can prove useful for suppliers in extracting greater value from supply chain relationships. The role of SCAC in driving value has not been evaluated in contexts where independent decision making is constrained, such as the case for a power disadvantaged supplier. We use resource orchestration theory and social exchange theory to develop a model that explains how SCAC and adaptation can work in BSRs and test it using data from North American suppliers. The results show that in relationships marked by low levels of procedural justice, SCAC becomes critical in extracting value from the relationship, while in relationships marked by high levels of procedural justice, adaptation is more important. The findings lead to actionable insights on how suppliers can leverage SCAC and adaptation to maximize value from supply chain relationships. A key insight is that benefits from firm-level capabilities, like SCAC, depend on relational attributes in the supply chain context.
Modular Neural Networks for Low-Power Image Classification on Embedded Devices

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images into groups of categories based on a novel visual similarity metric. Once a group of categories is selected by a module, another module then continues to distinguish among the similar categories within the selected group. This process is repeated over multiple modules until we are left with a single category. The computation needed to distinguish dissimilar groups is avoided, thus reducing redundant operations, memory accesses, and energy. Experimental results using several image datasets reveal the effectiveness of our proposed solution to reduce memory requirements by 50% to 99%, inference time by 55% to 95%, energy consumption by 52% to 94%, and the number of operations by 15% to 99% when compared with existing DNN architectures, running on two different embedded systems: Raspberry Pi 3 and Raspberry Pi Zero.
Modeling recovery rates for small- and medium-sized entities in the US

A sound statistical model for recovery rates is required for various applications in quantitative risk management. We compare different models for predicting the recovery rate on borrower level including linear and quantile regressions, decision trees, neural networks and mixture regression models. We ﬁt and apply these models on the worldwide largest loss and recovery dataset for commercial loans provided by Global Credit Data, where we focus on small- and medium-sized entities in the US. Additionally, we include macroeconomic information via a predictive Crisis Indicator. The horserace is won by the mixture regression model with regressed weight probabilities. for clas-si(cid:28)cation result in lower MSEs than the models with the logistic regressions. This (cid:28)nding might be explained by the slightly lower prediction error of the neural network for the classi(cid:28)cation whether the RR attains values smaller than or equal to 0. However, the di(cid:27)erences in the MSE are marginal and since the logistic regression gives some more insight in the determinants of the RR, it might be preferable to use them. We compare the results of the decision tree approach with the regressions on the entire dataset and recognize that the models on the entire data result in lower MSEs than the decision trees.
LapFormer: surgical tool detection in laparoscopic surgical video using transformer architecture

ABSTRACT One of the most essential steps in the surgical workflow analysis is recognition of surgical tool presence. We propose a method to detect the presence of surgical tools in laparoscopic surgery videos, called LapFormer. The novelty of LapFormer is to use a Transformer architecture, which is a feed-forward neural network architecture with attention mechanism, growing in popularity for natural language processing, for analysing inter-frame correlation in videos instead of using recurrent neural network families. To the best of our knowledge, no methods using a Transformer architecture for analysing laparoscopic surgery videos have been proposed. We evaluate our method on a dataset called Cholec80, which contains 80 videos of cholecystectomy surgeries. We confirm that our proposed method outperforms the conventional methods such as single-frame analysis with convolutional neural networks or multiple frame analysis with recurrent neural networks by 20.3 and 17.3 points in macro-F1 score, respectively. We also conduct an ablation study on how hyper-parameters for Transformer block in our proposed method affect the performance of the detection.
Quality of teaching in higher education: reviewing teaching behaviour through classroom observations

ABSTRACT This study examines the extent to which lecturers demonstrate effective teaching behaviour. The results of 203 observations reveal substantial differences in detected teaching behaviour. Lecturers mostly demonstrated teaching behaviour in the domains classroom climate, efficient organisation, and instruction. Teaching behaviour relating to the domains activating teaching and teaching learning strategies was observed less frequently, with almost no evidence of behaviour associated with the domain differentiation. The quality of teaching in small classes was slightly higher than that in large classes. These findings can help tailor professional learning activities to lecturers’ professional development needs.
‘Sensations of Rootedness’ in Cosmopolitan Rangoon or How the Politics of Authenticity Shaped Colonial Imaginings of Home

ABSTRACT Colonial Rangoon was once a famously cosmopolitan city, a significant trade port of the British Empire and one of the world’s largest migrant destinations. Mobility, foreignness and cross-cultural hybridity was essential to Rangoon’s make-up during the colonial period. This paper compares three texts, each written from one of Rangoon’s resident mobile cultural identities, that represent ‘sensations of at-homeness’ in the colony. I analyse associations and metaphors of ‘rootedness’ in the personal correspondence of Gordon Luce, the self-published memoir of Kenneth Tun Tin and an interview with a Japanese businesswoman, Ikuyo Okuma. Drawing on Kristeva’s (1991. Strangers to Ourselves. New York: Columbia University Press) examination of the figure of foreigner in Western society in Strangers to Ourselves, as well as Greenblatt’s (2009. Cultural Mobility, A Manifesto. Cambridge: Cambridge University Press) Manifesto, I argue that ‘sensations of rootedness,’ imagined through associations with blood and soil, may be expressed as maintaining equilibrium. This paper contributes to understandings of ‘plurality’ in colonial Burma and ‘Anglo-Burmese’ experiences and identities.
Back to the Origins of the Initial Public Offerings Price Range: Underwriter-Funds Network and Information Production Timeline

Although the initial price range in U.S. Initial Public Offerings (IPOs) is constrained by SEC regulations, a non-negligible percentage of IPO price ranges falls outside the ‘safe harbour’. We investigate how the price range - which sends the very first signals on the IPO quality to the market - is set in the due diligence phase, with special attention to unexplored networking patterns between underwriters and institutional investors. By making use of a Mixture Model applied to 1,246 US firms listed between 2004 and 2016, we show that underwriters that are centrally positioned in their network of regular investors are more likely to set a price range that is compliant with SEC guidelines. We argue that the flexibility resulting from being safe harbour-compliant allows underwriters to preserve their reputation for fair dealing with issuers by exploiting a dumping ground proviso or quid pro quo agreements with their network funds. Despite information produced by network funds in the due diligence step having no significant effect on the width of the price range, in our study, we provide evidence that the range does serve as a proxy of the uncertainty of the listing firms.
Behavioural Finance and Investment Decisions: Does Behavioral Bias Matter?

This paper examines the nexus between behavioural bias and investment decisions in a developing country context. Specifically, this study tests the effect of four behavioural biases (overconfidence, regret, belief, and “snakebite”) on investment decisions. Descriptive statistics and inferential statistics including multiple regression are used to examine the behavioural biases-investment decisions nexus. The study reveals that the four bias have a significant positive and robust relationship with investment decision making. The result also shows that the "snakebite" effect contributes more to the decision making, followed by belief bias then regret bias. Overconfidence bias, however, contributes the least effect on investment decisions. Our contribution confirms the prospect theory and that behavioural bias influences investment decisions in the developing country perspective.
Effect of Sowing Dates on The Competision Between Faba Bean (Vicia Faba, L.)Genotypes and The Parasitic Weed, "Orobanche crenata, Forsk."

Marwa Kh. A. Mohamed And Azza F. El-Sayed Food Legumes Research Department (FLRD), Field Crops Research Institute (FCRI), Agricultural Research Center (ARC), Giza, Egypt. BSTRACT This investigation was carried out under naturally Orobanche infested fields, at Sakha Agricultural Research Station Kafr El-Sheikh Governorate, Egypt. during 2015/16 and 2016/17 winter growing seasons to investigate the effect of sowing date of six faba bean. genotypes(Sakha1,Sakha3,Giza843,Misr3,(Misr1*Misr2)and((Sakha1*Misr1)*(Misr1*Misr2))) on the behaviour of Orobanche. The experiment was carried out under two sowing dates (November first and November 30). A split plot design, with three replications, was used for each date. Each plot consisted of three ridges, 3m long and 60 cm apart. The seeds were sown in hills distanced at 20 cm. on the two sides of the ridge, at the rate of 2-seeds/ hill. The analysis of variance indicated that sowing dates highly significantly affected faba bean yield and its attributes in the two growing seasons, except for that No. of branches/plant, No. of seeds/plant and seed yield/plant in both seasons, and number of pods/plant, in the first season. Delaying sowing date from Nov. 1 to Nov. 30 significantly decreased plant height, No. of pods/plant in the second season, 100 seed weight (g), in both seasons, seed yield (ardab/fed.), in the first season and No. of Orobanche spikes/plot in both seasons. The lowest values were given by sowing on Nov.30 The seed yield and its attributes in all evaluated faba bean genotypes were significantly different in the two seasons. Misr1*Misr2 genotype recorded the highest values of seed yield and its attributes. , No. of seeds/plant and seed yield/plant of Misr3 recorded the highest values of seed yield (ardab/fed.) On the other hand, Sakha1and Sakha3 recoded the lowest values. Results showed that the interaction between genotypes and sowing date recorded the highest value of seed yield (ardab/fed.) with Misr3, planted in early sowing date. On the other side,the late sowing date with all genotypes recorded higher seed yield and resistant to Orobanche in the two growing seasons, than the early sowing date.
Peripheral mitochondrial function in multiple organ dysfunction syndrome

II ACKNOWLEDGMENTS . IV LIST OF FIGURES . XIII LIST OF TABLES . XV ABBREVIATIONS . XVI CHAPTER 1 MULTIPLE ORGAN DYSFUNCTION SYNDROME IN CRITICAL ILLNESS . 1 1.1 GROWING INCIDENCE OF MULTIPLE ORGAN DYSFUNCTION SYNDROME IN CRITICAL ILLNESS . 2 1.2 POOR PROGNOSIS OF MODS IN CRITICAL ILLNESS . 3 1.3 SUMMARY OF RESEARCH INTERVENTIONS IN MODS . 4 1.4 MITOCHONDRIAL PATHOPHYSIOLOGY IN MODS . 5 1.5 CONCLUSION . 6 CHAPTER 2 PATHOPHYSIOLOGY OF MODS . 7 2.1 PROINFLAMMATORY RESPONSE . 8 2.2 HYPOINFLAMMATORY RESPONSE. 9 2.3 APOPTOSIS . 10 2.4 GUT . 10 2.5 NEUROHORMONAL . 11 2.6 COAGULATION . 11 2.7 MACROCIRCULATION . 12 2.8 MICROCIRCULATION . 12 2.9 MITOCHONDRIAL DYSFUNCTION . 12 2.9. 1 Mitochondrial respiration . 13 2.9. 2 Electron transport system . 13 2.9. 3 The proton motive force . 14 2.9. 4 Other mitochondrial function . 15 2.9. 5 Mitochondria and inflammation . 15 2.9. 6 Mitochondrial function in sepsis and MODS . 16 2.9. 7 The need for a peripheral marker of mitochondrial function . 18 2.10 CONCLUSION . 19 CHAPTER 3 WHITE BLOOD CELL MITOCHONDRIAL RESPIRATORY FUNCTION IN DISEASE STATES . 20 3.
The effect of grazing on winter survival of midday gerbil (Meriones meridianus) of different genders

Abstract The objective of this study was to investigate the effects of grazing on midday gerbil (Meriones meridianus) population characteristics and survival of animals of different genders. The experiment used a randomized complete block design and was conducted in Alxa Left Banner, Inner Mongolia, China, in 2002 (The agricultural reclamation plots set up in 1994). From April 2006 to October 2010, midday gerbils were live‐trapped in 3 light grazing plots, 3 overgrazed plots, and 3 grazing exclusion plots. The quantity of vegetation was investigated in the two different grazing intensity areas and grazing exclusion area to determine the relationship between gerbils and plant food availability. The results suggested that there was higher gerbil density, individual body mass, and daily body mass growth rate in the grazing exclusion sites than the other sites across the whole year. Females had higher survival in grazing exclusion areas than in other treatments, but the males’ survival showed the opposite pattern. Our results indicated that grazing negatively influenced the midday gerbil population by reducing food availability. Grazing influenced the survival rates of male midday gerbils positively, but had negative effects on females. The reason for gendered differences in survival rates of midday gerbils requires further investigation.
Materyalizm ve Tüketici Uzmanlığının Reklamlara Yönelik Tutumlara Etkisi: Alt Gelir Grubu Tüketiciler Üzerine Bir Araştırma

Bu arastirmanin amaci materyalizm ve tuketici uzmanliginin reklamlara yonelik tutum uzerindeki etkisini incelemektir. Kesfedici bir amacla tasarlanan bu arastirma bir arastirma modelinin test edilmesine dayanmaktadir. Veriler Turkiye’nin Ege Bolgesi’nde yasayan alt gelir grubu mensubu 829 kisiden yuz yuze anket araciligiyla toplanmistir. Veri toplama islemi 5 Mart 2019 ve 31 Mayis 2019 tarihleri arasinda yurutulmustur. Arastirma modelinin testi icin LISREL 9.30 programi kullanilmistir. Arastirmanin sonuclari, materyalizm ve reklamlara yonelik tutum arasinda anlamli bir iliski oldugunu gostermistir. Materyalizm reklamlara yonelik tutumu pozitif yonde etkilerken, reklamlara yonelik tutum materyalizmi negatif yonde etkilemektedir. Ayrica, musteri uzmanliginin reklamlara yonelik tutum uzerinde dogrudan ve pozitif yonde bir etkisi oldugu gorulmustur. Bu calisma, tuketici uzmanligi, materyalizm ve reklama karsi tutum baglaminda literature katkida bulunmaktadir. Arastirma sonuclarinin ayrica bir pazar bolumu olarak sikca gozden kacirilan alt gelir grubu tuketicilere yonelik reklam kampanyalarinin yonetiminde uygulayicilara faydali olmasi beklenmektedir.
Dynamic Assortment Planning Under Nested Logit Models

We study a stylized dynamic assortment planning problem during a selling season of finite length T. At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the seller is to maximize the expected revenue, or equivalently, to minimize the worst‐case expected regret. One key challenge is that utilities of products are unknown to the seller and need to be learned. Although the dynamic assortment planning problem has received increasing attention in revenue management, most existing work is based on the multinomial logit choice models (MNL). In this paper, we study the problem of dynamic assortment planning under a more general choice model—the nested logit model, which models hierarchical choice behavior and is “the most widely used member of the GEV (generalized extreme value) family” (Train 2009). By leveraging the revenue‐ordered structure of the optimal assortment within each nest, we develop a novel upper confidence bound (UCB) policy with an aggregated estimation scheme. Our policy simultaneously learns customers’ choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the accumulated regret at the order of O ∼ ( MNT ) , where M is the number of nests and N is the number of products in each nest. We further provide a lower bound result of Ω ( MT ) , which shows the near optimality of the upper bound when T is much larger than M and N. When the number of items per nest N is large, we further provide a discretization heuristic for better performance of our algorithm. Numerical results are presented to demonstrate the empirical performance of our proposed algorithms.
Detecting Selective Forwarding using Sentinels in lustered IoT Networks

Compromised relays in clustered IoT networks can be used to launch attacks that cannot be easily detected using the traditional security algorithms. In this paper, we consider an attack where a compromised relay deliberately drops the packets received from the IoT devices it serves. Such an attack causes the IoT devices to retransmit more frequently, thereby increasing their processing load. As a result, their batteries will drain at a faster rate. The difficulty in differentiating a genuine packet drop event from a malicious packet drop event makes it necessary to develop a novel Intrusion Detection System (IDS) specially tailored for detecting such an attack. The IDS is installed in a special node called a sentinel, which monitors the network. The sentinel also estimates the packet retransmission rate of the IoT devices, a parameter required for the IDS. The effectiveness of the system is demonstrated experimentally on a clustered network.
Normal Mode Analysis: A Tool for Better Understanding Protein Flexibility and Dynamics with Application to Homology Models

Molecular dynamics (MD) and normal mode analysis (NMA) are very useful methods for characterizing various dynamic aspects of biological macromolecules. In comparison to MD, NMA is computationally less expensive which facilitates the quick and systematic investigation of protein flexibility and dynamics even for large proteins and protein complexes, whose structure was obtained experimentally or in silico. In particular, NMA can be used to describe the flexible states adopted by a protein around an equilibrium position. These states have been repeatedly shown to have biological relevance and functional significance. This chapter briefly characterizes NMA and describes the elastic network model, a schematic model of protein shape used to decrease the computational cost of this method. Finally, we will describe the applications of this technique to several large proteins and their complexes as well as its use in enhancing protein homology modeling.
Performance of backgrounding steers fed diets containing monensin or a lactobacillus fermentation product1

© The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. Transl. Anim. Sci. 2018.2:S130–S133 doi: 10.1093/tas/txy035
Content of bioactive compounds and antioxidant activity in garlic (Allium sativum L.)

Garlic (Allium sativum L.) is highly regarded throughout the world for both its medicinal and culinary properties. With its bioactive compounds and sulphur containing compounds, high trace mineral content, garlic has shown anti-viral, anti-bacterial, anti-fungal and antioxidant abilities.This work has focused on the measurement and comparison of the total polyphenols content, the total sulphur content and antioxidant activity of the studied varieties of garlic in the area of Nitra and Bardejov, Slovak Republic. The highest content of monitored indicators (TPC, TSC, AOA) was measured in the area of Nitra. TPC was determined using the spectrophotometric method of Folin-Ciocalteu agents. The total polyphenols content were determined in the range 621.13 to 763.28 mg kg-1 in area of Nitra. The content of total sulphur compounds in the area of Nitra ranges from 0.562 to 0.800 % and in the second area ranges from 0.421 % to 0.658 %. Antioxidant activity was measured by the spectrophotometric method using a compound DPPH. The value of antioxidant activity ranged from 12.01 % to 20.22 % in both monitored locations. The content of TPC, TSC and AOA beside the variety may be affected by the localitylinked factors like climatic conditions and the agrochemical composition of the soil.