Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Monetary Policy Spillovers between U.S. And German Bond Yields

In this paper we estimate the magnitude of spillovers between bond markets in the U.S. and Germany following monetary policy communications by the FOMC and the ECB. The identification of policy-related co-movements following FOMC announcements, in particular, can be difficult because many foreign bond markets, including those in Germany, are closed at the time of the announcement. To address this issue we use intraday futures market data to estimate spillovers during a narrow and overlapping event window. We find that about half of the reaction in German domestic yields spills over to U.S. yields following ECB announcements, which is nearly identical to the spillover from U.S. yields to German Bund yields following FOMC announcements. This result contrasts with the conventional wisdom that FOMC announcements spill over to other countries but that there is not much effect in the other direction. We also find that spillover estimates are slightly higher in the post-crisis period, but that there is little difference in the spillover impact of conventional versus unconventional monetary policy. Our results based on futures prices differ noticeably from those using daily prices, which suggests that spillover estimates based on cash market data can be misleading.
Pituitary Corticotroph Microadenomas VersusMacroadenomas Causing Cushing’s Disease

Purpose: Pituitary corticotroph macroadenomas are accused for a minority of cases of Cushing’s disease but their actual prevalance is believed to be underestimated. Further, clinical and biochemical manifestations of corticotroph macroadenomas and microadenomas differ widely and only small numbers of patients have been included for analysis in previous studies. We aimed to analyze and compare the clinical characteristics and biochemical features of patients with corticotroph macroadenomas and microadenomas causing Cushing’s disease. Methods: We included 107 patients with Cushing’s disease (67 microadenomas and 40 macroadenomas) in this analysis. Clinical characteristics, biochemical analysis of hypercortisolism, pituitary imaging fi ndings, treatment modalities, remission as well as recurrence rates were recorded. Results: Mean age of patients at diagnosis was 46.5 ± 11.4 years for macroadenomas and 37.3 ± 12.1 years for microadenomas (p < 0.001). Gender distribution was equal in macroadenoma patients whereas female predominance was apparent in microadenoma patients (p = 0.001). Compared to macroadenoma patients, clinical evidence of hypercortisolism was more pronounced with microadenomas. Although the presentation of clinical manifestations was different, biochemical analysis of hypercortisolism including basal cortisol, ACTH, 24-hour urinary cortisol levels, and responsivity to low-dose dexamethasone suppression were similar between the groups. Suppressability of serum cortisol after high-dose dexamethasone was lower in macroadenomas but did not reach signifi cance (p = 0.074). Remission rates after initial pituitary surgery (67.5 % vs 82.8 %) and recurrence rates in patients followed beyond 6 months were similar between groups. Cavernous sinus invasion was found to be a negative predictor of remission. Conclusion: Patients with corticotroph macroadenomas and microadenomas share similar biochemical evidence of hypercortisolism, remission and recurrence rates but presenting clinical manifestations are different. Cavernous sinus invasion is a negative predictor of remission.
Deep Reinforcement Learning for Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic management. However, operating viable real-time actuation mechanisms on a practically relevant scale involves formidable challenges, e.g., policy iteration and conventional Reinforcement Learning (RL) techniques suffer from poor scalability due to state space explosion. Motivated by these issues, we explore the potential for Deep Q-Networks (DQN) to optimize traffic light control policies. As an initial benchmark, we establish that the DQN algorithms yield the "thresholding" policy in a single-intersection. Next, we examine the scalability properties of DQN algorithms and their performance in a linear network topology with several intersections along a main artery. We demonstrate that DQN algorithms produce intelligent behavior, such as the emergence of "greenwave" patterns, reflecting their ability to learn favorable traffic light actuations.
Global Rigidity of Unit Ball Graphs

A $d$-dimensional bar-and-joint framework $(G,p)$, where $G$ is a graph and $p$ maps the vertices of $G$ to points in $\mathbb{R}^d$, is said to be globally rigid if every $d$-dimensional framework.
SOJOURN TIME TAILS IN THE M/D/1 PROCESSOR SHARING QUEUE

We consider the sojourn time V in the M/D/1 processor sharing (PS) queue and show that P(V > x) is of the form Ce−γx as x becomes large. The proof involves a geometric random sum representation of V and a connection with Yule processes, which also enables us to simplify Ott's [21] derivation of the Laplace transform of V. Numerical experiments show that the approximation P(V > x) ≈ Ce−γx is excellent even for moderate values of x.
Household Responses to Information on Child Nutrition: Experimental Evidence from Malawi

This paper provides evidence on household responses to the relaxation of one barrier constraining adoption of health practices - lack of information - in a resource constrained setting. It examines the effects of a randomized intervention in Malawi which provides mothers with information on infant nutrition and health. It finds that the intervention results in increases in household food consumption, particularly of protein-rich foods by children. The increased household consumption is funded by increased father’s labor supply, constituting evidence that changes in the perceived child health production function affect adult labor supply. Improved consumption also results in better child health.
Affect-based Social Image Search Results Summarization

Affect-based social image search results summarization is essential for efficiently visualizing search results and reranking them. In this paper, we present the selection of representative images based on human affects. The proposed method consists of three main stages: 1) The images are transformed into the affective space using convolutional neural network, which can automatically find the correlation between visual features and certain affective classes; 2) Images are clustered on affective space and then the resulting clusters are ranked based on the proposed three properties – coverage, affective coherence, and distinctiveness; 3) Representative images are selected from top-ranked clusters. The experiments were conducted on Flickr images and showed the effectiveness of the proposed method. Keywords-Image summarization; Human affects; Social Images; Ranking model; Convolutional Neural Network
ANN-Based Autonomous Navigation for a Mobile Robot Examinations in Effective ANN Layout Design and Training

In this thesis ANN layout and training methods for ANN based autonomous navigation is discussed. ANNs are trained to recognise a set of predefined situations. Movement commands are associated with every situation to achieve autonomous navigation. Experiments were conducted in the EyeSim simulator and with robots of the EyeBot family. Measurements from a laser distance sensor serve as input to the ANNs. The scenarios examined are autonomous maze and open-world way-point navigation. The process of finding a suitable ANN layout and training procedure is discussed for both scenarios. This includes determining the number of neurons for the input and hidden layer of the ANNs, a suitable stopping condition for the training and the number of training datasets. Further, performance enhancing manipulations on the data fed to the ANNs are discussed. Based on the developed ANNs, autonomous navigation for both scenarios is implemented on the EyeBot robots. The experiments show that ANNs with distance measurements as input can serve as a basis for autonomous navigation in indoor environments. Further it is shown, that economical choices in ANN neurons and training datasets can be made without limiting performance. This can be beneficial for systems with limited computational power and when the availability of training data is limited.
Gestational Diabetes Mellitus and Associated Impending Non-communicable Disease Epidemic: A Perspective for Actions

: Diabetes Mellitus is emerging as an epidemic globally; the major causative factors involved are both environmental and genetic, affecting the intrauterine exposures to the fetus as well. If stimulus occurs to be glucose intolerance during pregnancy, gestational diabetes Mellitus (GDM) establishes. Hyperglycemia poses serious immediate adverse consequences for both mother and foetal development by permanently changing physiology and metabolism. The objective of this review was to identify the global key foetal complications, risk assessment and preventive solutions to avoid T2DM epidemics. Search was done through PubMed, Google Scholar and Aga Khan University, Karachi Campus Library resources. Literature indicated that gestational diabetes is strongly associated with higher birth weights, cesarean section of mother and risk of infant shoulder dystocia, Erb’s palsy, clavicular fractures, fetal distress, and birth asphyxia resulting in 30-50 % of perinatal mortality. Respiratory distress syndrome (31%) of infants and cardiac septal hypertrophy was seen in 35-40 % of cases worldwide. GDM play a crucial role in increasing prevalence of diabetes, obesity and metabolic syndrome. Use of insulin therapy has decreased the incidence of foetal macrosomia however, the extent of any effect on maternal and neonatal health outcomes are uncertain. Policy makers need to work at ;1) to prevent the development of GDM per se, may implement appropriate guidelines such as ADA/WHO by which patients should be screened for risk factors for GDM at their initial visit and 2) to organize program for reducing the incidence of type 2 DM and non-communicable diseases .
Challenges and Opportunity in 3 D Integration Packaging

Demand for 3D integration is being driven by the need for 3D stacking to shorten interconnection length, increase signal speed, reduce power consumption and reduce power dissipation. Increasing demand for new and more advanced electronic products with a smaller form factor, superior functionality and performance with a lower overall cost has driven the semiconductor industry to develop more innovative and emerging advanced packaging technologies. 3D packaging using the z-axis TSV stacking concept has been investigated by a number of semiconductor manufacturers and research institutes, and is believed to be one of the most promising technologies. There is a growing interest in the development and application of this new chip stacking approach to existing and future devices. There are several steps involved in 3D chip stacking using TSV technology. Each of these steps requires different techniques, materials and processes. Applications have to be well understood and integrated in order to successfully be applied. This paper addresses 3D integration technology, TSV interposer technology, challenges of assembly and packaging, . 3D eWLB with TSV technology and future applications of 3D TSV integration.
Hydrothermal Synthesis and Study of an Inorganic-Organic Hybrid Vanadate of a Nickel ( II ) Coordination Complex with Pyrazine , Ni 3 ( C 4 H 4 N 2 ) 3 ( V 8 O 23 )

The three-dimensional hybrid compound Ni3(C4H4N2)3(V8O23) has been synthesized by mild hydrothermal methods under autogenous pressure at 170 oC. The structure of the phase is stable until 380 oC. The removal of the pyrazine molecules from the structure induces its collapse. The IR spectrum shows the vibration modes of the pyrazine molecule and those of the [VO4] 3groups. A UV-visible spectrum shows the characteristic bands of the Ni(II) d 8 -high-spin cation in a slightly distorted octahedral coordination. Magnetic measurements indicate the existence of antiferromagnetic interactions that can be fitted with a chain model to give g = 2.31, J/k = -5.3, and zJ’/k = -5.5.
System Identification and Adaptive Control of Planar Cable Robots

Cable-based robots generally perform better than other parallel robots with rigid links in terms of wider workspace and higher acceleration of end-effector because of light weight of robot links. Cable robots allow easy installation for outside applications at the expense of the requirement of a precise assembly of its components at the cable anchor points. In this study, system identification and adaptive control of cable planar robots are considered. Firstly, a parametric model is developed for estimation of position errors of anchor points for fully-constrained and redundant planar cable robots. A novel method based on inclusion of virtual cables facilitates the linear separation of the uncertain parameters from the input-output signals for redundant planar robots. A least-squares and gradient based parameter estimation algorithm provide the estimates of the system parameters. Further, this work deals with the design and comparison of three adaptive position control schemes combined with a classical PID controller for fully constrained and redundant planar robots. Then, three Lyapunov based adaptive controllers based on the (i) sliding mode, (ii) PD and (iii) backstepping schemes are designed to compensate for the matrix uncertainties appear in the system dynamics resulting from errors in the anchor point locations. Next, the adaptive controllers are evaluated and compared with a classical PID controller through simulations for a desired 2D singularity-free pose of the mobile platform. Supremacy of the PD control scheme over the aforementioned control schemes is observed through simulations. The least-squares algorithm is compared with the gradient-based method in terms of the speed of convergence of the estimated parameters as well. Finally, we establish linear parametric forms for common friction models, recursive least-squares on-line friction estimator is presented and adaptive friction compensation scheme is designed. The efficiency of this design is discussed via simulation results.
Investigating radio-frequency identification usage behaviours and organisational performance according to factors of user perception

Information technology has developed rapidly in the past decades, in particular the use of service-oriented architectures, which have been widely adopted in Taiwan's healthcare industry. Radio-frequency identification (RFID) is one of the most widely applied new technologies. Using the unified theory of acceptance and use of technology model, this study investigated the effect of perceived security (PS) and privacy concerns (PC) on health professionals' RFID usage behaviour (UB), and the effect of UB on organisational performance (OP). A questionnaire survey was used and 410 valid questionnaires were returned. The results of path analysis revealed that performance expectancy (PE), effort expectancy (EE), social influence, and facilitating conditions (FC) exert a significantly positive influence on UB; PS has a significantly positive effect on PE and PC; and UB exerts a significantly positive influence on OP. Different demographic variables also moderated the effect of PE, EE, and FC on RFID UB.
Reinforcement learning from multiple experts demonstrations

A major limitation of most Reinforcement Learning (RL) methods is low sample efficiency. One way to address this problem is to employ human expert demonstrations to speed up the RL process (Reinforcement Learning from Demonstration, or RLfD). The research so far has focused on demonstrations from a single expert, assuming that this expert is good at all aspects of a task. However, little attention has been given to the case where demonstrations are collected from multiple experts, whose expertise may vary on different aspects of the task. In such scenarios, it is likely that the demonstrations will contain conflicting advice in many parts of the state space. In this paper, we propose a two-level Q-learning algorithm, in which the RL agent not only learns the policy of deciding on the optimal action but also learns to select the most trustworthy expert according to the current state. Thus, our approach removes the traditional assumption that demonstrations come from one single source and are mostly conflict-free. We evaluate our technique on two different domains and the results show that the state-of-the-art RLfD baseline fails to converge or performs similarly to conventional Q-learning. In contrast, the performance level of our novel algorithm increases with more experts being involved in the learning process and the proposed approach has the capability to handle demonstration conflicts well.
Influence of a uniform current on collective magnetization dynamics in a ferromagnetic metal

We discuss the influence of a uniform current j on the magnetization dynamics of a ferromagnetic metal. We find that the magnon energye(q ) has a current-induced contribution proportional to qiJ W , where J W is the spin current, and predict that collective dynamics will be more strongly damped at finite j. We obtain similar results for models with and without local moment participation in the magnetic order. For transition metal ferromagnets, we estimate that the uniform magnetic state will be destabilized for j*10 9 Ac m 22 . We discuss the relationship of this effect to the spin-torque effects that alter magnetization dynamics in inhomogeneous magnetic systems.
Democratic Innovations in Political Systems

Magnus Adenskog (2018): Democratic Innovations in Political Systems Towards a Systemic Approach. Örebro Studies in Political Science 42. There are many indicators that the representative democratic system is currently facing serious legitimacy challenges. Two central indicators of these challenges are changing patterns of political participation and a decline in system support. Against this backdrop, a growing number of governments claim that democratic innovations (DIs) could reconnect institutions with citizens. This thesis focuses on online DIs implemented in real political contexts, and the overarching aim of the thesis is to contribute to the emergent empirical scholarship on how DIs can influence political systems. In the last two decades, most empirical studies in the field have analysed DIs at the micro level. This thesis takes a different stance by posing system-related research questions to the implementation of DIs and, by doing so, showing how DIs are interrelated with, rather than isolated from, the political system and that DIs can influence political systems. The thesis consists of one literature review and three empirical case studies, applying a multiple methodological approach. Its novelty lies in three main empirical findings that contribute to the development of the field. First, it shows that participation in DIs can influence citizens’ perceived trust towards local political institutions. In addition, the results suggest that predispositions and prior engagement mediate the direction of change in trust amongst citizens. Secondly, the results show that DIs can perform different political functions, such as facilitating spaces for citizens to provide original ideas and deliberation, while also having an agenda-setting function. Thirdly, the results suggest that long-term institutional change is complex and that the implementation of DIs can create a situation in which civil servants and politicians perceive their organisation to be in some ‘state of flux’, as they are torn between two competing institutional logics. In conclusion, this thesis should be understood as a piece in a broader movement that works towards a systemic approach to the study of DIs, and that by showing these empirical findings, the thesis contributes to deepening our understanding of what influences and functions DIs can have in political systems.
Implementation of Protection Against Unauthorized Access using SubSpace Outliers Ranking

The two knowledge-based approaches are not sufficient to tackle the anomaly detection problem, and that a holistic solution should also include knowledge-independent analysis techniques. There are some algorithms, and it becomes critical in the case of unsupervised detection, because there is no additional information to select the most relevant set some approaches can be easily extended to detect other types of attacks, considering different sets of traffic features. In fact, more features can be added to any standard list to improve detection and characterization results. The Knowledge of Independent Detection of Network Attack is simply to detect the attacks which are completely unknown to us. There is no previous knowledge about that data. There are some algorithms in existence which are used for network security but they are inefficient as they are knowledge based (Signature Based and Anomaly Based) whenever there is a vast amount of continuous incoming data then it is a big risk regarding the network attacks which are knowledge based. Our particular goal is to identify those attacks with the help of Robust Clustering Algorithm and make whole data secure.
Gastrointestinal Stromal Tumor of the Rectum : A Case Report

mal tumors” in 1983, but it was not widely adopted until the early 1990s when Miettinen et al discovered that most stromal tumors arising in the GI tract are CD34 positive, the first relatively specific marker for gastrointestinal stromal tumors (GISTs). Kindblom et al. revealed that the actual cells of origin of GISTs are pluripotential mesenchymal stem cells programmed to differentiate into the interstitial cells of Cajal. These are GI pacemaker cells and are largely responsible for the initiation and coordination of GI motility. Perhaps the most critical development that distinguishing GISTs as unique clinical entities is the discovery of c-kit proto-oncogene mutations in the tumors. GISTs are most common in the stomach and small intestine, but less common in colon and rectum. Traditionally, surgery is the cornerstone of treatment for resectable GISTs. In primary diseases, complete surgical resection provides the chance of cure. The standard of care in the management of patients with GISTs rapidly changed after the introduction of tyrosine kinase inhibitors (TKIs), such as imatinib mesylate and sunitinib malate. In the post-imatinib era, surgery shares a role in a multidisciplinary treatment plan.
A new energy harvester using a piezoelectric and suspension electromagnetic mechanism

This study presents a new design of a piezoelectric-electromagnetic energy harvester to enlarge the frequency bandwidth and obtain a larger energy output. This harvester consists of a primary piezoelectric energy harvesting device, in which a suspension electromagnetic component is added. A coupling mathematical model of the two independent energy harvesting techniques was established. Numerical results show that the piezoelectric-electromagnetic energy harvester has three times the bandwidth and higher power output in comparison with the corresponding stand-alone, single harvesting mode devices. The finite element models of the piezoelectric and electromagnetic systems were developed, respectively. A finite element analysis was performed. Experiments were carried out to verify the validity of the numerical simulation and the finite element results. It shows that the power output and the peak frequency obtained from the numerical analysis and the finite element simulation are in good agreement with the experimental results. This study provides a promising method to broaden the frequency bandwidth and increase the energy harvesting power output for energy harvesters.