Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Model of Inventory Investment by Durable Commodity

This paper introduces a new detailed data set of high-frequency observations on inventory investment by a U.S. steel wholesaler. Our analysis of these data leads to six main conclusions: orders and sales are made infrequently; orders are more volatile than sales; order sizes vary considerably; there is substantial high-frequency variation in the rm's sales prices; inventory/sales ratios are unstable; and there are occasional stockouts. We model the rm generically as a durable commodity intermediary that engages in commodity price speculation. We demonstrate that the rm's inventory investment behavior at the product level is well approximated by an optimal trading strategy from the solution to a nonlinear dynamic programming problem with two continuous state variables and one continuous control variable that is subject to frequently binding inequality constraints. We show that the optimal trading strategy is a generalized (S; s) rule. That is, whenever the rm's inventory level q falls below the order threshold s(p) the rm places an order of size S(p) q in order to attain a target inventory level S(p) satisfying S(p) s(p), where p is the current spot price at which the rm can purchase unlimited amounts of the commodity after incurring a xed order cost K. We show that the (S; s) bands are decreasing functions of p, capturing the basic intuition of commodity price speculation, namely, that it is optimal for the rm to hold higher inventories when the spot price is low than when it is high in order to pro t from \buying low and selling high. " We simulate a calibrated version of this model and show that the simulated data exhibit the key features of inventory investment we observe in the data.
Just Starting Out: Learning and Equilibrium in a New Market

We document the evolution of the newly created market for frequency response within the UK electricity system over a six-year period. Firms competed in price while facing considerable initial uncertainty about market demand and rival behavior. We show that over time prices stabilized, converging to a rest point that is consistent with equilibrium play, and then adjusted to subsequent changes in the market quite quickly. We draw on models of fictitious play and adaptive learning to analyze how this convergence occurs and show that these models predict behavior better than Nash equilibrium prior to convergence.
Bayesian Nonparametrics

This book consists of a collection of articles concerning Bayesian nonparametric methods. The recent theoretical and practical aspects of Dirichlet processes are discussed. The book covers the theoretical aspects in the first four chapters, while the last four chapters document a wide range of applications from biostatistics to signal processing. The theory of Bayesian nonparametrics presents flexible models, whose complexity increases with increasing amount of data and dimensionality. The theorems of ‘stick-breaking’ representation, Chinese restaurant process, Indian buffet process, and Bernstein–von Mises have been covered repeatedly throughout the first four chapters. On the practical aspects, the Bayesian nonparametric techniques (such as the hierarchical Dirichlet process) have been widely applied to the areas of information retrieval, speaker diarisation, word segmentation, bioinformatics, and microarrays. The contribution of this book is to collect most recent research of Bayesian nonparametric techniques together, with main emphasis on the use of Dirichlet process. The popularity of Dirichlet process is because that the Dirichlet prior is nonparametric and conjugate, thus presenting many opportunities to flexibly model complex data structure. The book incorporates the Bayesian philiosophy into the nonparametric concept. It also introduces Bayesian computational tools (such as the Metropolis-Hasting and Gibbs algorithms) to deal with possibly infinite number of parameters. A statistical software package in R called DRpackage has been introduced to implement Dirichlet process mixture density estimation, Pólya tree priors for density estimation, and nonparametric random effects models including generalized linear models. The authors have fulfilled their main aim which is to introduce Bayesian nonparametrics. However, I feel this book could go even further by combining the Bayesian computational techniques with frequentist nonparametric methods. In other words, could the Bayesian techniques be well suited for solving frequentist nonparametric problems? If so, this would bring the Bayesian philosophy and frequentist philiosophy one step closer. Overall, I enjoyed reading and reviewing this book and I feel that this book gives nice theoretical and practical viewpoints on Bayesian nonparametrics. The use of Bayesian nonparametric techniques is suitable for any statistician, who aims to analyse complex data structures with possibly infinite number of parameters. Although this book concentrates mainly on the Dirichlet process, it serves perfectly as a reference for scientists and graduate students working in the field of Bayesian data analysis.
Vocal Cord Dysfunction: Etiologies and Treatment

Vocal cord dysfunction, a syndrome of paradoxical inspiratory closure of the vocal cords, is becoming more frequently recognized and diagnosed recently since its initial modern description 30 years ago. Initially described as single case reports, the first case series in 1983 helped to clarify the typical patient and findings of vocal cord dysfunction. Recent investigations have elucidated specific etiologies such as gastroesophageal reflux, exercise, and irritants as causative factors in addition to the numerous associated psychologic factors. Speech therapy and psychotherapy have been used extensively with vocal cord dysfunction patients, but the optimal treatment has yet to be prospectively studied. This manuscript provides a comprehensive review of the reported causative factors and treatments for vocal cord dysfunction.
Digenean species diversity in teleost fishes from the Gulf of Gabes, Tunisia (Western Mediterranean)

This study is the first attempt to survey the diversity of fish digeneans in the Gulf of Gabes (southern coast of Tunisia). A total of 779 fishes belonging to 32 species were sampled. 53 species of Digenea belonging to 15 families were recorded. Among these species, 24 are reported for the first time from the coast of Tunisia. We report one new host record, Lecithochirium sp. from Sardinella aurita. The Hemiuridae is the dominant family. A host-parasite list is presented with the information on the prevalence, abundance and mean intensity of each species collected. The diversity of Digenea is compared with other localities in the Mediterranean Sea and the northern east of Tunisia. The Gulf of Gabes shows the lowest diversity linked to the anthropogenic activities and impact of exotic species. The use of Digenea as indicators of the state of the ecosystem is discussed.
Site-Directed Mutagenesis of Conserved Amino Acids Residues in N 5-Carboxyaminoimidazole Ribonucleotide Mutase : converting N 5-CAIR mutase into Aminoimidazole Ribonnucleotide carboxylase & Developing a High-Throughput Screening Assay for the Discovery of N 5-Carboxyaminoimidazole Ribonucleotide Mut

Site-Directed Mutagenesis of Conserved Amino Acids Residues in N Carboxyaminoimidazole Ribonucleotide Mutase & Developing a High-Throughput Screen Assay for the D iscovery of N Carboxyaminoimidazole Ribonucleotide Mutase Inhibit ors by ZHIWEN SHI July 2014 Advisor : Dr. Steven M. Firestine Major : Pharmaceutical Sciences Degree : Master of Science The de novo purine biosynthesis pathway plays a critical role in providing new purines to the cell. Previous studies have shown differences between the human and bacterial pathways which suggests that pathway may be a potential target for antibiotic drug discovery. Three critical enzymes are involved in the pathway divergence. In the bacterial pathway, N-CAIR synthetase (PurK) first converts AIR to N-CAIR, which is then transformed into CAIR catalyzed by the enzyme N-CAIR mutase (Class I PurE). In the human pathway, AIR carboxylase (Class II PurE) catalyzes the direct conversion of AIR to CAIR. Class I and Class II PurEs have structure and sequence similarities but also are functionally different. However, the residues responsible for these differences are unknown. In this study, we hypnotized that the class-specific conserved residues in the active site might be the key to
Original research: Impact of globalization and trade openness on economic growth in Bangladesh

We investigate the impact of globalization and openness in trade on the economic growth of Bangladesh by econometric testing using an autoregressive distributed lag (ARDL) model and the Granger causality test. The key variables used in the study are in log transformation i.e. the log of GDP, log of exports and log of imports. In order to examine the existence of a causal relationship (both long run and short run) between economic growth and globalization in terms of trade openness measured by exports and imports of Bangladesh, the Granger causality was used with vector autoregression (VAR) methodology along with two different approaches for testing of cointegration: Johansen cointegration and autoregressive distributed lag (ARDL). The results of the error correction model (ECM) and Granger causality test suggest a causal relationship between GDP and exports and imports. The exports show bidirectional Granger causality with economic growth, but imports do not Granger cause GDP and exports. This indicates a positive impact for globalization over trade and economic growth in the least developed countries (LDCs) such as Bangladesh. This vital connection may become a footstep for those LDCs who are reluctant to open up their trade barriers. The empirical findings suggest bidirectional causality between exports and GDP, with impacts of globalization in terms of exports affecting economic growth (GDP). Accordingly, more export oriented policies may be recommended with strong checks on imports because expansion of exports may generate more foreign exchange for payment of import bills as well as enhance capital accumulation.
Mobility and income inequality in the European Union and in Portugal

This article aims at establishing some facts on mobility and income distribution in the European Union countries, with a special focus on the Portuguese case. The analysis was developed with the latest information from the EU-SILC database, for the period 2005-2009. There is substantial mobility between the various income deciles in the EU and, to a lesser extent, in Portugal. Income mobility decreases the degree of inequality in non-negligible terms, but the fraction of inequality that takes a permanent nature remains quite high in all EU countries and in particular in Portugal. Additionally, there is no relation between the level of inequality and the contribution of income mobility to the reduction in inequality in the EU countries. In the recent past, income growth in the EU countries, including Portugal, was skewed towards lower income individuals. However, the contribution of this “progressive” growth to the reduction of inequality was mitigated, or even canceled, by the re-ranking of individuals in the income distribution.
Visual Representations by Cortical Somatostatin Inhibitory Neurons—Selective But with Weak and Delayed Responses

Somatostatin-expressing inhibitory (SOM) neurons in the sensory cortex consist mostly of Martinotti cells, which project ascending axons to layer 1. Due to their sparse distribution, the representational properties of these neurons remain largely unknown. By two-photon imaging guided cell-attached recordings, we characterized visual response and receptive field (RF) properties of SOM neurons and parvalbumin-expressing inhibitory (PV) neurons genetically labeled in the mouse primary visual cortex. In contrast to PV neurons, SOM neurons exhibit broader spikes, lower spontaneous firing rates, smaller On/Off subfields, and broader ranges of basic RF properties such as On/Off segregation, orientation and direction tunings. Notably, the level of orientation and direction selectivity is comparable to that of excitatory neurons, from weakly-tuned to highly selective, whereas PV neurons are in general unselective. Strikingly, the evoked spiking responses of SOM cells are ∼3- to 5-fold weaker and 20–25 ms delayed compared with those of PV neurons. The onset latency of the latter is consistent with that of inhibitory input to excitatory neurons. These functional differences between SOM and PV neurons exist in both layer 2/3 and 4. Our results suggest that SOM and PV neurons engage in cortical circuits in different manners: while PV neurons provide fast, strong but untuned feedforward inhibition to excitatory neurons, likely serving as a general gain control for the processing of ascending inputs, SOM neurons with their selective but delayed and weak inhibition may provide more specific gating of later arriving intracortical excitatory inputs on the distal dendrites.
Adapting palettes to color vision deficiencies by genetic algorithm

In choosing a color palette, it is necessary to take into the account the needs of color vision impaired users, in order to make information and services accessible to a broader audience. This means to search the space of color palettes aimed to find a color combination representing a good trade-off between aesthetics and accessibility requirements. In this paper, we present a solution based on genetic algorithms. Experimental results highlight this approach to be an efficient but effective way to assist UI designers by suggesting appropriate variations of color palettes.
Codes , Correlations and Power Control in OFDMJames

Practical communications engineering is continuously producing problems of interest to the coding theory community. A recent example is the power-control problem in Orthogonal Frequency Division Multiplexing (OFDM). We report recent work which gives a mathematical framework for generating solutions to this notorious problem that are suited to low-cost wireless applications. The key result is a connection between Golay complementary sequences and Reed-Muller codes. The former are almost ideal for OFDM transmissions because they have a very low peak-to-mean envelope power ratio (PMEPR), while the latter have eecient encoding and decoding algorithms and good error correction capability. This result is then generalised in two ways. Firstly we study polyphase Golay sequences, motivating the introduction of non-binary generalisations of the Reed-Muller codes. Secondly we consider Golay complementary sets, where the results can be presented most naturally in the language of graph theory. The practical impact is a exible family of OFDM codes which combine low PMEPR with good error correction capability. However, the interaction between theory and practice is a two-way process: the application motivates further study of a fertile interplay between coding theory, graph theory and sequence design. We include a list of open problems which we hope will stimulate further research in this area.
The Puzzle of the Swiss Interest Rate Island: Stylized Facts and a New Interpretation

This paper contributes to the debate about the puzzle of the Swiss Interest Rate Island. It starts out by establishing some stylized facts about the nature of the puzzle. First it shows that long run real returns on Swiss Euro Deposits have been significantly lower than in any other major currency. A decomposition of return differentials into deviations from uncovered interest rate parity and deviations from purchasing power parity reveals that the former contributes most to the puzzle. Two implications follow from these stylized facts: (i) since the puzzle is present in Euro Deposit rates it cannot be due to local factors such as banking secrecy, and (ii) solutions to the puzzle have to provide an explanation for a long run failure of uncovered interest rate parity rather than for real appreciation. Historical evidence is presented that suggests the puzzle may be attributed to a reverse peso problem. The paper discusses the consequences of this interpretation for Swiss monetary and exchange rate policy.
Foreign Direct Investment-Led Growth: Evidence from Time Series and Panel Data

This paper develops an endogenous growth model with foreign direct investment (FDI) and examines the impact of the latter on capital accumulation, output and total factor productivity (TFP) growth in the recipient economy. Time series and panel data evidence are provided for a sample of OECD and non OECD countries in teh 1970-90 period. Although FDI is expected to boost long-run growth in the recipient economy via technological transfers and knowlege spillovers, it is shown that the extent to which FDI is growth-enhancing depends on the degreee of complementarity and substitution between FDI and domestic investment. Also, FDI is shown to be more growth-enhancing in technological followers than leaders and sensitive to unobservable country-specific effects.
Managing Performance Interference in Cloud-Based Web Services

Web services have increasingly begun to rely on public cloud platforms. The virtualization technologies employed by public clouds can, however, trigger contention between virtual machines (VMs) for shared physical machine resources, thereby leading to performance problems for Web services. Past studies have exploited physical-machine-level performance metrics such as clock cycles per instruction to detect such platform-induced performance interference. Unfortunately, public cloud customers do not have access to such metrics. They can only typically access VM-level metrics and application-level metrics such as transaction response times, and such metrics alone are often not useful for detecting inter-VM contention. This poses a difficult challenge to Web service operators for detecting and mitigating platform-induced performance interference issues inside the cloud. We propose a machine-learning-based interference detection technique to address this problem. The technique applies collaborative filtering to predict whether a given transaction being processed by a Web service is adversely suffering from interference. The results can be then used by a management controller to trigger remedial actions, e.g., reporting problems to the system manager or switching cloud providers. Results using a realistic Web benchmark show that the approach is effective. The most effective variant of our approach is able to detect about 96% of performance interference events with almost no false alarms. Furthermore, we show that a load redistribution technique that exploits the information from our detection technique is able to more effectively mitigate the interference than techniques that are interference agnostic.
Real Effects of Sovereign Bond Market Spillovers in the Euro Area

This paper develops a small open economy model to investigate the impact of rising sovereign bond market spreads on the real economy. One key element of the model is a "sovereign risk channel" through which tensions in the sovereign bond market tend to spill over into private credit markets. The model is estimated with Bayesian methods and data for "high-spread" countries in the euro area. It turns out that spread shocks during the Euro crisis had a negative effect on real GDP growth in these countries, up to 0.8 percentage points (PP) Portugal and Ireland, 0.3 PP in Italy and 0.2 PP in Spain.
Efficient massively parallel transport sweeps

The full-domain “sweep,” in which all angular fluxes in a problem are calculated given previous-iterate values only for the volumetric source, forms the foundation for many iterative methods that have desirable properties [1]. One important property is that iteration counts do not grow with mesh refinement [1]. The sweep solution on parallel machines is complicated by the dependency of a given cell on its upstream neighbors. A simple task dependence graph (TDG) for a single quadrature direction in a 2D example (Fig.1) illustrates the issue: tasks at a given level of the graph cannot be executed until some tasks finish on the previous level. The KBA algorithm [2] partitions the problem by assigning a column of cells to each processor, indicated by the four diagonal task groupings in Fig.1. KBA parallelizes over planes perpendicular to the sweep direction—over the breadth of the TDG. Early and late in a single-direction sweep, some processors are idle, as in stages 1-3 and 9-11 in Fig.1. In this example, parallel efficiency for an isolated singledirection sweep could be no better than 8/11 = 0.73. KBA is much better, because when a processor finishes its tasks for the first direction it begins its tasks for the next direction in the octant-pair with the same sweep ordering. That is, each processor begins a new TDG as soon as it completes its work on the previous TDG, until all directions in the octant-pair finish. This effectively lengthens the “pipe” and increases efficiency. If there were 2M directions in the octant pair, then the pipe length is 2M × 8 in this example, and the efficiency could be up to (2M × 8)/(3 + 2M × 8). KBA’s pipe-fill penalty grows as processor count grows, even if cell count grows proportionally. The width of the TDG grows only as P , so traditional KBA eventually runs out of parallelism to exploit. These issues fuel the common belief that sweeps cannot perform well in parallel beyond a few thousand processing elements. One purpose of this summary is to help dispel this belief. A sweep algorithm is defined by its partitioning (dividing the domain among processors), aggregation (grouping cells, directions, and energy groups into “tasks”), and scheduling (choosing which task to execute if more than one is available). The work presented here follows that
Reactive Astrocytes Form Scar-Like Perivascular Barriers to Leukocytes during Adaptive Immune Inflammation of the CNS

Factors that regulate leukocyte entry and spread through CNS parenchyma during different types of CNS insults are incompletely understood. Reactive astrocytes have been implicated in restricting the spread of leukocytes from damaged into healthy parenchyma during the acute and local innate inflammatory events that follow CNS trauma, but the roles of reactive astrocytes during the chronic and widespread CNS inflammation associated with adaptive or acquired immune responses are uncertain. Here, we investigated the effects of transgenically targeted ablation of proliferating, scar-forming reactive astrocytes on the acquired immune inflammation associated with experimental autoimmune encephalitis (EAE). In wild-type mice with EAE, we found that reactive astrocytes densely surrounded perivascular clusters of leukocytes in a manner reminiscent of astrocyte scar formation after CNS trauma. Transgenically targeted ablation of proliferating astrocytes disrupted formation of these perivascular scars and was associated with a pronounced and significant increase in leukocyte entry into CNS parenchyma, including immunohistochemically identified macrophages, T lymphocytes and neutrophils. This exacerbated inflammation was associated with a substantially more severe and rapidly fulminant clinical course. These findings provide experimental evidence that reactive astrocytes form scar-like perivascular barriers that restrict the influx of leukocytes into CNS parenchyma and protect CNS function during peripherally initiated, acquired immune inflammatory responses in the CNS. The findings suggest that loss or disruption of astrocyte functions may underlie or exacerbate the inflammation and pathologies associated with autoimmune diseases of the CNS, including multiple sclerosis.