Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian Sequential Experimental Study of Learning in Games

Abstract We apply a sequential Bayesian sampling procedure to study two models of learning in repeated games. In the first model individuals learn only about an opponent when they play her or him repeatedly but do not update from their experience with that opponent when they move on to play the same game with other opponents. We label this the nonsequential model. In the second model individuals use Bayesian updating to learn about population parameters from each of their opponents, as well as learning about the idiosyncrasies of that particular opponent. We call this the sequential model. We sequentially sample observations on the behavior of experimental subjects in the so-called “centipede game. ” This game allows for a trade-off between competition and cooperation, which is of interest in many economic situations. At each point in time, the “state” of our dynamic problem consists of our beliefs about the two models and beliefs about the nuisance parameters of the two models. Our “choice” set is to samp.
Effects of brain temperature on CBF thresholds for extracellular glutamate release and reuptake in the striatum in a rat model of graded global ischemia

WE simultaneously measured extracellular glutamate ([Glu]e) elevation and local CBF using a real-time monitoring method and laser-Doppler flowmetry, respectively, in the rat striatum in a modified graded global ischemia model. Ischemic brain temperatures were kept at 32°C, 37°C and 39°C. Three distinct types of intraischemic [Glu]e elevation, reflecting mild, moderate and massive glutamate release, were observed. Brain temperature plays an important role in determining CBF thresholds for each of the three types of [Glu]e elevation. CBF thresholds for [Glu]e elevations shifted to a lower level range as brain temperature was reduced. In mild or moderate ischemia, there is no exposure to sustained [Glu]e elevation, which is seen only in relatively severe ischemia characterized by biphasic [Glu]e elevation.
Molecular and Immunohistochemical Characterization of Historical Long-Term Preserved Fixed Tissues from Different Human Organs

University and museum collections are very important sources of biological samples that can be used to asses the past and present genetic diversity of many species. Modern genetic and immunohistochemical techniques can be used on long-term preserved fixed tissues from museum specimens to answer epidemiological questions. A proof of principle was established to apply modern molecular genetics and immunohistochemical methods to these old specimens and to verify the original diagnosis. We analysed 19 specimens from our university collection including human organs that had been in fixative for more than 80 years. The tissues originated from lung, colon, brain, heart, adrenal gland, uterus and skin. We isolated amplifiable DNA from these wet preparations and performed mutational analysis of BRAF, KRAS and EGFR. The tissues were also embedded in paraffin and used for modern histology and immunohistochemistry. Our data show that amplifiable DNA is extractable and ranged from 0.25 to 22.77 μg of total DNA. In three specimens BRAFV600E or KRASG12D mutations were found. Additionally, expression of different proteins like vimentin and GFAP was detected immunohistochemical in six investigated specimens. On the basis of our results the original diagnosis was altered in three specimens. Our work showed that it is possible to extract amplifiable DNA suitable for sequence analysis from long-term fixed tissue. Furthermore, histology and immunohistochemistry is feasible in specimens fixed long time ago. We conclude that these old preparations are suitable for further epidemiological research and that our methods open up new opportunities for future studies.
Fluorescent-stainable antibodies to the Eaton agent in human primary atypical pneumonia transmission studies.

The etiology of those cases of nonbacterial pneumonia that cannot be associated with the viruses of psittacosis and influenza, adenoviruses, or other well delineated agents, and in which cold hemagglutinins or streptococcus MG agglutinins often develop, has been a controversial subject. The possible role in this disease of an agent isolated by Eaton, Meiklejohn, van Herick and Talbot in 1941 from patients with primary atypical pneumonia (1) has been difficult to evaluate because of the agent's limited host range and technical problems in performing specific serologic tests with it. Eaton's agent can be propagated in embryonated hens' eggs, as demonstrated by production of pneumonia on passage to cotton rats (2), but produces no apparent pathologic change in the chick embryo (2, 3). Using a modification of the fluorescent antibody technique of Coons and Kaplan (4), Liu, Eaton and Heyl have provided both a means of localizing the antigens in the chick embryo and a method by which sera can le examined for fluorescent-stainable antibodies to Eaton's agent (3, 5). Additional isolations of Eaton's agent from patients and demonstrations of fluorescent-stainable antibodies to it have been reported by Cook (6) and Chanock (7) and their associates, confirming and extending the observations of Eaton and Liu. The specificity of this fluorescent antibody reaction in terms of the etiology of the disease has been questioned (8), however, since patients with primary atypical pneumonia may develop a number of nonspecific
NET Institute* www.NETinst.org

As a common practice, various ﬁrms initially make information and access to their products/services scarce within a social network; identifying inﬂuential players that facilitate information dissemination emerges as a pivotal step for their success. In this paper, we tackle this problem using a stylized model that features payoﬀ externalities and local network eﬀects, and the network designer is allowed to release information to only a subset of players (leaders); these targeted players make their contributions ﬁrst and the rest followers move subsequently after observing the leaders’ decisions. In the presence of incomplete information, the signaling incentive drives the optimal selection of leaders and can lead to a ﬁrst-order materialistic eﬀect on the equilibrium outcomes. We propose a novel index for the key leader selection (i.e., a single player to provide information to) that can be substantially diﬀerent from the key player index in Ballester et al. (2006) and the key leader index with complete information proposed in Zhou and Chen (2013). We also show that in undirected graphs, the optimal leader group identiﬁed in Zhou and Chen (2013) is exactly the optimal follower group when signaling is present. The pecking order in complete graphs suggests that the leader
Artificial Stereo Extension of Speech Based on Inter-Channel Coherence

In this paper, an artificial stereo extension method of speech is proposed to control the width of auditory image. The proposed stereo extension method employs a decorrelator to make stereo-channel signals without any additional information from mono speech signal. In other words, uncorrelated signal is first generated from mono speech signal passing though the decorrelator, and it is added to or subtracted from the mono signal to obtain the left or right channel signal, respectively. Here, the degree of inter-channel coherence is controlled to vary the width of auditory image. The performance of the proposed stereo extension method is evaluated using a MUSHRA test and compared with that of a parametric stereo method. It is shown from the test that the mean opinion score of the signals extended by the proposed stereo extension method is 92, which is around 5% higher than that of the parametric stereo method.
Modeling the Impact of Disk Scrubbing on Storage System

One of characteristics of the rising cloud storage technology is low-cost and high reliability. A distinct benefit of disk scanning or scrubbing operation is identifying the potential failure sectors as early as possible, thus providing high reliability. Obviously, the higher the scrubbing frequency is, the higher the system reliability is. However, it may take a few hours for a scanning process to check the whole disk. In other words, the scrubbing process may result in a downtime or a lower system performance. Furthermore, the scrubbing process consumes energy. In order to reduce the impact of disk scrubbing on disk performance and energy consumption, system designers choose to scan the disk in a low frequency, which results in a lower reliability. Thus it is essential to design a good scrubbing scheme in a large scale storage system over long time horizons. In this paper, we present a novel scrubbing scheme to solve the challenge. In this scheme, an optimum scrubbing cycle is decided by keeping a balance between data loss cost, scrubbing cost, and disk failure rate. Our research shows how the data price and the scrubbing cost affect scrubbing frequency, and the scrubbing scheme is applicable for storage with inexpensive data. Our experiment shows that our scheme outperforms routine method 73.3% in cost and 40% in reliability.
Fast Electron Thermometry for Ultrasensitive Calorimetric Detection

We demonstrate radio-frequency thermometry on a micrometer-sized metallic island below 100 mK. Our device is based on a normal-metal-insulator-superconductor tunnel junction coupled to a resonator with transmission readout. In the first generation of the device, we achieve 90 mu K/root Hz noise-equivalent temperature with 10 MHz bandwidth. We measure the thermal relaxation time of the electron gas in the island, which we find to be of the order of 100 mu s. Such a calorimetric detector, upon optimization, can be seamlessly integrated into superconducting circuits, with immediate applications in quantum-thermodynamics experiments down to single quanta of energy.
A Varroa destructor protein atlas reveals molecular underpinnings of developmental transitions and sexual differentiation*

Varroa destructor is the most economically damaging honey bee pest, weakening colonies by simultaneously parasitizing bees and transmitting harmful viruses. Despite these impacts on honey bee health, surprisingly little is known about its fundamental molecular biology. Here, we present a Varroa protein atlas crossing all major developmental stages (egg, protonymph, deutonymph, and adult) for both male and female mites as a web-based interactive tool (http://foster.nce.ubc.ca/varroa/index.html). We used intensity-based label-free quantitation to find 1,433 differentially expressed proteins across developmental stages. Enzymes for processing carbohydrates and amino acids were among many of these differences as well as proteins involved in cuticle formation. Lipid transport involving vitellogenin was the most significantly enriched biological process in the foundress (reproductive female) and young mites. In addition, we found that 101 proteins were sexually regulated and functional enrichment analysis suggests that chromatin remodeling may be a key feature of sex determination. In a proteogenomic effort, we identified 519 protein-coding regions, 301 of which were supported by two or more peptides and 169 of which were differentially expressed. Overall, this work provides a first-of-its-kind interrogation of the patterns of protein expression that govern the Varroa life cycle and the tools we have developed will support further research on this threatening honey bee pest.
Enhancing forestry supply chain through innovative integration of digital tools and techniques

Internationally forestry and wood products supply chains are being transformed. This traditionally resource intensive low technology sector is moving rapidly towards precision-manufacturing, advanced technology use and innovation in ways that increase the value and sustainability of wood within the emerging bio-economy. This research-in-progress reports on a major Australian Research Council funded applied industry based program integrating different digital tools and techniques along the forestry supply chain to improve product traceability, characterisation and knowledge management to stimulate and support market innovation. The paper provides insight into contemporary forest supply chains and illustrates the breadth of research being undertaken in this program. A specific example involving the innovative use of unmanned aerial vehicles (UAV) and digital telemetry to advance the speed and accuracy of forestry resource assessments is described. Preliminary results comparing manual and automated techniques for inventory estimation appear positive. Planned next steps for this project and the program are presented.
A Strongly Polynomial Algorithm to Solve Combinatorial Linear Programs

Khachiyan, and recently Karmarkar, gave polynomial algorithms to solve the linear programming problem. These algorithms have a small theoretical drawback; namely, the number of arithmetic steps depends on the size of the input numbers. We present a polynomial linear programming algorithm whose number of arithmetic steps depends only on the size of the numbers in the constraint matrix, but is independent of the size of the numbers in the right-hand side and objective vectors. In particular, it gives a polynomial algorithm for the minimum cost flow and multicommodity flow problems in which the number of arithmetic steps is independent of the size of the costs and capacities. The algorithm makes use of an existing polynomial linear programming algorithm. The problem of whether any algorithm has a running time that is independent even of the size of the numbers in the constraint matrix remains open.
Measuring Platelet Aggregation in Dialysis Patients With a Whole Blood Aggregometer by the Screen Filtration Pressure Method

A semi‐automatic, whole‐blood aggregometer based on the screen filtration pressure (SFP) method is now widely used clinically and in research, but has not been used with hemodialysis (HD) patients. We measured whole‐blood platelet aggregation in HD patients by the SFP method. This retrospective cross‐sectional study included 62 HD patients, of whom 47 were non‐diabetic and 15 were diabetic; we also included a control group of healthy, non‐uremic subjects. With the t‐test, we examined differences in the platelet aggregation threshold index (PATI) in meaningful sub‐groupings of the HD patients, depending on whether or not they had diabetes, and whether or not they had been given antiplatelet agents. Considering the non‐diabetic HD patients first, their PATI values were significantly higher than those values in the control subjects (3.1 [1.0–5.2] vs. 1.8 [1.3–2.3] µM, P < 0.001). The non‐diabetic HD patients taking antiplatelet agents showed significantly (1.9 times) higher PATI values than the non‐diabetic HD patients without antiplatelet agents (4.4 [1.8–7.0] vs. 2.3 [1.3–3.3] µM, P = 0.003). We observed similar trends among diabetic HD patients. Whole‐blood analysis by the SPF method seems to be a promising way of monitoring platelet function for HD patients.
Mutagenesis of ARS2 Domains To Assess Possible Roles in Cell Cycle Progression and MicroRNA and Replication-Dependent Histone mRNA Biogenesis

ABSTRACT ARS2 is a regulator of RNA polymerase II transcript processing through its role in the maturation of distinct nuclear cap-binding complex (CBC)-controlled RNA families. In this study, we examined ARS2 domain function in transcript processing. Structural modeling based on the plant ARS2 orthologue, SERRATE, revealed 2 previously uncharacterized domains in mammalian ARS2: an N-terminal domain of unknown function (DUF3546), which is also present in SERRATE, and an RNA recognition motif (RRM) that is present in metazoan ARS2 but not in plants. Both the DUF3546 and zinc finger domain (ZnF) were required for association with microRNA and replication-dependent histone mRNA. Mutations in the ZnF disrupted interaction with FLASH, a key component in histone pre-mRNA processing. Mutations targeting the Mid domain implicated it in DROSHA interaction and microRNA biogenesis. The unstructured C terminus was required for interaction with the CBC protein CBP20, while the RRM was required for cell cycle progression and for binding to FLASH. Together, our results support a bridging model in which ARS2 plays a central role in RNA recognition and processing through multiple protein and RNA interactions.
Machine learning and sentiment analysis approaches for the analysis of Parliamentary debates

In this thesis the author seeks to establish the most appropriate mechanism for conducting sentiment analysis with respect to political debates; firstly so as to predict their outcome and secondly to support a mechanism to provide for the visualisation of such debates in the context of further analysis. To this end two alternative approaches are considered, a classification-based approach and a lexicon-based approach. In the context of the second approach both generic and domain specific sentiment lexicons are considered. Two techniques to generating domain-specific sentiment lexicons are also proposed: (i) direct generation and (ii) adaptation. The first was founded on the idea of generating a dedicated lexicon directly from labelled source data. The second approach was founded on the idea of using an existing general purpose lexicon and adapting this so that it becomes a specialised lexicon with respect to some domain. The operation of both the generic and domain specific sentiment lexicons are compared with the classification-based approach. The comparison between the potential sentiment mining approaches was conducted by predicting the attitude of individual debaters (speakers) in political debates (using a corpus of labelled political speeches extracted from political debate transcripts taken from the proceedings of the UK House of Commons). The reported comparison indicates that the attitude of speakers can be effectively predicted using sentiment mining. The author then goes on to propose a framework, the Debate Graph Extraction (DGE) framework, for extracting debate graphs from transcripts of political debates. The idea is to represent the structure of a debate as a graph with speakers as nodes and “exchanges” as links. Links between nodes were established according to the exchanges between the speeches. Nodes were labelled according to the “attitude” (sentiment) of the speakers, “positive” or “negative”, using one of the three proposed sentiment mining approaches. The attitude of the speakers was then used to label the graph links as being either “supporting” or “opposing”. If both speakers had the same attitude (both “positive” or both “negative”) the link was labelled as being “supporting”; otherwise the link was labelled as being “opposing”. The resulting graphs capture the abstract representation of a debate where two opposing factions exchange arguments on related content. Finally, the author moves to discuss mechanisms whereby debate graphs can be structurally analysed using network mathematics and community detection techniques. To this end the debate graphs were conceptualised as networks in order to conduct appropriate network analysis. The significance was that the network mathematics and community detection processes can draw conclusions about the general properties of debates in parliamentary practice through the exploration of the embedded patterns of connectivity and reactivity between the exchanging nodes (speakers).
P 2 . 3 USE OF SCAN OBSERVATIONS FOR VALIDATION OF SOIL MOISTURE SPATIAL DISTRIBUTION SIMULATED BY THE LAND-SURFACE MODEL OVER THE LOWER MISSISSIPPI DELTA REGION

Surface soil moisture levels control the heat partitionbetween latent and sensible heat fluxes. Due to this control soil moisture spatial variability represents an important factor affecting surface and planetary boundary layer thermodynamic properties. It has a direct impact on development of weather systems, especially at the local scale. Therefore, a retrieval of accurate and reliable estimates of soil moisture content at local, regional, and planetary scales represents an important practical task (Smirnova et al., 2000) for Numerical Weather Prediction (NWP). These estimates are also considered as a valuable input for various environment models including water management, agriculture, and forestry applications.
Framing the Undergraduate Research Experience: Discovery Involvement in Retailing Undergraduate Education

We provide an overview of ways to involve undergraduate business and retailing students in faculty research projects and discuss advantages of these student–faculty collaborations. We use Kolb’s experiential learning cycle to provide a framework for creating an effective and engaging undergraduate research experience and use it to classify types of projects in which our undergraduate students have participated. These types of projects include (a) theory building and testing, (b) empirical research using primary or secondary data collection, (c) practitioner-oriented projects, and (d) creative projects. We provide specific examples of each type of project and then apply Kolb’s experiential learning cycle roles to a variety of undergraduate research projects. We conclude with a discussion of the benefits of involving undergraduate students in research projects.
Degeneracy in the ensemble Monte Carlo method for high-field transport in semiconductors

An algorithm to include the Pauli exclusion principle in the Ensemble Monte Carlo method is presented. The results indicate that significant changes in the transport properties of GaAs have to be expected when degenerate conditions are reached. Important repercussions should be found in the modeling of microwave devices, where one often deals with highly doped regions.
A MEDIATORY ROLE OF FINANCE EDUCATION IN PERCEPTION OF CHOSEN ECONOMIC ASPECTS IN FAMILY AND NON-FAMILY FIRMS

: Proper goals setting respects the current market circumstances, considers assessment of facts, respects various options and takes into consideration their consequences. Different decisions in companies are often taken fast, under stress, when emotions or other factors are likely to influence their results. The paper considers three aspects: self-assessment of the companies’ performance related to the industry, economic goals and economic education. The aim is to investigate whether education can mediate the interrelation between self-assessment of companies’ performance and salience of their economic goals. With the use of linear regression models of mediation and correlation coefficients it was pointed out that the self-esteem of companies’ performance compared to the sector as a factor of financial decision is mitigated by economic education.
A New Approach to Predicting Analyst Forecast Errors: Do Investors Overweight Analyst Forecasts?

I provide evidence that investors overweight analyst forecasts by demonstrating that prices do not fully reflect predictable components of analyst errors, which conflicts with conclusions in prior research. I highlight estimation bias in traditional approaches and develop a new approach that reduces this bias. I estimate characteristic forecasts that map current firm characteristics into forecasts of future earnings. Contrasting characteristic and analyst forecasts predicts analyst forecast errors and revisions. I find abnormal returns to strategies that sort firms by predicted forecast errors, consistent with investors overweighting analyst forecasts and predictable biases in analyst forecasts influencing the information content of prices.