Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the immunogenicity of different therapeutic preparations of human factor VIII in the murine model of hemophilia A

Von Willebrand factor (VWF) has been proposed to reduce the immunogenicity of therapeutic factor VIII (FVIII) in patients with hemophilia A. Using FVIII-deficient mice, we compared the immunogenicity of different preparations of plasma-derived (pd) and recombinant (r) FVIII. Treatment of mice with pdFVIII induced significantly lower titers of FVIII inhibitors, as measured by ELISA and in vitro coagulation assays, compared with rFVIII. Furthermore, pre-incubation of rFVIII with excess VWF significantly reduced rFVIII immunogenicity. Our data confirm that pdFVIII induces lower levels of inhibitors than rFVIII, and that VWF is an immuno-chaperone molecule for FVIII.
What Is a Good Risk Measure: Bridging the Gaps between Data, Coherent Risk Measures, and Insurance Risk Measures

Two main axiomatically based risk measures are the coherent risk measure, which assumes subadditivity for random variables, and the insurance risk measure, which assumes additivity for comonotonic random variables. We propose a new, data based, risk measure, called natural risk statistic, that is characterized by a new set of axioms. The new axioms only require subadditivity for comonotonic random variables, which is consistent with the prospect theory in psychology. Comparing to two previous measures, the natural risk statistic includes the tail conditional median which is more robust than the tail conditional expectation suggested by the coherent risk measure; and, unlike insurance risk measures, the natural risk statistics can also incorporate scenario analysis. The natural risk statistic includes the VaR as a special case and therefore shows that VaR, though simple, is not irrational.
MarkovBin: An Algorithm to Cluster Metagenomic Reads Using a Mixture Modeling of Hierarchical Distributions

Metagenomics is the study of genomic content of microorganisms from environmental samples without isolation and cultivation. Recently developed next generation sequencing (NGS) technologies efficiently generate vast amounts of metagenomic DNA sequences. However, the ultra-high throughput and short read lengths make the separation of reads from different species more challenging. Among the existing computational tools for NGS data, there are supervised methods that use reference databases to classify reads and unsupervised methods that use oligonucleotide patterns to cluster reads. The former may leave a large fraction of reads unclassified due to the absence of closely related references. The latter often rely on long oligonucleotide frequencies and are sensitive to species abundance levels. In this work, we present MarkovBin, a new unsupervised method that can accurately cluster metagenomic reads across various species abundance ratios. We first model the nucleotide sequences as a fixed-order Markov chain. We then propose a hierarchical distribution to model the dependency between paired-end reads. Finally, we employ the mixture model framework to separate reads from different genomes in a metagenomic dataset. Using extensive simulation data, we demonstrate a high accuracy and precision by comparing to selected unsupervised read clustering tools. The software is freely available at http://orleans.cs. wayne.edu/MarkovBin.
Multicast Partition Problem for Smart Home Applications

In smart home, multimedia services can be provided to users, who want to receive multimedia data through mobile phones. Most of multimedia applications are implemented using the streaming technique from contents provider to subscribers. It is important to manage the buffering amount remained in the user side in order to support the adequate quality of video contents and minimize complaints of subscribers due to buffer starvation. In the wireless environment, it is difficult to provide several subscribers with multicast traffic stably, since the data rate which users can receive varies according to the channel state of each user. Therefore, we propose and discuss an efficient usage together with MIU (Multicast In Unicast portion) as well as MIM (Multicast In Multicast portion) in order to maximize the aggregate throughput of users and minimize the starvation duration degrading QoS supported to users. We consider the multicast partitioning problem applying list partitioning algorithm and provide the analytical foundation that the proposed scheme can be evaluated. Simulation results show that proposed model can provide enhanced performance and allowed streaming quality.
Unemployment vs. in-work bene...ts with search unemployment and observable abilities

This paper explores the optimal interaction between the tax system and unemployment compensation in insuring people against the risks of involuntary unemployment and low ability. To that end, we introduce search unemployment in a model of optimal non-linear income taxation. We . nd that the optimal search subsidy (i.e. the di¤erence between the in-work bene.t and the unemployment bene.t) increases if, for e¢cient agents, the participation constraint (governing job search) becomes relatively more important than the incentive compatibility constraint (determining hours worked). The relation between unemployment bene.ts and the optimal level of in-work bene.ts (the number of people exerting positive work e¤ort) is U (inversely U) shaped.
Economic Factors Affecting Home Mortgage Disclosure Act Reporting

The release of the 2004-2005 Home Mortgage Disclosure Act data raised a number of questions given the increase in the number and percentage of higher-priced home mortgage loans and continued differentials across demographic groups. This paper assesses three possible explanations for the observed increase in 2005 over 2004: (1) changes in lender business practices; (2) changes in the risk profile of borrowers; and (3) changes in the yield curve environment. Results suggest that after controlling for the mix of loan types, credit risk factors, and the yield curve, there was no statistically significant increase in reportable volume for loans originated directly by lenders during 2005, though indirect, wholesale originations did significantly increase. The findings also reveal that the market price of risk increased by about 15 basis points in 2005 versus 2004, implying that mortgage costs increased for all borrowers on a risk-adjusted basis.
Exploiting File System Awareness for Improvements to Storage Virtualization

Exploiting File System Awareness for Improvements to Storage Virtualization Vivek Lakshmanan Master of Science Graduate Department of Computer Science University of Toronto 2009 File systems are tasked with storing, organizing, and retrieving valuable data for long periods of time. This requires them to provide excellent fault-tolerance and reliability standards throughout their extended lifetime. As a result, most file systems follow a conservative development model and evolve slowly. In comparison, the underlying storage hardware, as well as the requirements of the modern storage environment are changing much more rapidly. Due to the growing adoption of Storage Virtualization, hardware configurations have grown in complexity, but the file system is shielded from the details by the block interface. This puts file systems at a disadvantage when trying to make efficient use of the underlying hardware and in general, acts as an impediment to meeting higher-level goals for the storage stack entirely from within the file system. At the same time, the block interface also obfuscates most of the vital file system context from the hardware, limiting its ability to compensate for the file system’s limitations and make sound decisions for the entire storage system. We show that simple hints from the file system exposed to a cognisant block layer can be effective in achieving high-level goals for the storage system without requiring functional modifications to existing components of the storage stack, like the file system itself or the block interface. We demonstrate the viability of our approach by achieving significantly improved corruption detection and failure recovery performance without causing a discernible slowdown in most normal file system workloads. In fact, we sped-up certain metadata-heavy workloads by upto 60%.
HYPERCRITICAL ACCRETION, INDUCED GRAVITATIONAL COLLAPSE, AND BINARY-DRIVEN HYPERNOVAE

The induced gravitational collapse (IGC) paradigm has been successfully applied to the explanation of the concomitance of gamma-ray bursts (GRBs) with supernovae (SNe) Ic. The progenitor is a tight binary system composed of a carbon–oxygen (CO) core and a neutron star (NS) companion. The explosion of the SN leads to hypercritical accretion onto the NS companion, which reaches the critical mass, hence inducing its gravitational collapse to a black hole (BH) with consequent emission of the GRB. The first estimates of this process were based on a simplified model of the binary parameters and the Bondi–Hoyle–Lyttleton accretion rate. We present here the first full numerical simulations of the IGC phenomenon. We simulate the core-collapse and SN explosion of CO stars to obtain the density and ejection velocity of the SN ejecta. We follow the hydrodynamic evolution of the accreting material falling into the Bondi–Hoyle surface of the NS all the way up to its incorporation in the NS surface. The simulations go up to BH formation when the NS reaches the critical mass. For appropriate binary parameters, the IGC occurs in short timescales ∼102–103 s owing to the combined effective action of the photon trapping and the neutrino cooling near the NS surface. We also show that the IGC scenario leads to a natural explanation for why GRBs are associated only with SNe Ic with totally absent or very little helium.
Novel Inducers of the Envelope Stress Response BaeSR in Salmonella Typhimurium: BaeR Is Critically Required for Tungstate Waste Disposal

The RpoE and CpxR regulated envelope stress responses are extremely important for SalmonellaTyphimurium to cause infection in a range of hosts. Until now the role for BaeSR in both the Salmonella Typhimurium response to stress and its contribution to infection have not been fully elucidated. Here we demonstrate stationary phase growth, iron and sodium tungstate as novel inducers of the BaeRregulon, with BaeR critically required for Salmonella resistance to sodium tungstate. We show that functional overlap between the resistance nodulation-cell division (RND) multidrug transporters, MdtA, AcrD and AcrB exists for the waste disposal of tungstate from the cell. We also point to a role for enterobactinsiderophores in the protection of enteric organisms from tungstate, akin to the scenario in nitrogen fixing bacteria. Surprisingly, BaeR is the first envelope stress response pathway investigated in S. Typhimurium that is not required for murine typhoid in either ityS or ityR mouse backgrounds. BaeR is therefore either required for survival in larger mammals such as pigs or calves, an avian host such as chickens, or survival out with the host altogether where Salmonella and related enterics must survive in soil and water.
An overview of modeling technology of speaker recognition

With the development of the ubiquitous telephone network, speaker recognition-a more direct authentication technology attracts further attention. In this paper, an overview on the primary parts of current speaker recognition technology-modeling technology is presented. Based on the typical system structure we discussed the effective modeling techniques for both speaker and background, and distortion compensation techniques. Some issues on the technical challenges of current speaker recognition applications are also included so that some potential future trend in research, development and applications may be concluded.
The Circular effects of Popularity Information and Electronic Word-of-Mouth on Consumer Decision-Making: Evidence from a crowdfunding Platform

Potential backers of crowdfunding campaigns often need to make investment decisions based on limited information, as the project they invest in has not come into existence at the time the campaign is running. As a consequence, other evidence for the trustworthiness and quality of a crowdfunding campaign, such as popularity information and electronic Word-of-Mouth (eWOM), are becoming increasingly important. In order to identify interdependencies between these influential factors and the backers’ decisions, we deploy the Panel Vector Auto-Regression (PVAR) methodology to estimate impulse-response functions that depict the response of one variable to a shock in another variable. Preliminary results from Kickstarter suggest that a positive shock in popularity information is associated with a higher number of campaign backers in the next period. The same is true for eWOM within the social networks Twitter and Facebook. Despite strong feedback cycles within platforms, our preliminary results show little evidence for cross-platform effects between social media and the number of backers and vice versa. First results show that our current understanding of the impact of popularity information and eWOM on decision making is far from conclusive. We will further validate these findings by extending the dataset, both in time and scope.
Inducing Strategic Bias: and its implications for Choice Modelling design

It has been suggested that the nature of the task within a multi-attribute multi-alternative choice experiment may be sufficiently complex to make it difficult for individuals to develop response strategies to strategically bias their answers. This experiment tested that hypothesis by setting experimental conditions that provide incentives for strategic bias. By changing design parameters one can investigate whether the strategic bias can be reduced. The answer is effectively no: under most circumstances, respondents could find a strategy that achieved significant bias in inferred preferences. The circumstances where this did not occur (involving ranking alternatives, rather than selecting a single preferred alternative) the inferred preferences reflected neither the intended bias, nor their original preferences, making the answers useless to both respondent and researcher.
Unobtrusive Augmentation of Physical Environments

ii iii Abstract The fundamental idea of Augmented Reality (AR) is to improve and enhance our perception of the surroundings, through the use of sensing, computing and display systems that make it possible to augment the physical environment with virtual computer graphics. AR is, however, often associated with user-worn equipment, whose current complexity and lack of comfort limit its applicability in many scenarios. The goal of this work has been to develop systems and techniques for uncomplicated AR experiences that support sporadic and spontaneous interaction with minimal preparation on the user's part. This dissertation defines a new concept, Unobtrusive AR, which emphasizes an optically direct view of a visually unaltered physical environment, the avoidance of user-worn technology, and the preference for unencumbering techniques. The first part of the work focuses on the design and development of two new AR display systems. They illustrate how AR experiences can be achieved through transparent see-through displays that are positioned in front of the physical environment to be augmented. The second part presents two novel sensing techniques for AR, which employ an instrumented surface for unobtrusive tracking of active and passive objects. These techniques have no visible sensing technology or markers, and are suitable for deployment in scenarios where it is important to maintain the visual qualities of the real environment. The third part of the work discusses a set of new interaction techniques for spatially aware handheld displays, public 3D displays, touch screens, and immaterial displays (which are not constrained by solid surfaces or enclosures). Many of the techniques are also applicable to human-computer interaction in general, as indicated by the accompanying qualitative and quantitative insights from user evaluations. The thesis contributes a set of novel display systems, sensing technologies, and interaction techniques to the field of human-computer interaction, and brings new perspectives to the enhancement of real environments through computer graphics. iv v Acknowledgments
Analysis on the Resemblance between Export and Import Commodities Price Index in Nigeria

This study examined the resemblance between export and import commodities price index in Nigeria. The source of data employed in this study is secondary data obtained from Central bank of Nigeria Statistical Bulletin 2010. The statistical tools employed were the Mantel test statistic and the Bar chart analysis. From the result of the analysis it was found that commodities price index for export and import in Nigeria has about 67% resemblance and a significant measure of 0.07 which falls on the acceptance region of the hypothesis; assuming a 95% confidence interval. Also, from the graphical expression, it was observed that across the years import commodities price index has been greater than export commodities price index. This result implies that export and import commodities price index in Nigeria has a positive resemblance which can be attributed to the recent adoption of the diversification psychology by the Nigerian government and at present has not yielded full benefits that can enable export commodities price index close on import commodities price index in Nigeria.
Running Real-World Software on Simulated Wireless Sensor Nodes

In the domain of wireless sensor networks, simulation is the predominant way of evaluating new algorithms. One commonly found drawback of simulation tools is that they provide a programming environment that does not match the one present on real-world platforms. In this paper, we address this problem by presenting the steps required to port an existing software stack to a popular network simulator. This novel procedure allows for applications to run both in the simulated environment and on realworld sensor nodes without any changes to the source code. We verify our results by means of performance analyses of several simulated applications.
Censured Exchange Rates in a Discrete Time Target Zones Model: The Spanish Peseta/Deutsche Mark Case

The literature on “ Target Zones” is characterized by a continuous stochastic time modelization, where the exchange rate is a non-censured dependent variable. In this paper we propose a discrete time target zones model, taking into account the censured disposition of the exchange rate, whose parameters will be estimated by the FIML method. The settled theoretical model is a simpli.ed version of Dornbusch ́s (1976) model, applied in a two countries environment. It will be tested into a peseta/deutsche mark exchange rate frame, from june 1989 to may 1998. The period is split in two sub-samples thinking over the enlargement of bands decided in august 1993. The estimation procedure of the model is based on the limited dependent rational expectation technique developed by Pesaran and Ruge-Murcia (1999). The results point out weightily di¤erences between the two considered sample periods.
Fast incremental clustering and representation of a 3D point cloud sequence with planar regions

An incremental clustering technique to partition 3D point clouds into planar regions is presented in this paper. The algorithm works in real-time on unknown and noisy data, without any initial assumption. An iterative cluster growing technique is proposed in order to correctly classify a flow of 3D points and to merge close regions. The computational efficiency of the approach is achieved by using an Incremental Principal Component Analysis (IPCA) technique, and with the adoption of a compact geometrical representation based on the concave-hull computation of each cluster. This solution adds a more realistic representation of the observed environment and reduces the number of points needed to identify the cluster shape. The effectiveness of the proposed algorithm has been validated with both synthetic and real data sets.
The Online Discovery Problem and Its Application to Lifelong Reinforcement Learning

Transferring knowledge across a sequence of related tasks is an important challenge in reinforcement learning. Despite much encouraging empirical evidence that shows benefits of transfer, there has been very little theoretical analysis. In this paper, we study a class of lifelong reinforcement-learning problems: the agent solves a sequence of tasks modeled as finite Markov decision processes (MDPs), each of which is from a finite set of MDPs with the same state/action spaces and different transition/reward functions. Inspired by the need for cross-task exploration in lifelong learning, we formulate a novel online discovery problem and give an optimal learning algorithm to solve it. Such results allow us to develop a new lifelong reinforcement-learning algorithm, whose overall sample complexity in a sequence of tasks is much smaller than that of single-task learning, with high probability, even if the sequence of tasks is generated by an adversary. Benefits of the algorithm are demonstrated in a simulated problem.
McCulloch-Pitts Brains and Pseudorandom Functions

Abstract In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system. Motivated by EEG recordings of normal brain activity, Chvátal and Goldsmith asked whether these dynamical systems can be engineered to produce trajectories that are irregular, disorderly, and apparently unpredictable. We show that they cannot build weak pseudorandom functions.
Broadband cavity antennas with highly directive emissions

Recently, the concept of partially reflecting surface (PRS) with positive reflection phase gradients has been proposed for the design of wideband, cavity antennas. In this work, we numerically implemented this idea and demonstrated a broadband and highly directive Fabry-Pérot cavity antennas (FPCAs) with dual layer PRS operating in the 8–12GHz frequency band. Good performance of impedance matching bandwidth (S11 < −10 dB), and a peak directivity of about 17 dB at around 9.5 GHz were predicted through numerical simulations. Experimental prototypes are being manufactured for experimental validations.
Comparison Between Three Memory Tests: Cued Recall, Priming and Saving Closed-Head Injured Patients and Controls

Twenty closed-head injured (CHI) patients and 20 matched controls were tested with three different memory tasks: cued recall, word stem completion (WSC), and saving. Saving is defined as the advantage of relearning of a list of word pairs, in terms of the number of learning trials to the criterion of one errorless trial, over the original learning of the same list. It was predicted that CHI patients' explicit memory (i.e., cued recall), but not implicit memory (i.e., WSC), would be impaired. The question addressed in this study is whether the memory of CHI patients will be impaired when memory is tested with a saving task, with 2 weeks delay between original learning and relearning. The findings confirm impairment of CHI patients in explicit memory, although the learning rate is preserved. Implicit memory is preserved in CHI patients only when based on reactivation of preexisting knowledge, but not when dependent on forming new associations. Finally, the CHI patients, even after 2 weeks delay, demonstrated a significant saving in relearning old, as compared to new, pairs of words. The clinical contribution of this study is the delineation of those aspects of memory that are impaired and those that are preserved in CHI patients. The theoretical implications of the finding that memory could be preserved in CHI patients when measured by saving, are discussed in terms of the relationship between implicit memory and saving.