Miracle-dz/UniFAR-FTUs huggingface dataset:
Fair value accounting: information or confusion for financial markets?

This paper examines whether and how fair value measurement and disclosure by US bank holding companies influences financial analysts’ ability to forecast earnings. Fair value measurement relates to more dispersed forecasts. Measurement basis disclosure (levels 1, 2 and 3) enacted by SFAS 157 translates into more accurate forecasts but has neutral effects for banks with a sizable proportion of assets at fair value. Furthermore, level 2 measurement relates to enhanced forecast accuracy, while level 3 measurement relates to increased forecast dispersion. These contrasting results reflect analysts’ underlying information environment, with level 2 measurement translating into higher quality private and public information and level 3 into reductions in the quality of private and public information. Results do not change after controlling for assets’ underlying riskiness. Overall, it appears that analysts perceive that managers convey useful information through level 2 figures but act opportunistically in measuring level 3 fair value figures.
Fast Reaction Limit and Large Time Behavior of Solutions to a Nonlinear Model of Sulphation Phenomena

We investigate the qualitative behavior of solutions to the initial-boundary value problem on the half-line for a nonlinear system of parabolic equations, which arises to describe the evolution of the chemical reaction of sulphur dioxide with the surface of calcium carbonate stones. We show that, both in the fast reaction limit and for large times, the solutions of this problem are well described in terms of the solutions to a suitable one phase Stefan problem on the same domain.
Secure wireless communication under spatial and local Gaussian noise assumptions

We consider wireless communication between Alice and Bob when the intermediate space between Alice and Bob is controlled by Eve. That is, our model divides the channel noise into two parts, the noise generated during the transmission and the noise generated in the detector. Eve is allowed to control the former, but is not allowed to do the latter. While the latter is assumed to be a Gaussian random variable, the former is not assumed to be a Gaussian random variable. In this situation, using backward reconciliation and the random sampling, we propose a protocol to generate secure keys between Alice and Bob under the assumption that Eve's detector has a Gaussian noise and Eve is out of Alice's neighborhood. In our protocol, the security criteria are quantitatively guaranteed even with finite block-length code based on the evaluation of error of the estimation of channel.
Cyclin T1/CDK9 Interacts with Influenza A Virus Polymerase and Facilitates Its Association with Cellular RNA Polymerase II

ABSTRACT Influenza virus RNA-dependent RNA polymerase scavenges the 5′ cap from host pre-mRNA to prime viral transcription initiation. It is also well established that viral RNA-dependent RNA polymerase (vRNP) associates with cellular RNA polymerase II (Pol II), on which viral replication depends. Here we report that cyclin T1/CDK9 can interact with influenza virus polymerase and facilitate its association with cellular Pol II. The immunodepletion of cyclin T1/CDK9 totally abolished the association of vRNP with the C-terminal domain (CTD) Ser-2-phosphorylated form of RNA polymerase II. Further studies showed that overexpression of cyclin T1/CDK9 increased the transcription activity of vRNP, while knockdown of cyclin T1/CDK9 impaired viral replication. Our results suggest that cyclin T1/CDK9 serves as an adapter to mediate the interaction of vRNP and RNA Pol II and promote viral transcription.
Avoiding Contradictions in the Paradoxes, the Halting Problem, and Diagonalization

The fundamental proposal in this article is that logical formulas of the form (f ~f) are not contradictions, and that formulas of the form (t t) are not tautologies. Such formulas, wherever they appear in mathematics, are instead reason to conclude that f and t have a third truth value, different from true and false. These formulas are circular definitions of f and t. We can interpret the implication formula (f ~f) as a rule, a procedure, to find the truth value of f on the left side: we just need to find the truth value of f on the right side. When we use the rules to ask if f and t are true or false, we need to keep asking if they are true or false over and over, forever. Russell's paradox and the liar paradox have the form (f ~f). The truth value provides a straightforward means of avoiding contradictions in these problems. One broad consequence is that the technique of proof by contradiction involving formulas of the form (f ~f) becomes invalid. One such proof by contradiction is one form of proof that the halting problem is uncomputable. The truth value also appears in Cantor's diagonal argument, Berry's paradox, and the Grelling-Nelson paradox.
A comparative study of hybrid, neural networks and nonparametric regression models in time series prediction

This paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furthermore, various multilayer perceptron algorithms and radial basis function network model are regarded as the artificial neural networks components. The performances of these models are compared by forecasting the series of number of produced Cars and Domestic product per capita (GDP) data occurred in Turkey. This comparisons show that hybrid models proposed in this paper have denoted much more excellent performance than the hybrid models in literature.
Giving Them What They Want: Online Feedback That Works

Many instructors can recall times when they have handed back assignments with written feedback and could feel the confusion in the room as students read through their comments. Perhaps they fielded questions at that moment or met with students after class to clarify their expectations of revision. This timely teaching is essential to ensure that students can continue the learning process. For online educators, who spend a significant amount of time giving specific feedback on assignments in a virtual setting, it is almost impossible to know how that feedback is received or understood by the student. In an online environment, feedback is essential to build relationships between the instructor and student (Bonnel, Ludwig, & Smith 2007; Leibold & Schwarz 2015); therefore, we must consider how we can adjust our teaching practices to match the new mediums we teach in. According to Weaver (2006), misinterpretation of feedback is likely to occur and interfere with students’ ability or willingness to respond to feedback. In order to find out what type of feedback works best for students, they respond to a feedback survey after each formative assessment. Table 1 shows select quotes regarding students’ perception of the “most helpful piece of feedback” that they received as well as an explanation of the feedback strategy associated with each comment. Specific attention was given to the order in which online feedback strategies were used. Video explanation of the feedback process was delivered pre-assessment, strategies that support dialogue and suggest resources were ongoing, and the feedback survey was delivered post-assessment. With reduced opportunities to visually gauge student understanding, systematically using these modified feedback strategies increases student learning in the online environment. References
Neural network versus behavior based approach in simulated car racing game

This paper presents examines the design of controllers that is computationally efficient yet demonstrates highly competitive performance for a real time simulated car racing game. Algorithms that require large amount of computational resources are impractical for fast paced and real time games (i.e. racing games, sports simulators, first person shooters and real time strategy games). This paper examines the design of two computationally efficient approaches, neural networks and behaviour based intelligence, in the context of a real time car racing game. Both approaches are optimized using evolutionary strategies. The behaviour based approach was found to obtain a higher fitness value yet being more computationally efficient. The design approaches can also be applied to real-time face animation which involves data-intensive computations.
Multihop Data Message Transmission with Inter-Vehicle Communication and Store-Carry-Forward in Sparse Vehicle Ad-Hoc Networks (VANET)

In wireless multihop data message transmission by multiple mobile computers in ITS systems, even though in an area with sparsely distributed mobile computers, mobility of a computer with transmitting data messages helps to achieve high data message transmission ratio. This paper proposes data message transmission along a sequence of roads detected by a GPS satellite navigation system. Due to combination of dynamic determination of intermediate mobile computers and a store-carry-forward approach, higher transmission ratio and shorter end-to-end delay are achieved even with high mobility and sparse distribution.
Identification of normalized coprime plant factors from closed loop experimental data ‡

Recently introduced methods of iterative identification and control design are directed towards the design of high performing and robust control systems. These methods show the necessity of identifying approximate models from closed loop plant experiments. In this paper a method is proposed to approximately identify normalized coprime plant factors from closed loop data. The fact that normalized plant factors are estimated gives specific advantages both from an identification and from a robust control design point of view. It will be shown that the proposed method leads to identified models that are specifically accurate around the bandwidth of the closed loop system. The identification procedure fits very naturally into a recently developed the iterative identification/control design scheme based on H∞ robustness optimization.
Empathy in Undergraduate Medical Students of Bangladesh: Psychometric Analysis and Differences by Gender, Academic Year, and Specialty Preferences

Empathy is considered to be associated with better patient compliance, satisfaction, and clinical outcomes. The aim of the study is to measure and examine empathy among a sample of undergraduate medical students of Bangladesh. It was a cross-sectional study and all the medical students of first through fifth year enrolled at Chattagram Maa-O-Shishu Hospital Medical College during the study period of 2014 were surveyed. Participants anonymously completed the Jefferson Scale of Empathy Medical Student version translated into Bengali language, a valid and reliable 20-item self-administered questionnaire. Principal component factor analysis with varimax rotation and Cronbach's alpha coefficient were calculated to check validity and reliability of the scale. ANOVA was used to examine the differences in empathy between gender, academic years, and specialty preferences. The mean empathy score was 110.41 ± 13.59. Cronbach's alpha coefficient was 0.88. There were significant associations between gender and empathy scores. The level of empathy in medical students gradually increases after clinical training in medical college. A nonsignificant difference was found between empathy scores and specialty preferences. It is suggested that the medical curriculum in Bangladesh should include more extensive program to promote empathy and other humanistic values among the medical students.
The Apolipoprotein E Polymorphism rs7412 Associates with Body Fatness Independently of Plasma Lipids in Middle Aged Men

Background The apolipoprotein E (APOE) gene is polymorphic, encoding one of 3 common alleles (ε2, ε3, ε4) produced from combinations of 2 non-synonymous SNPs (rs429358 and rs7412). APOE plays an important role controlling plasma lipids but its association with adipocyte functionality and body fatness remains to be determined. Methods We analyzed fasting plasma lipids and genotyped the two main APOE-SNPs (rs429358 and rs7412), both located in the fourth exon of the APOE, in 4660 Caucasian middle-aged men free of cardiovascular disease. Results The rs7412 SNP, which determines the APOE2 isoform, was significantly associated with Body Mass Index (BMI) and Waist Girth (WG) in a multivariate model accounting for age, smoking status and plasma lipids. BMI and WG were highest in TT homozygotes and lowest in CC homozygotes. This effect was independent of the rs429358 SNP, which failed to show any association with the BMI and WG variables. The odds ratio of being obese (BMI>30) for individuals carrying the APOε2 allele, present in 14% of the cohort and defined by the rs7412 SNP, was also significant in this multivariate model, with an OR of 1.27 (95% CI: 1.01–1.59). Conclusions This study provides an evidence of a lipid-independent association between the APOE SNP rs7412 and body fatness surrogates, BMI and WG, in a large cohort of middle-aged males.
Field testing a wireless sensor network for reactive environmental monitoring [soil moisture measurement]

Wireless sensor network technology promises to reveal fine-grained, dynamic changes in monitored variables of an outdoor landscape. But there are significant problems to be overcome in translating this vision to working systems. This paper describes the design and implementation of a reactive, event driven network for environmental monitoring of soil moisture and evaluates the effectiveness of this solution. A novel feature of our solution is its reactivity to the environment: when rain falls and soil moisture is changing rapidly, measurements are collected frequently, whereas during dry periods, between rainfall events, measurements are collected much less often. Reactivity allows us to focus on dynamic responses and limit the amount of useless data gathered, as well as improving robustness and network lifetime. The main contribution of the paper is to demonstrate that a reactive sensor network can deliver useful data on soil moisture responses to rainfall. Field trial results on the reactivity, robustness and longevity of the network are evaluated, and future improvements proposed.
SocioScape – a Tool for Interactive Exploration of Spatio-Temporal Group Dynamics in Social Networks

We introduce SocioScape, an interactive tool for the visual exploration of spatially referenced, dynamic social networks. The tool combines a novel depiction methodology that illustrates the evolution of social groups with a spatio-temporal visualization showing the movement of these groups in the environment. The depiction technique used by SocioScape provides an alternative to graphs, allowing domain scientists to easily explore the evolution of the social structure and examine the role of the environment in shaping the social behavior of populations.
Inflation with large gravitational waves

It is well known that in manifestly Lorentz invariant theories with nontrivial kinetic terms, perturbations around some classical backgrounds can travel faster than light. These exotic "supersonic" models may have interesting consequences for cosmology and astrophysics. In particular, one can show that in such theories the contribution of the gravitational waves to the CMB fluctuations can be significantly larger than that in standard inflationary models. This increase of the tensor-to-scalar perturbation ratio leads to a larger B-component of the CMB polarization, thus making the prospects for future detection much more promising. Interestingly, the spectral index of scalar perturbations and mass of the scalar field considered in the model are practically indistinguishable from the standard case. Whereas the energy scale of inflation and hence the reheating temperature can be much higher compared to a simple chaotic inflation.
Ipsilateral and Contralateral Retinal Ganglion Cells Express Distinct Genes during Decussation at the Optic Chiasm

Visual Abstract The increasing availability of transcriptomic technologies within the last decade has facilitated high-throughput identification of gene expression differences that define distinct cell types as well as the molecular pathways that drive their specification. The retinal projection neurons, retinal ganglion cells (RGCs), can be categorized into distinct morphological and functional subtypes and by the laterality of their projections. Here, we present a method for purifying the sparse population of ipsilaterally projecting RGCs in mouse retina from their contralaterally projecting counterparts during embryonic development through rapid retrograde labeling followed by fluorescence-activated cell sorting. Through microarray analysis, we uncovered the distinct molecular signatures that define and distinguish ipsilateral and contralateral RGCs during the critical period of axonal outgrowth and decussation, with more than 300 genes differentially expressed within these two cell populations. Among the differentially expressed genes confirmed through in vivo expression validation, several genes that mark “immaturity” are expressed within postmitotic ipsilateral RGCs. Moreover, at least one complementary pair, Igf1 and Igfbp5, is upregulated in contralateral or ipsilateral RGCs, respectively, and may represent signaling pathways that determine ipsilateral versus contralateral RGC identity. Importantly, the cell cycle regulator cyclin D2 is highly expressed in peripheral ventral retina with a dynamic expression pattern that peaks during the period of ipsilateral RGC production. Thus, the molecular signatures of ipsilateral and contralateral RGCs and the mechanisms that regulate their differentiation are more diverse than previously expected.
MySpace Video Recommendation with Map-Reduce on Qizmt

Recent years have seen a surge in online video content which is often used as a communication medium and information resource by users. The explosive growth in content has given rise to the need of developing effective recommendation system which can help users discover meaningful and interesting videos. In this paper, we present a large-scale Map-Reduce video recommendation system. Our approach includes item-to-item collaborative filtering using video views data, and involves content analysis of video metadata to extract feature representation for identifying similar videos for recommendation. Recommendation results are further filtered through a refinement stage using semantic similarity. As an integrated pipeline, we show how our proposed approach is implemented in Qizmt which is a. Net MapReduce framework. Additionally, our approach is capable of updating video recommendation index with hourly added video data. We describe our recommendation approach using a portion (23 million) of all videos from My Space and undertake quantitative as well as qualitative evaluation.
A KNOWLEDGE BASED HIERARCHICAL CLASSIFICATION TREE FOR 3 D FACADE MODELING USING TERRESTRIAL LASER SCANNING DATA

Due to high point density, accuracy and cost-effectiveness, Terrestrial Laser Scanner (TLS) do have the great advantage of providing the details of building facade as required for the production of realistic 3D city models. Thus, a significant amount of research efforts has been directed towards reconstructing building facade models using TLS data. Most of reconstruction strategies are based on geometric feature extraction, which is categorized into two groups: point-based and segmentation-based boundary extraction. However, little attention was paid on further recognizing and separating those extracted geometric features, result of which is helpful for 3D building modeling at LOD3. This paper presents an approach to recognize 3D facade features automatically from TSL data based on heuristic knowledge of geometrical and topological configuration of facade objects. All the facade knowledge is stored in a hierarchical tree, which guides the classification process. Finally, a confusion matrix is created to evaluate the classification performance.
Diffusion Tensor MR Imaging in Chronic Spinal Cord Injury

BACKGROUND AND PURPOSE: Diffusion tensor MR imaging is emerging as an important tool for displaying anatomic changes in the brain after injury or disease but has been less widely applied to disorders of the spinal cord. The aim of this study was to characterize the diffusion properties of the entire human spinal cord in vivo during the chronic stages of spinal cord injury (SCI). These data provide insight into the structural changes that occur as a result of long-term recovery from spinal trauma. MATERIALS AND METHODS: Thirteen neurologically intact subjects and 10 subjects with chronic SCI (>4 years postinjury) were enrolled in this study. A single-shot twice-refocused spin-echo diffusion-weighted echo-planar imaging pulse sequence was used to obtain axial images throughout the entire spinal cord (C1-L1) in <60 minutes. RESULTS: Despite heterogeneity in SCI lesion severity and location, diffusion characteristics of the chronic lesion were significantly elevated compared with those of uninjured controls. Fractional anisotropy was significantly lower at the chronic lesion and appeared dependent on the completeness of the injury. Conversely, mean diffusivity measurements in the upper cervical spinal cord in subjects with SCI were significantly lower than those in controls. These trends suggest that the entire neuraxis may be affected by long-term recovery from spinal trauma. CONCLUSION: These results suggest that diffusion tensor imaging may be useful in the assessment of SCI recovery.
Allocating Portfolio Economic Capital to Sub-Portfolios

Risk adjusted performance measurement for a portfolio involves calculating the contributions to total economic capital for sub-portfolios or single assets. We show that there is only one definition for the contributions which is suitable for performance measurement, namely as derivative of the underlying risk measure with respect to the weight of the considered sub-portfolio or asset. We review the formulas for the derivatives for some popular risk measures including quantile-based value at risk (VaR) and Expected Shortfall in a rather general context.
Fair NURBS curve generation using a hand-drawn sketch for computer aided aesthetic design

Image processing techniques are used to detect the edges of a hand-drawn sketch. Generally, various types of gradation are expressed in the background of a hand-drawn sketch. Therefore, the original image is transformed into a logarithmic image. The entire image has to be binarized for edge detection. To smooth the edge of the binary image, feature based erosion and dilation is applied. A Laplacian operation is applied to the binary image to detect the edge in the binary image. This edge position corresponds to the position of the line in the hand-drawn sketch image. Line segments which represent the detected edges are generated. Using the positions and gradients on the line segments, a NURBS curve is generated. The shape of the NURBS curve extracted from the sketch image is examined by the designer visually. The shape of this NURBS curve generally coincides with the intention of the designer, but does not coincide precisely. Therefore, a curve shape modification method based on the specified radius of curvature is applied. The sum of the squared differences between the radius of curvature of a curve and the specified radius of curvature of an entire curve is linearized by Taylor's theorem, then minimized. The specification of radius of curvature is performed by selecting a predefined fair NURBS curve using the similarity of radius of curvature distribution. Examples of NURBS curve generation based on a hand-drawn sketch are given according to the generation process.