Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of top management team characteristics on a firm’s default risk: Evidence from the United Kingdom SMEs

Top management team (TMT) as the coalition of powerful actors in an organization with their own particular characteristics has a great influence in any decision-making processes and may affects the organizational outcomes accordingly. A number of researches has indicated that TMT characteristics do have influences on a firm’s strategic outcomes and its performance. Yet, none of the literature – in the making of this study – explored the influence of TMT characteristics on a firm’s default risk. Against the backdrop, this study investigates the impact of TMT characteristics on a firm’s default risk, specifically the age, tenure, and size of the TMT and the presence of female top managers in the team, of small and medium-sized enterprises (SMEs). By treating the TMT characteristics as predictors and the existing financial-based default risk measure as the dependent variable, this study shows promising results. Based on the sample consisting of 7151 observations of SMEs across United Kingdom from the year 2013-2016, the results show that the average age and tenure of a TMT and the presence of female top managers in the team have a negative impact on a firm’s default risk. This study contributes in two ways. For the literature, this study acts as the preliminary attempt to explore the impact of TMT characteristics on a firm’s default risk. As for more practical application, this study gives an insight and helps the practitioners in assessing firm’s default risk by taking into account the TMT characteristics in the process.
The case for linguistic narrative analysis, illustrated studying small firms in tourism

Abstract We advocate the adoption of more expansive and creative methodological approaches the study of tourism. More specifically, we argue that by examining how individuals narrate their experiences and social practices, researchers can gain an insight into the meanings actors attach to their actions. Considered from this perspective, narratives become performative; they prompt actors to take actions that (they feel) actualise the story they are seeking to tell. To illustrate its value, we use linguistic narrative analysis to explore how the owner-managers of small values-based tourism firms narrate the operation of their business. A dataset of first person accounts made of both narratives in storified form featuring a chronological order with beginning, middle, and end, and narratives without a storified form largely recounting opinions, feelings, and points of view, are interpreted to offer new perspectives on the behaviours of small firms in tourism. We argue that narrative approaches should complement methods used routinely by tourism scholars to examine this constituency of actors (and others).
Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions

Resolving a conjecture of Abbe, Bandeira, and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model (SBM) of two equal-sized clusters. The same was shown for the case of a single cluster and outliers. Extending the proof techniques, in this paper, it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: 1) binary SBM with two clusters of sizes proportional to network size but not necessarily equal; 2) SBM with a fixed number of equal-sized clusters; and 3) binary censored block model with the background graph being Erdös-Rényi. Furthermore, a sufficient condition is given for an SDP procedure to achieve exact recovery for the general case of a fixed number of clusters plus outliers. These results demonstrate the versatility of SDP relaxation as a simple, general purpose, computationally feasible methodology for community detection.
Solving Multiclass Learning Problems via Error-Correcting Output Codes

Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k "classes"). The definition is acquired by studying collections of training examples of the form (xi, f(xi)). Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C4.5 and CART, application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and application of binary concept learning algorithms with distributed output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range of multiclass learning tasks. We also demonstrate that this approach is robust with respect to changes in the size of the training sample, the assignment of distributed representations to particular classes, and the application of overfitting avoidance techniques such as decision-tree pruning. Finally, we show that--like the other methods--the error-correcting code technique can provide reliable class probability estimates. Taken together, these results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.
Medians and beyond: new aggregation techniques for sensor networks

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the <i>consensus</i> value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.
Numeric Computational Scheme for Adaptive Tearing Model

—To produce details and component of tearing and cracking is complicated. The detail and component for every object would also produce certain type of tearing line. Meanwhile using traditional finite element method will need such a complex computation caused by updated value for every crack made. Such model needed to predict magnitude and angle of tearing or cracking depend on the variables value. To generate tearing model that satisfy the requirements and produce tearing line with small computational cost, numerical scheme will be needed. This research will be focused on thin plates especially paper. Modification of finite element method will be proposed. Every step of process will be done adaptively depending on the existing results each step. Experiments generate a model which stiffness matrix convergent for each time function and each variation of elastic variables such as Young modulus and Poisson’s ratio. Experiments shows that computation time for tearing model without inclusion component works on average 21.17 seconds faster than with inclusion with average 30.57 seconds. Computation time for tearing model with circular inclusion work faster dan linear inclusion.
A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.
Guidance a Stability of Parachute Systems for Planetary Descent

A key requirement for enabling Mars manned mission is the capability to land a payload within 100 m from its nominal target. State of the art technology, however, is still far from reaching this objective. A possible solution for improving the landing accuracy on Mars is the development of a guidance system that exploits thrusters installed on the backshell of the descent vehicle to control its position during the parachute flight. The research aims at evaluating how the stability properties of the parachute-payload spacecraft are influenced by the control action and what performance the resulting closed-loop system can achieve. To do this, first the dynamics of the spacecraft, modelled as a rigid body, has been investigated analytically. Second, its response in different mission scenarios was simulated by means of a multibody model. The results are encouraging and prove that the parachute descent guidance system could efficiently contribute to improving the landing accuracy on Mars.
Price-Based Resource Allocation for Spectrum-Sharing Femtocell Networks: A Stackelberg Game Approach

This paper investigates price-based resource allocation strategies for the uplink transmission of a spectrum-sharing femtocell network, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band as the macrocell. Assuming that the macrocell base station(MBS) protects itself by pricing the interference from the femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and the femtocells subject to a maximum tolerable interference power constraint at the MBS. In particular, two pricing schemes: uniform pricing and non-uniform pricing, are investigated. Then, the Stackelberg equilibriums for the proposed games are studied, and the relationship between the two pricing schemes is examined. It is shown that the nonuniform pricing scheme maximizes the revenue of the MBS, while the uniform pricing scheme maximizes the sum-rate of the femtocell users.
Electron transport chain biogenesis activated by a JNK-insulin-Myc relay primes mitochondrial inheritance in Drosophila

Oogenesis features an enormous increase in mitochondrial mass and mtDNA copy number, which are required to furnish mature eggs with adequate mitochondria and to curb the transmission of deleterious mtDNA variants. Quiescent in dividing germ cells, mtDNA replication initiates upon oocyte determination in the Drosophila ovary, which necessitates active mitochondrial respiration. However, the underlying mechanism for this dynamic regulation remains unclear. Here, we show that an feedforward insulin-Myc loop promotes mitochondrial respiration and biogenesis by boosting the expression of electron transport chain subunits and factors essential for mtDNA replication and expression, and mitochondrial protein import. We further reveal that transient activation of JNK enhances the expression of insulin receptor and initiates the insulin-Myc signaling loop. Importantly, this signaling relay ensures sufficient mtDNA in eggs and limits the transmission of a deleterious mtDNA mutation. Our study demonstrates cellular mechanisms that couple mitochondrial biogenesis and inheritance with oocyte development.
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks

Feedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission. In many FFNs, neurons in one layer have intrinsic properties that are distinct from those in their pre-/postsynaptic layers, but how this affects network-level information processing remains unexplored. Here we show that layer-to-layer heterogeneity arising from lamina-specific cellular properties facilitates signal and information transmission in FFNs. Specifically, we found that signal transformations, made by neighboring layers of neurons on an input-driven spike signal, are complementary to each other. This mechanism boosts information transfer carried by a propagating spike signal, and thereby supports reliable spike signal and information transmission in a deep FFN. Our study suggests that distinct cell types in neural circuits have complementary computational functions and facilitate information processing on the whole.
Meta-Analysis Steps and Reporting

Evidence based medicine is a medical decision making approach using the best available medical evidence. For this, the research question should be clearly defined, the evidence should be investigated, the evidences should be assessed, the medical decisions must be made and implemented and the results of the implementation should be observed. Meta-analysis and systematic review are at the top of the evidence based-pyramid at evidence based medicine. Meta-analysis is an original article that it is the synthesis of a large number of researches to reach a large number of data, provides more reliable information about the subject being searched. When the meta-analysis is prepared; the research question is determined, the literature is searched and appropriate studies are selected, the selected researches are coded, the effect size is calculated, the data are analyzed again according to the effect sizes and meta-analysis is written respectively. The PRISMA Protocol is often used in conducting meta-analysis. This review summarizes steps for evidence based medicine, steps for conducting meta-analysis, the PRISMA Checklist and the PRISMA Flow Diagram which are translated into Turkish. This review is belived to be a valuable contribution to Turkish scientist, especially in planning to conduct a meta-analysis. Kanita dayali tip, elde bulunan en iyi tibbi kanitlari kullanarak tibbi karar verme yaklasimidir. Bu amacla arastirma sorusu net olarak belirlenmeli, kanitlar arastirilmali, belirlenen kanitlar degerlendirilmeli, tibbi karar verilmeli ve uygulanmali, uygulamanin sonuclari gozlenmelidir. Kanita dayali tipta, kanit piramidinin en ust basamaginda meta-analiz ve sistematik derlemeler yer almaktadir. Meta-analiz, cok sayida arastirmayi sentezleyerek genis veri sayisina ulasmayi ve arastirilan konu hakkinda daha objektif bilgiler edinilmesini saglayan bir orijinal arastirma turudur. Meta-analiz hazirlanirken, sirasiyla arastirma sorusu belirlenir; literatur taramasi yapilarak uygun arastirmalar secilir, secilen arastirmalar kodlanir; etki buyuklugu hesaplanir, etki buyukluklerine gore veriler tekrar analiz edilir ve meta-analiz yazilir. Meta-analiz yazim asamasinda siklikla PRISMA Protokolu’nden yararlanilmaktadir. Bu derlemede, kanita dayali tip basamaklari, meta-analiz yazim basamaklari ozetlenmis; PRISMA Kontrol Listesi ve PRISMA Akis Şemasinin Turkce cevirilerine yer verilmistir. Bu arastirmanin ozellikle meta-analiz yapma hazirliginda olan arastirmacilara katkisi olabilecegi dusunulmektedir.
Combining a Quantum Cascade Laser Spectrometer with an Automated Closed-Chamber System for δ13C Measurements of Forest Soil, Tree Stem and Tree Root CO2 Fluxes

Recent advances in laser spectroscopy have allowed for real-time measurements of the 13C/12C isotopic ratio in CO2, thereby providing new ways to investigate carbon cycling in natural ecosystems. In this study, we combined an Aerodyne quantum cascade laser spectrometer for CO2 isotopes with a LI-COR LI-8100A/8150 automated chamber system to measure the δ13C of CO2 during automated closed-chamber measurements. The isotopic composition of the CO2 flux was determined for each chamber measurement by applying the Keeling plot method. We found that the δ13C measured by the laser spectrometer was influenced by water vapour and CO2 concentration of the sample air and we developed a method to correct for these effects to yield accurate measurements of δ13C. Overall, correcting for the CO2 concentration increased the δ13C determined from the Keeling plots by 3.4‰ compared to 2.1‰ for the water vapour correction. We used the combined system to measure δ13C of the CO2 fluxes automatically every two hours from intact soil, trenched soil, tree stems and coarse roots during a two-month campaign in a Danish beech forest. The mean δ13C was −29.8 ± 0.32‰ for the intact soil plots, which was similar to the mean δ13C of −29.8 ± 1.2‰ for the trenched soil plots. The lowest δ13C was found for the root plots with a mean of −32.6 ± 0.78‰. The mean δ13C of the stems was −30.2 ± 0.74‰, similar to the mean δ13C of the soil plots. In conclusion, the study showed the potential of using a quantum cascade laser spectrometer to measure δ13C of CO2 during automated closed-chamber measurements, thereby allowing for measurements of isotopic ecosystem CO2 fluxes at a high temporal resolution. It also highlighted the importance of proper correction for cross-sensitivity with water vapour and CO2 concentration of the sample air to get accurate measurements of δ13C.
Nitrate Runoff Contributing from the Agriculturally Intensive San Joaquin River Watershed to Bay-Delta in California

Nitrogen loading from agricultural landscapes can trigger a cascade of detrimental effects on aquatic ecosystems. Recently, the spread of aquatic weed infestations (Eichhornia crassipes, Egeria densa, Ludwigia spp., and Onagraceae) in the Sacramento-San Joaquin Delta of northern California has raised concerns, and nitrogen loading from California’s intensive farming regions is considered as one of the major contributors. In this study, we employed the Soil and Water Assessment Tool (SWAT) to simulate nitrogen exports from the agriculturally intensive San Joaquin River watershed to the Delta. The alternate tile drainage routine in SWAT was tested against monitoring data in the tile-drained area of the watershed to examine the suitability of the new routine for a tile nitrate simulation. We found that the physically based Hooghoudt and Kirkham tile drain routine improved model performance in representing tile nitrate runoff, which contributed to 40% of the nitrate loading to the San Joaquin River. Calibration results show that the simulated riverine nitrate loads matched the observed data fairly well. According to model simulation, the San Joaquin River plays a critical role in exporting nitrogen to the Delta by exporting 3135 tons of nitrate-nitrogen annually, which has a strong ecological implication in supporting the growth of aquatic weeds, which has impeded water flow, impairs commercial navigation and recreational activities, and degrades water quality in Bay-Delta waterways. Since nitrate loadings contributed by upstream runoff are an important nutrient to facilitate weed development, our study results should be seen as a prerequisite to evaluate the potential growth impact of aquatic weeds and scientific evidence for area-wide weed control decisions.
Generative Adversarial Networks toward Representation Learning for Image Captions

Captions generated from a single image are possibly different from each others as for representations (e.g. attention points or sentence expressions). However, a vast amount of image captioning datasets in the world have few or no annotations of latent variables. Learning latent variables of captions with no supervision is an important from perspectives of scalability and interpretability of conditional image captioning models. In this research, we propose a deep generative model to learn and leverage latent variables of image captions. In experiments, we used the task of image classification with several MNIST images and ground truth labels as down-scaled setting of image captioning, and we show that our proposed model acquired latent variables which represent sub-groups of labels.
A Positional Timewarp Accelerator for Mobile Virtual Reality Devices

Author(s): Barnes, Russell | Advisor(s): Xie, Yuan | Abstract: Mobile virtual reality devices are becoming more common, and yet their performance is still too low to be considered ideal. Frame rate and latency are two of the most important areas that should improve in order to provide a high-quality virtual reality experience. Meanwhile, positional tracking is improving the immersive experience of new mobile virtual reality devices by allowing users to physically move about in space and see their corresponding view matched in the virtual world. Timewarping is a technique that can improve the perceived latency and frame rate of virtual reality systems, but the positional variant of timewarping has proven to be difficult to implement on mobile devices due to the performance demands. A depth-informed positional time warp cannot be fully parallelized due to the depth test required for each pixel or group of pixels.This thesis proposes a positional timewarp hardware accelerator for mobile devices. The accelerator accepts a rendered frame and depth image and produces an updated frame corresponding to the user’s head position and orientation. The accelerator is compatible with existing deferred rendering engines for minimal modification of the software structure. Its execution time is directly proportional to the image resolution and is agnostic of the scene complexity. The accelerator’s size can be adjusted to meet the latency requirement for a given image resolution. It can be integrated into a system-on-chip or fabricated as a separate chip.Three examples are designed and simulated to show the performance potential of this accelerator architecture. The designs provide latencies of 15.43 ms, 11.58 ms and 9.27 ms for frame rates of 64.7, 86.4 and 107.9 frames per second, respectively. Although the visual side-effects may be insufficiently few to completely disregard the GPU’s frame rate, the accelerator can still improve the end-to-end positional latency and is also capable of substituting the GPU in the case of dropped frames.
Standards-Based Grading Derived Data to Monitor Grading and Student Learning

Grading of student work is the primary practice for evaluating students’ learning and performance in a course. As such, the data generated from grading can be a powerful source of evidence for course-level decision-making by stakeholders. This paper demonstrates, through a specific large engineering course example, how standards-based grading (SBG) derived data can be used to monitor student learning and grading. Three criteria for using SBG data confidently and effectively for this purpose are established. First, the grading data have to be of high quality. Second, the grading data results need to accessible through simple visual representations. Third, there needs to be a clear path forward from grading data, to interpretation, to actions.