Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmic redshift in the nonexpanding cellular universe

A review of the traditional possible causes of cosmic redshift —namely Doppler, expanding vacuum, gravitational, and tired light— is presented along with a discussion of why they failed. A new cosmic redshift mechanism is constructed based on a non-mass, non-energy, space medium (which serves as the luminiferous substrate) and the DSSU cellular cosmology (a remarkably natural problem-free cosmology). The cosmic redshift is shown to be a velocity-differential effect caused by a flow differential of the space medium. Furthermore, the velocity-differential redshift/effect is shown to be part of a much broader unification, since the very mechanism that causes the gravitation effect and sustains the Universe’s gravity-cell structure is also the mechanism that causes the λ elongation manifesting as the cosmic redshift. Agreement with the verifiable portion of the redshift-distance graph (z ≤ 5) is outstanding. The main point is that intrinsic spectral shift occurs with a transit across/through any gravity well (sink). It is caused by the difference in propagation velocity between the axial ends of the photon or wave packet. Which, in turn, is caused by the difference in velocity of the aether flow, the flow differential of the aether, that occurs throughout a gravity well. And here the causal chain is linked to gravity: the change in velocity of the aether flow is what produces the effect of gravitation. The acceleration of the aether flow is the manifestation of gravity.
Empirical impacts of macroeconomic risk factors on tanker shipping equities

From a historical point of view tanker freight rates have been volatile. The tanker shipping segment is the second largest segment within shipping and considered to be the segment with the highest volatility. Shipowners will experience periods abundant of “milk and honey” followed periods where they have to live out of “bread and water”. Shipping is a high risk business characterized by stronger impacts from macroeconomic forces and maritime economic theory explains this market behavior by imbalances between supply and demand. This thesis sets out to model how tanker equity values are affected by this supply and demand relationship. Earlier research by Grammenous & Arkoulis (2002) and Mitter & Jensen (2006) have been conducted with different approaches and results. As a dependent variable, we have created a portfolio of 18 tanker shipping companies. The portfolio returns were recalculated monthly and weighted by company specific market capitalization divided by total portfolio market capitalization. To describe the dependent variable, different macroeconomic risk factors are considered as independent variables. The variables that are chosen for our model is based on risk factors depicted in maritime economic literature and macroeconomic literature. To investigate for how independent variables affect the dependent variable a multifactor model approach was applied. The variables that proved to have a significant effect on the dependent variable was: World portfolio stock return, Oil price, exchange rate USDEUR, VLCC fleet size and selected lags of the series. The tanker portfolio was proven to have a significant positive relationship with the development of the WTI oil price, VLCC spot rates, tanker fleet size, the MSCI world index and exchange rate of USD. Further the analysis could not detect any significant relationship related to the industrial production in OECD and US or petroleum inventories in the US. The final model ended up with an explanatory power of 39.7% which means that a large part of the changes in the tanker stock portfolio remain unexplained. The results of this thesis are for some relationships in line with earlier studies on risk factors and shipping stock returns, however some relationships differ from earlier studies.
Evaluation of Criteria for Selected Islands using Fuzzy Analytic Hierarchy Process (FAHP)

Tourism is one of the most become an extremely popular, global activity. In 2009, there were over 23.6 million international tourist arrivals to Malaysia. As a service industry, tourism has numerous tangible and intangible elements. Major tangible elements include transportation, accommodation, and other components of a hospitality industry. Major intangible elements relate to the purpose or motivation for becoming a tourist, such as rest, relaxation, the opportunity to meet new people and experience other cultures, or simply to do something different and have an adventure. The terms tourism and travel are sometimes used interchangeably. In this context travel has a similar definition to tourism, but implies a more purposeful journey. The terms tourism and tourist are sometimes used pejoratively to imply a shallow interest in the cultures or islands visited by tourists. This paper presents fuzzy MCDM approach for social attributes of islands evaluation. In order to accomplish the best criteria of social attributes performance, Fuzzy Analytic Hierarchy Process (FAHP) is used for weight and alternative evaluation. Fuzzy environment is used for dealing with the problem rating and ranking of social attributes. There are different criteria in characterizing real world for evaluating islands. This paper described the implementation of FAHP that influences ten specific social attributes of islands. The simulation process shows the results that find the two most important aspects for tourist in order to choose the destination of islands. Key-Words: Fuzzy Analytic Hierarchy Process (FAHP), Island Tourism, Multi-Criteria Decision Making
ASTROPHYSICAL IMPLICATIONS OF THE BINARY BLACK HOLE MERGER GW150914

The discovery of the gravitational-wave (GW) source GW150914 with the Advanced LIGO detectors provides the first observational evidence for the existence of binary black hole (BH) systems that inspiral and merge within the age of the universe. Such BH mergers have been predicted in two main types of formation models, involving isolated binaries in galactic fields or dynamical interactions in young and old dense stellar environments. The measured masses robustly demonstrate that relatively “heavy” BHs ( ≳ 25 ? > M ⊙ ? > ) can form in nature. This discovery implies relatively weak massive-star winds and thus the formation of GW150914 in an environment with a metallicity lower than about 1/2 of the solar value. The rate of binary-BH (BBH) mergers inferred from the observation of GW150914 is consistent with the higher end of rate predictions ( ≳ 1 ? > Gpc−3 yr−1) from both types of formation models. The low measured redshift ( z ≃ 0.1 ? > ) of GW150914 and the low inferred metallicity of the stellar progenitor imply either BBH formation in a low-mass galaxy in the local universe and a prompt merger, or formation at high redshift with a time delay between formation and merger of several Gyr. This discovery motivates further studies of binary-BH formation astrophysics. It also has implications for future detections and studies by Advanced LIGO and Advanced Virgo, and GW detectors in space.
Unknown-Aware Deep Neural Network

An important property of image classification systems in the real world is that they both accurately classify objects from target classes (“knowns”) and safely reject unknown objects (“unknowns”) that belong to classes not present in the training data. Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence. As a result, simply using low-confidence detections as a way to detect unknowns does not work well. In this work, we propose an Unknownaware Deep Neural Network (UDN for short) to solve this challenging problem. The key idea of UDN is to enhance existing CNNs to support a product operation that models the product relationship among the features produced by convolutional layers. This way, missing a single key feature of a target class will greatly reduce the probability of assigning an object to this class. UDN uses a learned ensemble of these product operations, which allows it to balance the contradictory requirements of accurately classifying known objects and correctly rejecting unknowns. To further improve the performance of UDN at detecting unknowns, we propose an information-theoretic regularization strategy that incorporates the objective of rejecting unknowns into the learning process of UDN. We experiment on benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN, adding unknowns by injecting one dataset into another. Our results demonstrate that UDN significantly outperforms state-of-the-art methods at rejecting unknowns by 25 percentage points improvement in accuracy, while still preserving the classification accuracy.
Orthogonal Approximation of Marginal Likelihood of Generative Models

This paper presents a new approximation of the marginal likelihood of generative models which is used as a score for anomaly detection. The score is motivated by the shortcoming of the popular reconstruction error that it can behave arbitrarily outside the known samples. The proposed score corrects this by orthogonal combination of the reconstruction error and the likelihood in the latent space. As experimentally shown on benchmark problems from anomaly detection and illustrated on a toy problem, this combination lends the score robustness to outliers. Generative models evaluated with this score outperformed the competing methods especially in tasks of learning distribution from data corrupted by anomalies. Finally, the score is compatible with contemporary generative models, namely variational auto-encoders and generative adversarial networks.
Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models

We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudoposterior. Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be analyzed to provide loss guarantees on the predictions with the pseudo-posterior. Our criterion can be used to derive new sparse Gaussian process algorithms that have error guarantees applicable to various likelihoods.
The Risks and Opportunities Associated with Weak Arithmatic Skills of Accounting Students1

This paper explored the authors concerns about students enrolled in their introductory accounting course. Anecdotal evidence suggested that students struggle with basic arithmetic concepts that underlie basic business transactions even though their math placement and ACT scores are high. A survey of 125 students in a first accounting course was conducted in the spring of 2010 to assess the basic arithmetical skills. The results indicated that the ACT scores and math placement tests do not reveal weakness in basic arithmetic. We find that faculty and students will experience frustration due to the impaired arithmetic ability. By taking for granted that students possess basic skills in arithmetic faculty will exclude exercises from the curriculum that will build the kind of arithmetic abilities students need to think on their feet about basic business transactions. We conclude by arguing that making curricular accommodations to cope with the deficit in arithmetic is not in the student’s best interest. A competitive advantage for students can be created by addressing the deficit head on rather than adjusting the curriculum to work around the problem. 1 The authors wish to thank Skip Burhans for his contributions to an earlier version of this paper. The authors are also grateful for the feedback provided by member of the Bradley University CBER forum (DATE) and the Mid-West Regional AAA Conference (DATE).
University of Groningen Competing-Risk Analysis of Death and End Stage Kidney Disease by Hyperkalaemia Status in Non-Dialysis Chronic Kidney Disease Patients Receiving Stable Nephrology Care Provenzano,

Hyperkalaemia burden in non-dialysis chronic kidney disease (CKD) under nephrology care is undefined. We prospectively followed 2443 patients with two visits (referral and control with 12-month interval) in 46 nephrology clinics. Patients were stratified in four categories of hyperkalaemia (serum potassium, sK ≥ 5.0 mEq/L) by sK at visit 1 and 2: Absent (no-no), Resolving (yes-no), New Onset (no-yes), Persistent (yes-yes). We assessed competing risks of end stage kidney disease (ESKD) and death after visit 2. Age was 65 ± 15 years, eGFR 35 ± 17 mL/min/1.73 m2, proteinuria 0.40 (0.14–1.21) g/24 h. In the two visits sK was 4.8 ± 0.6 and levels ≥6 mEq/L were observed in 4%. Hyperkalaemia was absent in 46%, resolving 17%, new onset 15% and persistent 22%. Renin-angiotensin-system inhibitors (RASI) were prescribed in 79% patients. During 3.6-year follow-up, 567 patients reached ESKD and 349 died. Multivariable competing risk analysis (sub-hazard ratio-sHR, 95% Confidence Interval-CI) evidenced that new onset (sHR 1.34, 95% CI 1.05–1.72) and persistent (sHR 1.27, 95% CI 1.02–1.58) hyperkalaemia predicted higher ESKD risk versus absent, independently from main determinants of outcome including eGFR change. Conversely, no effect on mortality was observed. Results were confirmed by testing sK as continuous variable. Therefore, in CKD under nephrology care, mild-to-moderate hyperkalaemia status is common (37%) and predicts per se higher ESKD risk but not mortality.
Compositional Languages Emerge in a Neural Iterated Learning Model

The principle of compositionality, which enables natural language to represent complex concepts via a structured combination of simpler ones, allows us to convey an open-ended set of messages using a limited vocabulary. If compositionality is indeed a natural property of language, we may expect it to appear in communication protocols that are created by neural agents via grounded language learning. Inspired by the iterated learning framework, which simulates the process of language evolution, we propose an effective neural iterated learning algorithm that, when applied to interacting neural agents, facilitates the emergence of a more structured type of language. Indeed, these languages provide specific advantages to neural agents during training, which translates as a larger posterior probability, which is then incrementally amplified via the iterated learning procedure. Our experiments confirm our analysis, and also demonstrate that the emerged languages largely improve the generalization of the neural agent communication.
CHANGES IN THE CONTENT OF BETALAIN PIGMENTS AND THEIR ANTIOXIDATIVE CAPACITY DURING STORAGE

Background. The red beet is an important source of natural colourants and antioxidants in the diet, such as betalain pigments (betacyanins and betaxanthins). Betalain pigments are commonly used in the food industry for colouring food products. Material and methods. The authors of this study analysed changes in the content of betalain pigments and their antioxidative capacity during the 12-month storage of a betalain preparation (pH 7.0) made from red beet (Beta vulgaris L.) in a laboratory. Additionally, the HPLC technique was used to isolate individual betalain pigments from the preparation and to assess their antioxidative activity. Results. During the storage (12 months at 20°C) of the dry preparation (pH 7.0) red pigments decomposed almost completely. The loss of their content amounted to 94%, and their antioxidative capacity decreased only by 31%. The content of yellow pigments, which was measured spectrophotometrically using Nilsson’s method, increased over two times. Neobetanin (5-O-β-D-glucopyranosyl neobetanidin) was identified in the mass spectrum of the betalain preparation. Neobetanin is a degradation product of betacyanins. The antioxidative activity of neobetanin was found to be about 2.5 times greater than the antioxidative activity of betanin, which is the most abundant pigment in the red beet. The antioxidative activity of betanidin was also greater (1.9 times per 1 mg) than the antioxidative activity of betanin. Moreover, the antioxidative activity of isobetanin was slightly greater than that of betanin. After 24-hour storage in light and at room temperature neobetanin in an aqueous solution was found to be almost completely degraded. Conclusions. The storage of the dry betalain preparation (pH 7.0) proved that betanin transformed into yellow-orange neobetanin, which exhibited very high antioxidative activity, but, due to the low stability of the pigment during storage, it cannot be used for food colouring.
Sequential Learning for Dirichlet Process Mixtures

Dirichlet process mixture model provides a flexible nonparametric framework for unsupervised learning. Monte Carlo based sampling methods always involve heavy computation efforts; conventional variational inference requires careful design of the variational distribution and the conditional expectation. In this work, we treat the DP mixture itself as the variational proposal, and view the given data as drawn samples of the unknown target distribution. We propose an evidence upper bound (EUBO) to act as the surrogate loss, and fit a DP mixture to the given data by minimizing the EUBO, which is equivalent to minimizing the KL-divergence between the target distribution and the DP mixture. We provide three advantages of the EUBO based DP mixture fitting and show how to build the black-box style sequential learning algorithm. We use the stochastic gradient descent (SGD) algorithm for optimization that leverages on the automatic differentiation tools. Simulation studies are provided to demonstrate the efficiency of our proposed methods.
A Flexible Annotation-Based Architecture for Intelligent Language Tutoring Systems

This thesis presents a general architecture for Intelligent Language Tutoring Systems. The architecture makes use of annotation-based processing to encode different linguistic information at deep and shallow levels in a flexible manner and associate it with the learner input string. At the same time, it leaves the original input untouched, which is different from traditional pipeline approaches used in other Intelligent Computer-Assisted Language Learning systems. We show that this is necessary in order for feedback to be both error-specific and useful to the learner. Furthermore, the architecture incorporates a general mechanism for the use of activity models in guiding processing and feedback. The approach uses error types as a means of formulating pedagogical goals of individual activities. These error types are then mapped to analysis requirements that can be used to adapt processing of the learner input to the activity. More importantly, error types are used to influence the feedback strategy towards the errors that a particular activity centers on. We also demonstrate a similar use of error counts in the learner model to guide feedback. We have evaluated the system on the basis of example scenarios. They show that we are indeed able to give more useful feedback than the baseline system TAGARELA (Amaral, 2007) in a number of cases. This is the result of both annotation-based processing and integration of activity and learner model into the feedback process.
Certifying Neural Network Audio Classifiers

We present the first end-to-end verifier of audio classifiers. Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM). The audio processing is verified using novel convex relaxations tailored to feature extraction operations used in audio (e.g., Fast Fourier Transform) while recurrent architectures are certified via a novel binary relaxation for the recurrent unit update. We show the verifier scales to large networks while computing significantly tighter bounds than existing methods for common audio classification benchmarks: on the challenging Google Speech Commands dataset we certify 95% more inputs than the interval approximation (only prior scalable method), for a perturbation of -90dB.
Aerodynamic Focusing of an Abrasive Air Jet

In order to decrease the size of the eroded footprint resulting from abrasive jet machining, the aerodynamic focusing of an abrasive particle jet was investigated. For an abrasive jet from a slot nozzle, the best focusing employed a pair of bounding air jets tilted 60° to the abrasive jet, and impinging it approximately 10% of the standoff distance from the workpiece. An optimum bounding jet pressure existed to achieve the maximum focusing, resulting in a machined channel width reduction of up to 16 % with a corresponding depth increase of 84%. A novel apparatus was constructed to generate an annular bounding jet impinging a central abrasive jet formed from a round nozzle. However, aerodynamic focusing in this case could not be achieved due to the large stagnation zone that formed near where the annular jet impinged. Computational fluid dynamics simulations were used to understand the mechanics of particle focusing.
A game theory model based on Gale-Shapley for dual-resource constrained (DRC) flexible job shop scheduling

Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. “when” and “where” rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a ‘couple’ made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to “when” and “where” rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.
OUT-OF-DISTRIBUTION DETECTION USING DEEP NEURAL NETWORKS

Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect outof-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes. Based on the Outlier Exposure (OE) technique, we propose a novel loss function that achieves state-of-the-art results in out-of-distribution detection with OE both on image and text classification tasks. Additionally, we experimentally show that the combination of our method with the Mahalanobis distance-based classifier achieves state-of-the-art results in the OOD detection task.