Miracle-dz/UniFAR-FTUs huggingface dataset:
Underspecification of the Empirical Return-Factor Model and a Factor Analytic Augmentation as a Solution to Factor Omission

This empirical paper comprehensively sets out the impact of underspecification on a key foundational concept in empirical finance, the linear factor model. It places emphasis on the extensive consequences of factor omission for model estimation and interpretation. Factor omission in time-series models that relate asset returns to pre-specified factor sets is a common problem. A proposed standard and widely-used solution is the inclusion of a residual market factor which is assumed to be a catch-all proxy for omitted factors. This study shows that a specification that incorporates a set of carefully selected macroeconomic factors will be underspecified. The inclusion of residual market factors will alleviate but not eliminate the consequences of underspecification. Although the early use of factor analytically derived factor scores in factor models has been criticized, augmenting a model comprising pre-specified factors with statistical factors derived from the residuals results in an accurately specified model for which the diagonality assumption holds. Consequently, this paper shows that a factor analytic augmentation is an effective and readily implementable solution to the factor omission problem.
AN EXPLORATORY STUDY OF THE FACTORS ASSOCIATEDWITH SUCCESSFUL REUSE OF OTHERS KNOWLEDGE

The goal of this exploratory research study is to understand factors that affect the likelihood of successfully reusing someone elses knowledge vs. inventing ones own solution. We examine knowledge reuse by scientists and engineers in conjunction with their work processes in order to learn how knowledge is truly reused in the context of the work required. Our findings indicate that there are eight factors that affect the likelihood of successfully reusing others knowledge. We also we suggest knowledge management strategies and knowledge management systems tools that may be helpful in this endeavor. Based on our findings we propose a conceptual model for efficiently finding solutions to problems for which one does not have a ready answer.
Sliding observer-based demagnetisation fault-tolerant control in permanent magnet synchronous motors

This study proposes a fault-tolerant control method for permanent magnet synchronous motors (PMSMs) based on the active flux linkage concept, which addresses permanent magnet (PM) demagnetisation faults in PMSMs. First, a mathematical model for a PMSM is established based on active flux linkage, and then the effect of PM demagnetisation on the PMSM is analysed. Second, the stator current in the static coordinate is set as the state variable, an observer is designed based on a sliding-mode variable structure, and an equation for active flux linkage is established for dynamic estimation based on the equivalent control principle of sliding-mode variable structure. Finally, the active flux linkage for the next moment is predicted according to the operating conditions of the motor and the observed values of the current active flux linkage. The deadbeat control strategy is applied to eliminate errors in the active flux linkage and realise the objective of fault-tolerant control. A timely and effective control for demagnetisation faults is achieved using the proposed method, which validity and feasibility are verified by the simulation and experiment results.
A New Dual Channel Based Medium Access Control Protocol for Ad Hoc Networks

Ad hoc networks are widely researched and applied on various fields. To implement Ad Hoc Networks, the IEEE 802.11 standard Medium Access Control(MAC) protocol are widely applied. However, there are some problems which decrease the transmission efficiency of wireless networks at the standard MAC layer. The standard use 4-way handshake procedure(RTS/CTS/DATA/ACK) to overcome the MAC layer problems. But some problems cannot be solved if only a single channel is used in MAC Layer. Therefore, many multi-channel based MAC protocols were proposed to prevent MAC layer problems. But, those scheme either cannot solve MAC layer problems perfectly. This paper proposes New Dual Channel based Medium Access Control(NDCMAC) protocol for ad hoc networks. This protocol requests just one additional channel and perfectly solves most of MAC layer related problems. For this scheme, busy tone and Negative ACK(NACK) concepts are applied. Using NDCMAC, the channel utilization is significantly increased. Result of the simulation show how NDCMAC is better than the standard. Keywords—ad hoc network, medium accecc control, MAC, busy tone, dual channel
The politics of quantitative easing and housing stimulus by the Federal Reserve and European Central Bank, 2008‒2018

Abstract Central banks have been important yet underexplored actors in the fight against the Great Recession. In addition to ultra-low interest rates, they adopted large-scale bond-buying programmes known as quantitative easing (QE). Yet there is significant variation in QE programmes with important distributive consequences. Why has the Fed adopted multi-trillion-dollar bond-buying programmes in housing, while the ECB has not? This article argues that the Fed targeted the integrated housing finance market as a monetary transmission strategy to stimulate core elements of the US growth model: credit, demand, and consumption. In contrast, the ECB hardly stimulated housing given the eurozone’s fragmented housing finance markets and the macroeconomic frictions with some eurozone growth models, particularly export-oriented Germany. Analysing archival and interview data, this article traces the decisions of these central banks since the Great Recession, contributing to scholarship on the politics of central banking, economic policy in hard times, and the welfare state.
The effectiveness of the matching principle in different financial reporting systems and its impact on the quality of earnings

In the last decade, there has been a renewed interest in the fundamentals of accounting, highlighting a considerable downward trend in the effectiveness of the matching process. Therefore, this study analyses how changes to the financial reporting system (revenue/expense vs. asset/liability) affect the degree of matching and assesses the relationship between the latter and the quality of accounting numbers. Focusing on private firms in the Italian institutional settings, this paper highlights how the switch from a revenue/expense model (as proxied by the Italian GAAP) to an asset/liability approach (as proxied by the IAS/IFRS) has clearly worsened the level of matching between current revenue and expenses. Moreover, this study analyses if quality of the accounting numbers is systematically influenced by the degree of matching effectiveness through a direct correlation and highlights that the degree of matching is positively related to the predictability and persistence of earnings, while having a negative correlation with earnings volatility. This stresses the positive impact of such basic reporting processes on the quality of accounting numbers. These findings are particularly relevant for regulators, standard setters and academics, since they provide further insights for the debate on the accounting harmonisation process and represent an additional call for further research into this topic.
Mentors' perceptions of the effectiveness of the components and technological venues implemented in online teacher induction programs for novice teachers

Mentors’ Perceptions of the Effectiveness of the Components and Technological Venues Implemented in Online Teacher Induction Programs for Novice Teachers. (December 2006) Kimberly K. Livengood, B.S., Texas Tech University; M.S., Texas A&M University-Corpus Christi Chair of Advisory Committee: Dr. Norvella P. Carter This descriptive study provided a synthesis of the form, structure, activity and relationship components utilized by online teacher induction programs designed to support novice teachers. Mentors involved in online teacher induction programs responded to an online questionnaire. The perceptions of 51 mentors involved in 36 online teacher induction programs in 16 states and one country in addition to the United States were examined to determine the effectiveness of components incorporated. A synthesis of the technological venues utilized was provided through a statistical analysis of the mentors’ responses. The mentors’ perceptions of the effectiveness of incorporated technological venues were statistically examined. Additionally, their perceptions of professional development, emotional support, reflection, and formative observation components in relation to the program’s characteristics were also examined through the analysis of their responses to the online questionnaire.
Robust control of redundant manipulators with constraints using a general reduced order model

Redundant manipulators can achieve additional objectives in conjunction with the main task of tracking the desired end-effector trajectory. When these additional objectives are expressed in the form of motion constraints, control algorithms can be developed to satisfy both the main and additional tasks simultaneously without explicit computation of the inverse kinematics. The paper considers the problem of controller design for redundant robots in the presence of uncertainties in the dynamic model. It is proposed that a simple and effective solution to this problem can be obtained by: first using a reduction procedure to obtain a lower dimensional system which retains the structure property of the original system; second based on the theory of guaranteed stability of uncertain systems, controlling the reduced system in such a way that the complete system is driven to the goal configuration.
Comparison of constant coefficient multipliers for CSD and Booth recoding

In this paper, the designs of constant coefficient multipliers (KCM) with canonical signed-digit (CSD) and Booth recoding are presented and compared. It is shown that the designs with CSD recoding have fewer additions than those with Booth recoding. But in some cases, with pre-calculated partial products, the number of additions in the latter designs can be reduced significantly. Pre-calculation can also be used in CSD KCM design. ASIC designers should make the choice according to the constant multiplier.
Richer and Deeper Supervision Network for Salient Object Detection

Recent Salient Object Detection (SOD) systems are mostly based on Convolutional Neural Networks (CNNs). Specifically, Deeply Supervised Saliency (DSS) system has shown it is very useful to add short connections to the network and supervising on the side output. In this work, we propose a new SOD system which aims at designing a more efficient and effective way to pass back global information. Richer and Deeper Supervision (RDS) is applied to better combine features from each side output without demanding much extra computational space. Meanwhile, the backbone network used for SOD is normally pre-trained on the object classification dataset, ImageNet. But the pre-trained model has been trained on cropped images in order to only focus on distinguishing features within the region of the object. But the ignored background information is also significant in the task of SOD. We try to solve this problem by introducing the training data designed for object detection. A coarse global information is learned based on an entire image with its bounding box before training on the SOD dataset. The large-scale of object images can slightly improve the performance of SOD. Our experiment shows the proposed RDS network achieves the state-of-the-art results on five public SOD datasets.
Improving Noise Robustness of Automatic Speech Recognition via Parallel Data and Teacher-student Learning

For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for transferring data. We incorporate up to 8000 hours of untranscribed data for training and present our results on sequence trained models apart from cross entropy trained ones. The best sequence trained student model yields relative word error rate (WER) reductions of approximately 10.1%, 28.7% and 19.6% on our clean, simulated noisy and real test sets respectively comparing to a sequence trained teacher.
Large-Scale Markov Decision Problems via the Linear Programming Dual

We consider the problem of controlling a fully specified Markov decision process (MDP), also known as the planning problem, when the state space is very large and calculating the optimal policy is intractable. Instead, we pursue the more modest goal of optimizing over some small family of policies. Specifically, we show that the family of policies associated with a low-dimensional approximation of occupancy measures yields a tractable optimization. Moreover, we propose an efficient algorithm, scaling with the size of the subspace but not the state space, that is able to find a policy with low excess loss relative to the best policy in this class. To the best of our knowledge, such results did not exist in the literature previously. We bound excess loss in the average cost and discounted cost cases, which are treated separately. Preliminary experiments show the effectiveness of the proposed algorithms in a queueing application.
Interpretable CNNs

—This paper proposes a generic method to learn interpretable convolutional ﬁlters in a deep convolutional neural network (CNN), where each interpretable ﬁlter encodes features of a speciﬁc object part. Our method does not require additional annotations of object parts or textures for supervision. Instead, we use the same training data as traditional CNNs. Our method automatically assigns each interpretable ﬁlter in a high conv-layer with an object part of a certain category during the learning process. Such explicit knowledge representations in conv-layers of CNN help people clarify the logic encoded in the CNN, i
Tree Tensor Networks for Generative Modeling

Matrix product states (MPSs), a tensor network designed for one-dimensional quantum systems, were recently proposed for generative modeling of natural data (such as images) in terms of the ``Born machine. '' However, the exponential decay of correlation in MPSs restricts its representation power heavily for modeling complex data such as natural images. In this work, we push forward the effort of applying tensor networks to machine learning by employing the tree tensor network (TTN), which exhibits balanced performance in expressibility and efficient training and sampling. We design the tree tensor network to utilize the two-dimensional prior of the natural images and develop sweeping learning and sampling algorithms which can be efficiently implemented utilizing graphical processing units. We apply our model to random binary patterns and the binary MNIST data sets of handwritten digits. We show that the TTN is superior to MPSs for generative modeling in keeping the correlation of pixels in natural images, as well as giving better log-likelihood scores in standard data sets of handwritten digits. We also compare its performance with state-of-the-art generative models such as variational autoencoders, restricted Boltzmann machines, and PixelCNN. Finally, we discuss the future development of tensor network states in machine learning problems.
Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation

Simulators that generate observations based on theoretical models can be important tools for development, prediction, and assessment of signal processing algorithms. In order to design these simulators, painstaking effort is required to construct mathematical models according to their application. Complex models are sometimes necessary to represent a variety of real phenomena. In contrast, obtaining synthetic observations from generative models developed from real observations often require much less effort. This paper proposes a generative model based on adversarial learning. Given that observations are typically signals composed of a linear combination of sinusoidal waves and random noises, sinusoidal wave generating networks are first designed based on an adversarial network. Audio waveform generation can then be performed using the proposed network. Several approaches to designing the objective function of the proposed network using adversarial learning are investigated experimentally. In addition, amphibian sound classification is performed using a convolutional neural network trained with real and synthetic sounds. Both qualitative and quantitative results show that the proposed generative model makes realistic signals and is very helpful for data augmentation and data analysis.
Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents

We describe our entry for the Systematic Review Information Extraction track of the 2018 Text Analysis Conference. Our solution is an end-to-end, deep learning, sequence tagging model based on the BI-LSTM-CRF architecture. However, we use interleaved, alternating LSTM layers with highway connections instead of the more traditional approach, where last hidden states of both directions are concatenated to create an input to the next layer. We also make extensive use of pre-trained word embeddings, namely GloVe and ELMo. Thanks to a number of regularization techniques, we were able to achieve relatively large capacity of the model (31.3M+ of trainable parameters) for the size of training set (100 documents, less than 200K tokens). The system's official score was 60.9% (micro-F1) and it ranked first for the Task 1. Additionally, after rectifying an obvious mistake in the submission format, the system scored 67.35%.
Planning under Uncertainty for Coordinating Infrastructural Maintenance (abstract)

Scheduling of infrastructural maintenance poses a complex multi-agent problem. Commonly a central authority is responsible for the quality and throughput of the infrastructure, while the actual maintenance is performed by multiple self-interested contractors. Not only does the central authority have to (economically) incentivise agents to consider quality and throughput, it is also burdened with the coordination of agents’ activities on the network with contingent activity durations. We introduce a coordination method that combines planning under uncertainty and dynamic mechanism design to coordinate agents on a network level. We apply this method on maintenance planning scenarios obtained through accurate modelling of the problem domain. To the best of our knowledge, this is the first application of dynamic mechanism design on a real-world problem. Finally, we validate the feasibility of our method through experimental evaluation and identify current open challenges for both the planning and scheduling as well as the mechanism design communities.
The Impact of Regularization on Convolutional Neural Networks

Deep learning has become the most popular class of machine learning family in recent times. Convolutional neural networks is one of the most popular deep learning architecture for solving many complicated and sophisticated problems like image classification, image recognition, and image detection. However, deep learning techniques faces overfitting problems, which is a hindrance to the model performance. Since convolutional neural networks are outperforming in the field of computer vision, so the need for new regularization techniques to reduce overfitting issues in convolutional neural networks is inevitable. This thesis work provides a peek into the recently developed regularization methods particularly for convolutional neural networks and generally for other deep learning techniques. This thesis also showcases the comparison of most commonly used regularization methods (dropout, batch normalization, kernel regularization) by training convolutional neural networks for image classification on two image datasets (CIFAR-10 and Kaggle‘s Cat vs Dog). Each model is cross validated by 10fold cross validation. Empirical results confirms that dropout is a strong regularization technique as compared to the other two methods( batch normalization and L1 and L2 regularization) on both datasets.
Designing A Musical Game of Shogi

This paper will discuss the design and implementation of the Shogi board, an interface built to allow members of the general public to interact with various Japanese instruments in a novel way. The Shogi board brings elements of Japanese culture to the attention of the general public in England ahead of the season of culture commencing in 2019. This interface utilizes maker technology to create an accessible method for members of the general public to compose music with traditional instruments in public settings.
Efficient Space Communication and Management (SCOaM) Using Cognitive Radio Networks Based on Deep Learning Techniques

The future of space communications has evolved towards being cognitive in order to improve energy and spectrum efficiency. Nowadays, near-Earth space satellites for weather, civil defense, and public commercial sectors are rapidly increasing, thereby resulting in congestion. Cognitive digital radio is a form of dynamic wireless communication in which a transceiver can intelligently detect the parts of communication channels that are currently not in use and instantly move into vacant channels while avoiding occupied ones. A challenge for communication between satellites and ground terminals involves calibrating both the time and frequency channels during rapid relative movement. A more dynamic and highly-precise algorithm for enhancing communication between satellites and base station terminals such as deep learning in cognitive radios is proposed that enables significant degree of automation in the space communication networks where spectrum interference is a key issue.
Homofoondominantie veroorzaakt dt-fouten tijdens het spellen en maakt er ons blind voor tijdens het lezen

Fouten tegen de spelling van de werkwoordvormen zijn hardnekkig. Leerlingen en studenten slagen er niet in om die fouten systematisch te vermijden. Zelfs professionele taalgebruikers, zoals journalisten of verantwoordelijken voor ondertitels, ontsnappen er niet aan. Geen enkele andere spelfout doet de gemoederen zo hoog oplaaien en zorgt voor zoveel polarisatie in de standpunten als deze fouten. Meestal leiden ze tot onbegrip en veroordelende uitspraken, waarbij de termen ‘lui’, ‘nonchalant’ en ‘onverstandig’ in de mond worden genomen. In hun artikel laten Nina Verhaert en Dominiek Sandra zien dat er meer aan de hand is dan nonchalance en gebrekkig taalinzicht: sommige dt-fouten ontstaan door de (normale) werking van onze cognitieve processen. Daardoor kan iedereen in bepaalde omstandigheden dt-fouten maken.