Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between the Romberg test and the Wii Fit basic balance test and cognition in athletes with concussion

Background: Approximately 30% of individuals with a sport-related concussion present with postural instability. Multiple clinical balance tests exist to diagnose postural instability; yet little is known about the potential relationship between these type of postural assessments and cognition post-concussion. Aim: The purpose of the current study was to assess the relationship between the Romberg test, the Wii Fit basic balance test (WBBT), and the composite scores on the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) test in a sample of athletes with concussions. Methods: Fifty five post-concussed athletes (40 males) completed the Romberg test (RT) (−/+), the WBBT, and ImPACT test. WBBT performance was operationalized as the number of successfully completed trials (of 5 trials of increasing difficulty) within 30 seconds. Pearson’s and point-biserial correlations examined univariate associations among the variables. Results: The RT and WBBT were not significantly related (r = − 0.029, p = 0.832). The RT weakly correlated with ImPACT impairment scores (r = 0.26, p = 0.041), whereas WBBT the number of trials did not (r = − 0.20, p = 0.155). The RT scores were significantly correlated with ImPACT Visual Processing Speed Score (r = 0.27, p = 0.036) and Reaction Time score (r = 0.34, p = 0.006). In contrast, WBBT trials were significantly correlated with the ImPACT Visual Memory Score (r = − 0.41, p = 0.003). Conclusions: These results suggest that the WBBT and RT assess unique aspects of postural control. The RT may relate directly to single sensory cognitive and motor processing, while the WBBT may relate to multi-sensory visually driven cognitive and motor processing. Relevance for patients: Clinical balance tests could point to different cognitive impairments post-concussion.
Closing the Training/Inference Gap for Deep Attractor Networks

This paper improves the deep attractor network (DANet) approach by closing its gap between training and inference. During training, DANet relies on attractors, which are computed from the ground truth separations. As this information is not available at inference time, the attractors have to be estimated, which is typically done by k-means. This results in two mismatches: The first mismatch stems from using classical k-means with Euclidean norm, whereas masks are computed during training using the dot product similarity. By using spherical k-means instead, we can show that we can already improve the performance of DANet. Furthermore, we show that we can fully incorporate k-means clustering into the DANet training. This yields the benefit of having no training/inference gap and consequently results in an scale-invariant signal-to-distortion ratio (SI-SDR) improvement of 1.1dB on the Wall Street Journal corpus (WSJ0).
OF RESEARCH IN DEVELOPMENT ADMINISTRATION IN ANGLOPHONE WEST AFRICA : AN EXAMINATION OF THE GHANAIAN AND NIGERIAN EXPERIENCES 1 By ‘

Development administration is now over half a century old and it has exhibited paradigmatic shifts and epistemological crises. Yet, while global research on development administration remains dominated by western thoughts and ideas owing to the western origins of the sub-discipline, this dominance continues to hamper scholarship on development administration amongst African intellectuals and academics whose works are dwarfed by their western counterparts. To this end, this paper argues that there is a need to encourage collaboration amongst western and southern researchers. To do this, the paper comparatively reviews the research of African intellectuals and academics along certain discernable patterns of convergence, divergence and/or persistence in the paradigmatic shifts and crises exemplified through western modernization models and African responses and reactions.
Failure Analysis and Quantification for Contemporary and Future Supercomputers

Large-scale computing systems today are assembled by numerous computing units for massive computational capability needed to solve problems at scale, which enables failures common events in supercomputing scenarios. Considering the demanding resilience requirements of supercomputers today, we present a quantitative study on fine-grained failure modeling for contemporary and future large-scale computing systems. We integrate various types of failures from different system hierarchical levels and system components, and summarize the overall system failure rates formally. Given that nowadays system-wise failure rate needs to be capped under a threshold value for reliability and cost-efficiency purposes, we quantitatively discuss different scenarios of system resilience, and analyze the impacts of resilience to different error types on the variation of system failure rates, and the correlation of hierarchical failure rates. Moreover, we formalize and showcase the resilience efficiency of failure-bounded supercomputers today.
Exploring the Suitability of BLE Beacons to Track Poacher Vehicles in Harsh Jungle Terrains

Our overall aim is focused on exploring whether we could use Bluetooth Low Energy (BLE) technology to track poacher vehicles in remote and rural areas such as Sabah, in Malaysia, especially deep inside the jungle terrain with little or no communication technologies exists. Tracking technologies are currently limited to relying on satellites or cellular-towers for environments that do not permit access to these signals, very few viable alternatives exist. This paper explores the use of BLE as a method to track vehicles. It works by mounting Bluetooth beacons beside a road and placing a receiver concealed somewhere inside the vehicle. As the vehicle drives past the beacon, the receiver and beacon are momentarily in range, the receiver then stores a unique ID from the beacon, and when the vehicle is then in an area with GSM signal, an SMS is sent containing the unique IDs of the beacons that have been detected. This project is prototyped and tested in collaboration with the Danau Girang Field Centre in Sabah, Malaysia. The results offer insights for how effective BLE beacons are in a tracking situation for where the beacon and receiver are in range for a short period of time as well as how different obstructions will affect the range and strength of the signal. It is important to note that our objective is not to catch the poacher, instead to understand how they move around within jungle terrain, as we can use such information to develop a comprehensive plan against poaching activities.
Cavity-assisted controlled phase-flip gates

Cavity-mediated two-qubit gates, for example between solid-state spins, are attractive for quantum network applications. We propose three schemes to implement a controlled phase-flip gate mediated by a cavity. The main advantage of all these schemes is the possibility to perform them using a cavity with high cooperativity, but not in the strong coupling regime. We calculate the fidelity of each scheme in detail, taking into account the most important realistic imperfections, and compare them to highlight the optimal conditions for each scheme. Using these results, we discuss which quantum system characteristics might favor one scheme over another.
Many-to-Many Voice Conversion Using Cycle-Consistent Variational Autoencoder with Multiple Decoders

One of the obstacles in many-to-many voice conversion is the requirement of the parallel training data, which contain pairs of utterances with the same linguistic content spoken by different speakers. Since collecting such parallel data is a highly expensive task, many works attempted to use non-parallel training data for many-to-many voice conversion. One of such approaches is using the variational autoencoder (VAE). Though it can handle many-to-many voice conversion without the parallel training, the VAE based voice conversion methods suffer from low sound qualities of the converted speech. One of the major reasons is because the VAE learns only the self-reconstruction path. The conversion path is not trained at all. In this paper, we propose a cycle consistency loss for VAE to explicitly learn the conversion path. In addition, we propose to use multiple decoders to further improve the sound qualities of the conventional VAE based voice conversion methods. The effectiveness of the proposed method is validated using objective and the subjective evaluations.
Spatial-Temporal Cluster Relations - A Foundation for Trajectory Cluster Lifetime Analysis

Spatial-temporal data, that is information about objects that exist at a particular location and time period, are rich in value and, as a consequence, the target of so many initiative efforts. Clustering approaches aim at grouping datapoints based on similar properties for classification tasks. These approaches have been widely used in domains such as human mobility, ecology, health and astronomy. However, clustering approaches typically address only the static nature of a cluster, and do not take into consideration its dynamic aspects. A desirable approach needs to investigate relations between dynamic clusters and their elements that can be used to derive new insights about what happened to the clusters during their lifetimes. A fundamental step towards this goal is to provide a formal definition of spatial-temporal cluster relations. This report introduces, describes, and formalizes 14 novel spatial-temporal cluster relations that may occur during the existence of a cluster and involve both trajectory-cluster membership conditions and cluster-cluster comparisons. We evaluate the proposed relations with a discussion on how they are able to interpret complex cases that are difficult to be distinguished without a formal relation specification. We conclude the report by summarizing our results and describing avenues for further research.
Common preperiodic points for quadratic polynomials

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ f_c(z) = z^2+c $\end{document}</tex-math></inline-formula> for <inline-formula><tex-math id="M2">\begin{document}$ c \in {\mathbb C} $\end{document}</tex-math></inline-formula>. We show there exists a uniform upper bound on the number of points in <inline-formula><tex-math id="M3">\begin{document}$ {\mathbb P}^1( {\mathbb C}) $\end{document}</tex-math></inline-formula> that can be preperiodic for both <inline-formula><tex-math id="M4">\begin{document}$ f_{c_1} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M5">\begin{document}$ f_{c_2} $\end{document}</tex-math></inline-formula>, for any pair <inline-formula><tex-math id="M6">\begin{document}$ c_1\not = c_2 $\end{document}</tex-math></inline-formula> in <inline-formula><tex-math id="M7">\begin{document}$ {\mathbb C} $\end{document}</tex-math></inline-formula>. The proof combines arithmetic ingredients with complex-analytic: we estimate an adelic energy pairing when the parameters lie in <inline-formula><tex-math id="M8">\begin{document}$ \overline{\mathbb{Q}} $\end{document}</tex-math></inline-formula>, building on the quantitative arithmetic equidistribution theorem of Favre and Rivera-Letelier, and we use distortion theorems in complex analysis to control the size of the intersection of distinct Julia sets. The proofs are effective, and we provide explicit constants for each of the results. </p>
Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.
The effects of territorial smoking restrictions on individual smoking behaviour in Australia

While smoking remains the leading preventable cause of death in Australia, existing policy options, except for bans on smoking at public places, seem to have limited scope for expansion in a tight regulatory environment. Eight new smoking bans introduced in six different Australian jurisdictions over 2003 and 2005, all but two of which involved public place bans only, provide a basis for evaluation. The analysis extends a popular two-part model of smoking behaviour by GLM and correlated random effect models which have been rarely employed in the previous literature. Difference-indifferences estimation using 4 waves of the Household, Income, Labour Dynamics Australia (HILDA) Survey indicates that the smoking bans affected neither the decision to smoke nor the decision of how much to smoke. The results are robust to alternative specifications and estimation methods. It appears appropriate to reconsider a view that smoking bans affect smoking behaviour through publicity on the smoke-related health risks which accompanies the introduction of a new regulation. This mechanism was absent during the period under this study. * A full copy of this paper can be accessed at http://www.ahes. org.au/Hong08%20paper.pdf.
Exploiting aggregate sparsity in second-order cone relaxations for quadratic constrained quadratic programming problems

Among many approaches to increase the computational efficiency of semidefinite programming (SDP) relaxation for nonconvex quadratic constrained quadratic programming problems (QCQPs), exploiting the aggregate sparsity of the data matrices in the SDP by Fukuda et al. [Exploiting sparsity in semidefinite programming via matrix completion I: General framework, SIAM J. Optim. 11(3) (2001), pp. 647–674] and second-order cone programming (SOCP) relaxation have been popular. In this paper, we exploit the aggregate sparsity of SOCP relaxation of nonconvex QCQPs. Specifically, we prove that exploiting the aggregate sparsity reduces the number of second-order cones in the SOCP relaxation, and that we can simplify the matrix completion procedure by Fukuda et al. in both primal and dual of the SOCP relaxation problem without losing the max-determinant property. For numerical experiments, nonconvex QCQPs from the lattice graph and pooling problem are tested as their SOCP relaxations provide the same optimal value as the SDP relaxations. We demonstrate that exploiting the aggregate sparsity improves the computational efficiency of the SOCP relaxation for the same objective value as the SDP relaxation, thus much larger problems can be handled by the proposed SOCP relaxation than the SDP relaxation.
Click Maximization in Online Social Networks Using Optimal Choice of Targeted Interests

Click-through rate (CTR) prediction of advertisements on online social network platforms to optimize advertising is of much interest. Prior works build machine learning models that take a user-centric approach in terms of training -- using predominantly user data to classify whether a user will click on an advertisement or not. While this approach has proven effective, it is inaccessible to most entities and relies heavily on user data. To accommodate for this, we first consider a large set of advertisement data on Facebook and use natural language processing (NLP) to extract key concepts that we call conceptual nodes. To predict the value of CTR for a combination of conceptual nodes, we use the advertisement data to train four machine learning (ML) models. We then cast the problem of finding the optimal combination of conceptual nodes as an optimization problem. Given a certain budget $k$, we are interested in finding the optimal combination of conceptual nodes that maximize the CTR. A discussion of the hardness and possible NP-hardness of the optimization problem is provided. Then, we propose a greedy algorithm and a genetic algorithm to find near-optimal combinations of conceptual nodes in polynomial time, with the genetic algorithm nearly matching the optimal solution. We observe that Decision Tree Regressor and Random Forest Regressor exhibit the highest Pearson correlation coefficients w.r. t. click predictions and real click values. Additionally, we find that the conceptual nodes of "politics", "celebrity", and "organization" are notably more influential than other considered conceptual nodes.
Investigating Ortega Hypothesis in Q&A portals: An Analysis of StackOverflow

Ortega Hypothesis considers masses, i.e., a large number of average people who are not specially qualified as being instrumental in any system's progress. This hypothesis has been reasonably examined in the scientific domain where it has been supported by a few works while refuted by many others, resulting in no clear consensus. While the hypothesis has only been explored in the scientific domain so far, it has hardly been examined in other fields. Given the large-scale collaboration facilitated by the modern Q&A portals where a crowd with a diverse skill-set contributes, an investigation of this hypothesis becomes necessary for informed policy-making. In this work, we investigate the research question inspired by Ortega Hypothesis in StackOverflow where we examine the contribution made by masses and check whether the system may continue to function well even in their absence. The results point towards the importance of masses in Q&A portals for the little but useful contribution that they provide. The insights obtained from the study may help in devising informed incentivization policies enabling better utilization of the potential of the users.
A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data

Predicting user churn and taking personalized measures to retain users is a set of common and effective practices for online game operators. However, different from the traditional user churn relevant researches that can involve demographic, economic, and behavioral data, most online games can only obtain logs of user behavior and have no access to users' latent feelings. There are mainly two challenges in this work: 1. The latent feelings, which cannot be directly observed in this work, need to be estimated and verified; 2. User churn needs to be predicted with only behavioral data. In this work, a Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which can get the users' latent feelings while predicting user churn. Besides, we proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn Prediction) to help models predict user churn with only behavioral data. The latent feelings are names as satisfaction and aspiration in this work. We designed experiments on a real dataset and the results show that our methods outperform baselines and are more suitable for long-term sequential learning. The latent feelings learned are fully discussed and proven meaningful.
Energy-resolved neutron imaging with high spatial resolution using a superconducting delay-line kinetic inductance detector

Neutron imaging is one of the key technologies for non-destructive transmission testing. Recent progress in the development of intensive neutron sources allows us to perform energy-resolved neutron imaging with high spatial resolution. Substantial efforts have been devoted to developing a high spatial and temporal resolution neutron imager. We have been developing a neutron imager aiming at conducting high spatial and temporal resolution imaging based on a delay-line neutron detector, called the current-biased kinetic-inductance detector, with a conversion layer 10B. The detector allowed us to obtain a neutron transmission image with four signal readout lines. Herein, we expanded the sensor active area, and improved the spatial resolution of the detector. We examined the capability of high spatial resolution neutron imaging over the sensor active area of 15 × 15 mm2 for various samples, including biological and metal ones. We also demonstrated an energy-resolved neutron image in which stainless-steel specimens were discriminating of other specimens with the aid of the Bragg edge transmission.
Thermal weakening of cracks and brittle-ductile transition of matter: A phase model

We present a model for the thermally activated propagation of cracks in elastic matrices. The propagation is considered as a subcritical phenomenon, the kinetics of which being described by an Arrhenius law. In this law, we take the thermal evolution of the crack front into account, assuming that a portion of the released mechanical energy is transformed into heat in a plastic process zone. We show that such a model leads to a two-phase crack propagation: a first phase at low velocity in which the temperature elevation is of little effect and the propagation is mainly governed by the mechanical load and by the toughness of the medium, and a second phase in which the crack is thermally weakened and propagates at greater velocity. Such a dual behavior can potentially explain the usual stick-slip in brittle fracturing, and we illustrate how with numerical simulations of mode I cracks propagating in thin disordered media. In addition, we predict the existence of a limiting ambient temperature above which the weakened phase ceases to exist and we propose this critical phenomenon as a novel explanation for the brittle-ductile transition of solids.
AN ANALYSIS OF THE TECHNOLOGICAL STRUCTURE OF REFINERIES AND BLENDERS : BIOFUEL RESERVATION PRICES

The Leontief multiproduct flexible cost function attempts to approximate the technology used by refineries and blenders. In general, this cost function satisfies the rational behavior restrictions imposed by economic theory. The estimated marginal costs are incorporated in a monopolistic competition model to calculate the virtual prices of the other products provided by the refineries and blenders under the hypothetical situation in which the reformulated gasoline is absent in the fuel markets. I have found that conventional gasoline and other product prices are greater than those of the mentioned hypothetical case. This result reflects the fact that consumers are being charged with high prices in order to have available a fuel which satisfies the environmental Protection Agency (EPA) regulations. Finally, when all the products become perfect substitutes, . , . e i the consumers are not interested in the environmental quality of the fuels, the price differences tend to be negligibly small.