Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Menu Type and Task Complexity on Information RetrievalPerformance

Success in finding information in a website depends not only on the complexity of the information retrieval task per se but also on the way information is made accessible. We explored the role of navigation menus when performing information retrieval tasks. We compared expandable menus with sequential menus. Expandable menus preserve the full context of choice while the user is browsing. Sequential menus provide only partial context. Task complexity was manipulated along two dimensions: path length and path relevance. Path length leading to the target information was varied using different levels of depth. Path relevance - based on semantic similarity - is the relevance of the optimal navigation path to the search task. In general, results show benefits of presenting expandable menus giving fully contextual information when retrieval tasks are highly complex. More specifically, with expandable menus, highly complex retrieval tasks with high path length and low path relevance are more efficiently performed than with sequential menus. Furthermore, users are also less disoriented with expandable menus. A practical implication is that for complex websites path relevance and expandable navigation menus are important usability considerations.
New Geometric Formalism for Gravity Equation in Empty Space

In this paper, a complex daor field which can be regarded as the square root of space-time metric is proposed to represent gravity. The locally complexified geometry is set up, and the complex spin connection constructs a bridge between gravity and SU(1,3) gauge field. Daor field equations in empty space are acquired, which are one-order differential equations and not conflict with Einstein's gravity theory.
The consequences of traumatic events on resilience among South Korean police officers

Purpose Drawing on survey data from South Korean police officers, the purpose of this paper is to explore the mediation effects of coping self-efficacy and social support in the relationship between the impact of traumatic events and resilience. Additionally, the moderation effects were assessed to examine how this relationship varies by the state of police officers’ mental health. Design/methodology/approach Both multiple mediation and multiple group models within the structural equation modeling framework were adopted to assess the proposed mediating and moderating effects. Findings Traumatic events affect resilience not just directly but also indirectly through mediating factors such as coping self-efficacy and social support. These patterns are more pronounced in the high-risk mental health group than in the normal group. Practical implications If exposure to traumatic events were an unavoidable aspect of police work, helping officers build resilience as a general capacity to overcome stressful situations would be an effective strategy to prevent many negative consequences associated with the traumatic events. This study examined specific causal mechanisms linking the impact of traumatic events to resilience to better understand the process of developing resilience among police officers. Originality/value Instead of further examining the relationship between impact of traumatic events and other physical and mental outcomes, this study attempted to expand the current literature by identifying important mediating and moderating mechanisms that reduce the negative influences of traumatic events on resilience.
A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer.
Literacy and education in Ireland

Recent media attention has focused on the low ranking of Ireland in a major international study on literacy. In this paper we examine the dataset used in these studies to consider the reason behind the low ranking. We find significant evidence that the underlying reason for this is the low level of formal schooling of older individuals, possibly due to the lack of free secondary schooling. Moreover we find that formal schooling in Ireland has a bigger effect on literacy outcomes than in either Northern Ireland or Great Britain.
A linear matrix inequality approach to system stabilization over constrained channels

In this paper we address the stabilization problem of a networked control system with signal-to-noise ratios (SNRs) constrained channels. The minimal SNRs required for stabilizability are obtained by a novel linear matrix inequality approach. Similar results are provided under circumstances of a constant delay and random packet loss. The effectiveness of the proposed approach is demonstrated with some numerical examples.
Music as a tool for environmental education and advocacy: artistic perspectives from musicians of the Playlist for the Planet

Abstract Environmental education is a key tool in humanity’s efforts to address environmental issues. The arts can help provide some of the affective components of environmental education – emotions, values, and motivations driving pro-environmental behavior. As one of the arts, music can captivate, entertain, and create a sense of community. Using non-probabilistic purposive sampling, we interviewed a cohort of environmentally aware musicians with the goal of understanding their mental constructions around the role of music in environmental education and advocacy. A constant comparative coding method was used to code the interviews. The analysis revealed four artistic and five quality dimensions that the participants considered when sharing their pro-environmental values through their music. The four artistic dimensions emerged as continua representing ranges of choice regarding how a musician might create and deliver an environment-related song. The five quality dimensions emerged as recommendations for effectiveness of messaging through art that is perceived as authentic. Video abstract Read the transcript Watch the video on Vimeo
Utilizing the Bezier descriptors for hand gesture recognition

In this paper, a novel approach is proposed for hand gesture recognition by modelling the Bezier curves. We have adapted a three-step approach which begins with the skin-based hand segmentation method using the normal Gaussian distribution. It is followed by the feature extraction module where the hand centroid points are computed which are then fitted with Bezier curves. These fitted Bezier curve points are quantized and concatenated to build the Bezier descriptors. The extracted Bezier descriptors are finally classified by Hidden Markov Models (HMM) using Left-Right Banded (LRB) topology for hand gesture recognition. We have tested our proposed approach with different HMM models on the same hand centroid points (i.e., control points) fitted with Bezier curves and compare results. The experimental results show that our proposed approach is capable to detect hands, model the Bezier curves to build descriptors and classify the descriptors for hand gesture recognition in real situations which proves its applicability in the domain of Human Computer Interaction.
The Plant-Derived Bauhinia bauhinioides Kallikrein Proteinase Inhibitor (rBbKI) Attenuates Elastase-Induced Emphysema in Mice

Background. Elastase mediates important oxidative actions during the development of chronic obstructive pulmonary disease (COPD). However, few resources for the inhibition of elastase have been investigated. Our study evaluated the ability of the recombinant plant derived Bauhinia bauhinioides Kallikrein proteinase Inhibitor (rBbKI) to modulate elastase-induced pulmonary inflammation. Methods. C57Bl/6 mice were given intratracheal elastase (ELA group) or saline (SAL group) and were treated intraperitoneally with rBbKI (ELA-rBbKI and SAL-rBbKI groups). At day 28, the following analyses were performed: (I) lung mechanics, (II) exhaled nitric oxide (ENO), (III) bronchoalveolar lavage fluid (BALF), and (IV) lung immunohistochemical staining. Results. In addition to decreasing mechanical alterations and alveolar septum disruption, rBbKI reduced the number of cells in the BALF and decreased the cellular expression of TNF-α, MMP-9, MMP-12, TIMP-1, eNOS, and iNOS in airways and alveolar walls compared with the ELA group. rBbKI decreased the volume proportion of 8-iso-PGF2α, collagen, and elastic fibers in the airways and alveolar walls compared with the ELA group. A reduction in the number of MUC-5-positive cells in the airway walls was also observed. Conclusion. rBbKI reduced elastase-induced pulmonary inflammation and extracellular matrix remodeling. rBbKI may be a potential pharmacological tool for COPD treatment.
Fuzzy Functional Dependencies in Relational Databases

A gas-insulated circuit breaker is described which includes a Y-shaped hollow grounded metal tank formed from three appropriately shaped pipes joined together. A hollow insulating bushing is secured to each upstanding leg. A stationary and a movable contact, separable from each other to establish an arc therebetween, are disposed in one of the upstanding legs, with an insulating gas disposed within both the tank and the insulating bushings. A mechanism is used for directing a blast of insulating gas into the arc established between the separating contact, and includes a piston fixedly disposed in the one leg and a puffer cylinder secured to the movable contact and movable over the piston to compress the gas therebetween. An operating mechanism is associated with the base leg for moving the movable contact, and a drive rod insulatably connects the movable contact and the operating mechanism. The operating mechanism thus moves the movable contact through the insulating drive rod.
PPCheck: A Webserver for the Quantitative Analysis of Protein–Protein Interfaces and Prediction of Residue Hotspots

Background Modeling protein-protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein-protein interfaces, which contribute more in stabilizing a protein-protein interface. Results We had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning. Conclusions PPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein-protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.
Which model of biological plausibility for language? The case of “What Darwin Got Wrong”

Ursini, Francesco-Alessio(2013), “Which Model of Biological Plausibility for Language? : The Case of ``What Darwin Got Wrong`` ”, Language & Information Society 19. The goal of this paper is to discuss some of the conceptual consequences of the arguments put forward in What Darwin Got Wrong, for a broader theory of the biolinguistic approach. The book offers arguments against “New Synthesis” approaches to Evolutionary Theory, that are particularly germane to biolinguistic matters. One main contention is that only approaches to evolutionary facts that capture the “laws of form” observed across living organisms can be theoretically and empirically adequate. However, the book does not investigate whether this contention applies to linguistic matters as well. This issue is addressed in the paper, and it is argued that organism-internal properties, which can be captured via the formal notion of “conservativity”, must be found in language as well. Therefore, it is argued that only those lin-guistic theories that capture these properties, be they about syntactic, semantic or acquisition matters alike can be considered as biolinguistically plausible.
Can GARCH Models Capture the Long-Range Dependence in Financial Market Volatility ? ∗

This paper investigates if component GARCH models introduced by Engle and Lee (1999) and Ding and Granger (1996) can capture the long-range dependence observed in measures of time-series volatility. Long-range dependence is assessed through the sample autocorrelations, two popular semiparametric estimators of the long-memory parameter, and the parametric fractionally integrated GARCH (FIGARCH) model. Monte Carlo methods are used to characterize the finite sample distributions of these statistics when data are generated from GARCH(1,1), component GARCH and FIGARCH models. For several daily financial return series we find that a two-component GARCH model captures the shape of the autocorrelation function of volatility, and is consistent with long-memory based on semiparametric and parametric estimates. Therefore, GARCH models can in some circumstances account for the long-range dependence found in financial market volatility. JEL classification: C22,C52
Scientific Drilling and Related Research in the Samail Ophiolite, Sultanate of Oman

Summary This workshop report describes plans for scientific drill ing in the Samail ophiolite in Oman in the context of past, current, and future research. Long-standing plans to study formation and evolution of the Samail crust and upper mantle, involving igneous and metamorphic processes at an oceanic spreading center, have been augmented by recent interest in ongoing, low temperature processes. These include alteration and weathering, and the associated sub-surface biosphere supported by chemical potential energy due to disequilibrium between mantle peridotite and water near the surface. This interest is motivated in part by the possibility of geological carbon capture and storage via engineered, accelerated mineral carbonation in Oman.
Safety of Intraoperative Transcranial Electrical Stimulation Motor Evoked Potential Monitoring

Summary This article reviews intraoperative transcranial electrical stimulation (TES) motor evoked potential (MEP) monitoring safety based on comparison with other clinical and experimental brain stimulation methods and clinical experience in more than 15,000 cases. Comparative analysis indicates that brain damage and kindling are highly unlikely. There have been remarkably few adverse events. Pulse train TES-induced or coincidental seizures (n = 5) are rare, probably because of very brief (<0.03 second) stimuli, anesthesia, and the general absence of predisposing cerebral conditions. Soft bite blocks may prevent tongue or lip laceration (n = 29) or mandibular fracture (n = 1). Rare cardiac arrhythmia (n = 5) and intraoperative awareness (n = 1) may be coincidental. Minor scalp burns (n = 2) are rare. Although possible, no spinal epidural recording electrode complications or injuries resulting from TES-induced movement were found. There have been no recognized adverse neuropsychological effects, headaches, or endocrine disturbances. Comprehensive relative contraindications include epilepsy, cortical lesions, convexity skull defects, raised intracranial pressure, cardiac disease, proconvulsant medications or anesthetics, intracranial electrodes, vascular clips or shunts, and cardiac pacemakers or other implanted biomedical devices. Otherwise unexplained intraoperative seizures and possibly arrhythmias are indications to abort TES. With appropriate precautions in expert hands, the well-established benefits of TES MEP monitoring decidedly outweigh the associated risks.
Theory Reenement through Knowledge-based Feature Set Selection

Classiication systems depend upon having the best (i.e., most relevant) set of input features from which a classiication decision can then be made. This is true both for the classiiers themselves, and for the inductive learners that might be used to build them. This paper describes indigent (Improving kNown Domains with Genetically Engineered feaTures). indigent utilizes genetic search to perform knowledge-based feature selection. It assumes that it will be provided with a knowlege-based neural network. It then performs a genetic search for better network topologies, focusing entirely on the incorporation (or deletion) of input features. indigent can be viewed in one of two ways: (1) as a theory reenement system, or (2) as a feature selection system, guided by expert-provided domain knowledge. It can be used either alone or in conjunction with more extensive theory reenement systems.
Composite learning control with application to inverted pendulums

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC strategy has been verified by the application to an inverted pendulum control problem.
Earliest Starting and Finishing time Duplication-based algorithm

Optimal task scheduling of a direct acyclic graph (DAG) onto distributed environments is a NP-hard problem. In this paper, we propose a new scheduling algorithm called Earliest Starting and Finishing time Duplication based (ESFD). ESFD has three phases: priority processing, task scheduling and task duplication. ESFD considers all parameters related to the task and its immediate predecessors to assigning a task. After each schedule ESFD will be updated its first phase, so ESFD is a dynamic algorithm. Experimental results on random graphs and real application graphs show that the NSL and speedups generated by the ESFD are better than those generated by the well-known HCPT, HEFT and LHCNF algorithms.
A cascade image transform for speaker independent automatic speechreading

We propose a three-stage pixel based visual front end for automatic speechreading (lipreading) that results in improved recognition performance of spoken words or phonemes. The proposed algorithm is a cascade of three transforms applied to a three-dimensional video region of interest that contains the speaker's mouth area. The first stage is a typical image compression transform that achieves a high "energy", reduced-dimensionality representation of the video data. The second stage is a linear discriminant analysis based data projection, which is applied to a concatenation of a small number of consecutive image transformed video data. The third stage is a data rotation by means of a maximum likelihood linear transform. Such a transform optimizes the likelihood of the observed data under the assumption of their class conditional Gaussian distribution with diagonal covariance. We apply the algorithm to visual-only 52-class phonetic and 27-class visemic classification on a 162-subject, 7-hour long, large vocabulary, continuous speech audio-visual dataset. We demonstrate significant classification accuracy gains by each added stage of the proposed algorithm, which, when combined, can reach up to 27% improvement. Overall, we achieve a 49% (38%) visual-only frame level phonetic classification accuracy with (without) use of test set phone boundaries. In addition, we report improved audio-visual phonetic classification over the use of a single-stage image transform visual front end.
Efficient FPGA Resynthesis Using Precomputed LUT Structures

The ability to efficiently match logic functions to structures of K-input look-up tables (K-LUTs) is a central problem in FPGA resynthesis algorithms. This paper addresses the problem of matching logic functions of ~ 9 to 12 inputs to K-LUT structures. Our method is based on the off-line generation of libraries of LUT structures. During resynthesis, matching is accomplished efficiently using NPN encoding and hash table look-ups. Generating an effective library of LUT structures may seem prohibitive due to the overwhelming number of logic functions which must be considered and represented in the library. We show that, by careful consideration of which logic functions and LUT structures to keep, it is possible to generate useful, compact libraries. We present numerical results demonstrating the effectiveness of our ideas when used during area-oriented resynthesis after FPGA technology mapping.