Miracle-dz/UniFAR-FTUs huggingface dataset:
Standing Waves : the Equilateral Triangle

It is well known that symmetry considerations can often be a powerful tool for simplifying physical systems. We consider a wave which satisfies the Helmholtz equation in a region with some given symmetry, and note that any solution can be transformed by that symmetry to give another solution. Using this simple observation we describe a number of necessary conditions for wave solutions. For bounded regions, explicit solutions to the Helmholtz solutions are only known in a few cases (square, circle, and some special triangles). In this paper, we investigate the special domain of an equilateral triangle and describe what the solutions must look like based purely on symmetry considerations. Our analysis classifies all solutions based on their symmetry properties in a way that can be extended to any regular polygon. If one were to impose Dirichlet Boundary Conditions, then additional relationships within and between symmetry classes emerge. We then describe a collection of solutions which can be used to construct all of the solutions to the Dirichlet problem in the equilateral triangle.
On Disease and Deontology: Multiple Tests of the Influence of Disease Threat on Moral Vigilance

Threat has been linked to certain facets of moral cognition, but the specific implications of disease threat for moral judgment remain poorly understood. Across three studies, we investigated the role of perceived disease threat in shaping moral judgment and hypothesized that perceived disease threat would cause people to be more sensitive to moral violations (or more “morally vigilant”). All three studies found a positive relationship between dispositional worry about disease transmission and moral vigilance. Additional analyses suggested that this worry was more strongly related to vigilance toward binding moral foundations. Study 3 demonstrated that moral vigilance was higher in individuals for whom the threat of disease was experimentally made salient, relative to individuals in both a neutral and a nondisease threat condition. Taken together, these results suggest that perceived disease threat may influence people’s moral vigilance across moral domains.
CLUSTERING AND MAKING DECISIONS METHODS FOR INTELLIGENT SYSTEMS

The processes involved in the classification of objects and word problems continue being a complex and ill-structured problem. In this paper, a software tool for Hypermedia Intelligent Tutoring Systems is proposed. This tool helps us to define the complexity levels for a set of proposed problems to students. In this way, the intelligent systems designers and professors can use it to organize the problem base. On the other hand, this tool constitutes an important element during training and learning process because it permits to organize the transit from simple to complex in problem solving. It uses different methods of classification, like as statistical methods, experts' proofs and others. The evaluations of parameters can involve objective and subjective elements. In this relation, a particular version of Delphi Method is developed. The system was implemented on Delphi 4.0 for Windows.
A Majorization-Minimization approach to total variation reconstruction of super-resolved images

Super-resolution is the task of reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. It can be formulated as an inverse problem for which regularization is necessary. In this paper we adopt this formulation and use the Total-Variation criterion for regularization. Then, we employ the Majorization-Minimization (MM) methodology to reconstuct high-resolution images from low-resolution observations. Experimental results are shown, which demonstrate the advantages of the proposed algorithm compared to other methods.
Computerized automation of wavelet based denoising method to reduce speckle noise in OCT images

Optical coherence tomography (OCT) is an optical imaging technique that allows the visualization of living tissues in vivo. However, the speckle noise affects their diagnostic accuracy by degrading the quality of OCT images. Wavelet based denoising methods have been successful in reducing the speckle noise in past but they require a manual selection of few parameters for optimum denoising. We propose a computerized method to select these parameters by using an evolutionary technique called differential evolution (DE). The selection process is purely objective and does not require any assumption of parameters as opposed to the existing wavelet based denoising methods. The performance improvement by using our method has been quantified in terms of signal to noise ratio (SNR) improvement and resolution preservation. An improvement of more than 17% in signal to noise ratio(SNR) was achieved while the resolution was restored after the application of our method.
Increased Brain Lactate Concentrations Without Increased Lactate Oxidation During Hypoglycemia in Type 1 Diabetic Individuals

Previous studies have reported that brain metabolism of acetate is increased more than twofold during hypoglycemia in type 1 diabetic (T1D) subjects with hypoglycemia unawareness. These data support the hypothesis that upregulation of blood-brain barrier monocarboxylic acid (MCA) transport may contribute to the maintenance of brain energetics during hypoglycemia in subjects with hypoglycemia unawareness. Plasma lactate concentrations are ∼10-fold higher than acetate concentrations, making lactate the most likely alternative MCA as brain fuel. We therefore examined transport of [3-13C]lactate across the blood-brain barrier and its metabolism in the brains of T1D patients and nondiabetic control subjects during a hypoglycemic clamp using 13C magnetic resonance spectroscopy. Brain lactate concentrations were more than fivefold higher (P < 0.05) during hypoglycemia in the T1D subjects compared with the control subjects. Surprisingly, we observed no increase in the oxidation of blood-borne lactate in the T1D subjects, as reflected by similar 13C fractional enrichments in brain glutamate and glutamine. Taken together, these data suggest that in addition to increased MCA transport at the blood-brain barrier, there may be additional metabolic adaptations that contribute to hypoglycemia unawareness in patients with T1D.
Comments on and implications of a steady-state in coastal marine ecosystems

Coastal ecosystems can be thought of as being established by a number of physico-geochemical drivers, e.g. geochemistry and bathymetry of the basins, climate, tidal and freshwater flows, natural and anthropogenic inputs of nutrients and toxins, all of which exert an influence on the resulting communities of organisms. Depending on the interactions among the major drivers, ecosystems may occur on both large and small scales and be basin-wide or within basins. For individual and separate ecosystems to exist with some permanence in time, e.g. reach a steady-state, they also have to be ‘defended’. Defences are mechanisms that counter changes to maintain the status quo. We argue, and present evidence to support the notion, that the defence mechanisms are inextricably tied to primary production and the biogeochemical cycling of organic matter and provide buffers that mitigate potentially adverse impacts by trace toxins. Colloid pumping, production of complexing ligands and sulfide formation are some of the mechanisms that control trace substances. Current methods for assessing ecosystems do not address the issue of steady-state, nor do they take account of defence activities, e.g. buffering. Therefore, they cannot assess the ‘robustness’ of ecosystems or their ability to resist change, for good or bad. Also, defence mechanisms may, for a time, mask future potentially serious impacts, suggesting that monitoring efforts with limited budgets should consider the measurement of the inputs into ecosystems as well as the immediate or short-term result of the inputs.
Graph-Based Semi-supervised Learning with Adaptive Similarity Estimation

Graph-based semi-supervised learning algorithms have attracted a lot of attention. Constructing a good graph is playing an essential role for all these algorithms. Many existing graph construction methods(e.g. Gaussian Kernel etc.) require user input parameter, which is hard to configure manually. In this paper, we propose a parameter-free similarity measure Adaptive Similarity Estimation (ASE), which constructs the graph by adaptively optimizing linear combination of its neighbors. Experimental results show the effectiveness of our proposed method.
A novel BIST approach for testing logic resources using Hard Macro

This paper explores an new Built-In Self-Test (BIST) approach to test the configurable logic blocks (CLBs) of Xilinx Virtex FPGAs using Hard Macro. The proposed approach completely detects and diagnoses single and multiple stuck-at gate-level faults as well as associated signal lines in the CLBs, while significantly reducing the Outputs needed to be compared by ORAs. Only 24 total test configurations for two sessions are required while retrieving the BIST results using scan chain method.
Collaborative Training of Tensors for Compositional Distributional Semantics

Type-based compositional distributional semantic models present an interesting line of research into functional representations of linguistic meaning. One of the drawbacks of such models, however, is the lack of training data required to train each word-type combination. In this paper we address this by introducing training methods that share parameters between similar words. We show that these methods enable zero-shot learning for words that have no training data at all, as well as enabling construction of high-quality tensors from very few training examples per word.
A profile-based framework for interaction prediction

In this paper, we generalize the link prediction problem to an interaction prediction problem. Compared with links in social networks, interactions can occur several times repeatedly. Based on the observation, we formulate an event-triggered interaction prediction problem. For example, we may want to know when a user connects to a website (e.g. Facebook), who will also connect to the website. We propose a Profile-based Interaction Prediction Framework (PIPF) which can solve the event-triggered interaction prediction problem efficiently and effectively. In PIPF, we first transform the interaction log into a Sliding-window Evolving Graph (SEG) to reduce the data volume and incrementally update SEG as interaction log grows. Then, we build profiles designed to present users' behavior by extracting the static and surprising features from SEG. The static (respectively, surprising) feature reflects the regularity of users' behavior (respectively, the temporal behavior). When an event occurs, we compute the similarity between the event and each candidate link. We propose two similarity functions for static and surprising features and an automatic selection strategy to control the influence of the two features. We use a real dataset that records Internet connections to evaluate the scalability, efficiency, and effectiveness of PIPF. The experimental results show that PIPF is far more scalable and efficient than the previous methods to perform real-time prediction.
Sensibility testbed: An internet-wide cloud platform for programmable exploration of mobile devices

Smartphones contain sensors that provide information about the user of the device. Studying smartphones, therefore, provides an invaluable window into the behavioral patterns of users. In this work, we describe an open infrastructure that provides research communities with principled access to mobile devices and their sensors. Our platform, the Sensibility Testbed, is a free, community-driven platform for mobile devices. It provides secure data access to user-owned mobile devices while preserving user privacy. The unified programmable interface to sensors on heterogeneous devices in the Sensibility Testbed enables research scientists to design and deploy experiments that run across large numbers of devices, for example to measure the coverage and performance of cellular and WiFi networks. The Sensibility Testbed makes the sensing capabilities of smartphones more accessible to a broad range of researchers.
Balancing autonomy and user control in context-aware systems - a survey

Application autonomy can reduce interactions with users, ease the use of the system, and decrease user distraction. On the other hand, users may feel loss of control over their applications. A further problem is that autonomous applications may not always behave in the way desired by the user. To mitigate these problems, autonomous context-aware systems must provide mechanisms to strike a suitable balance between user control and software autonomy. In this paper, we present a survey of research on balancing user control and system autonomy in context-aware systems. We address various issues that are related to the control-autonomy trade-off, including issues in context modelling, programming models and tools, and user interface design
B̄→D*ℓν̄ form factor at zero recoil from three-flavor lattice QCD: A model independent determination of |Vcb|

We present the first lattice QCD calculation of the form factor for B → D*lv with three flavors of sea quarks. We use an improved staggered action for the light valence and sea quarks (the MILC configurations), and the Fermilab action for the heavy quarks. The form factor is computed at zero recoil using a new double ratio method that yields the form factor more directly than the previous Fermilab method. Other improvements over the previous calculation include the use of much lighter light-quark masses, and the use of lattice (staggered) chiral perturbation theory in order to control the light-quark discretization errors and chiral extrapolation. We obtain for the form factor, F B→D * (1) = 0.921(13)(20), where the first error is statistical and the second is the sum of all systematic errors in quadrature. Applying a 0.7% electromagnetic correction and taking the latest PDG average for F B→D * (1)|V cb | leads to |V rb | = (38.7 ± 0.9 exp ± 1.0 theo ) X 10 -3 .
Using the domain identification model to study major and career decision-making processes

ABSTRACT The purpose of this study was to examine the extent to which (1) a domain identification model could be used to predict students’ engineering major and career intentions and (2) the MUSIC Model of Motivation components could be used to predict domain identification. The data for this study were collected from first-year engineering students. We used a structural equation model to test the hypothesised relationship between variables in the partial domain identification model. The findings suggested that engineering identification significantly predicted engineering major intentions and career intentions and had the highest effect on those two variables compared to other motivational constructs. Furthermore, results suggested that success, interest, and caring are plausible contributors to students’ engineering identification. Overall, there is strong evidence that the domain identification model can be used as a lens to study career decision-making processes in engineering, and potentially, in other fields as well.
Variational Beltrami flows over manifolds

In this paper, we study, in this paper, the problem of denoising images/data which are defined are nonflat surfaces. This problem arises often in many medical imaging tasks. The Beltrami flow which was defined in an explicit-intrinsic manner is generalized here to nonflat surfaces and is defined in an implicit way. We formulate the flow in a variational way which is generalized to a scalar field defined over an n-dimensional manifold. The implementation scheme of this flow is presented and various experimental results obtained on a set of real images illustrate the performances of the approach as well as the differences between various flows of interests.
Content-based music classification

The music classification techniques can be discriminated into two categories — based by music content-based classification and training by learning machine classification. Both have their advantages and disadvantages. For music content-based classifications, most of the approaches are based on single music feature, such as melody or chord, and the accuracy is up to 70% in few genres of music. However, the accuracy for classification of most music genres is lower. In this paper, we study the features of music content and use the multiple features of music data to improve the accuracy of music classification. Our performance studies shown that the higher accuracy can be achieved for classification of classical music by using multiple features of music content for classification.
Mechanisms underlying the impacts of exotic plant invasions

Although the impacts of exotic plant invasions on community structure and ecosystem processes are well appreciated, the pathways or mechanisms that underlie these impacts are poorly understood. Better exploration of these processes is essential to understanding why exotic plants impact only certain systems, and why only some invaders have large impacts. Here, we review over 150 studies to evaluate the mechanisms underlying the impacts of exotic plant invasions on plant and animal community structure, nutrient cycling, hydrology and fire regimes. We find that, while numerous studies have examined the impacts of invasions on plant diversity and composition, less than 5% test whether these effects arise through competition, allelopathy, alteration of ecosystem variables or other processes. Nonetheless, competition was often hypothesized, and nearly all studies competing native and alien plants against each other found strong competitive effects of exotic species. In contrast to studies of the impacts on plant community structure and higher trophic levels, research examining impacts on nitrogen cycling, hydrology and fire regimes is generally highly mechanistic, often motivated by specific invader traits. We encourage future studies that link impacts on community structure to ecosystem processes, and relate the controls over invasibility to the controls over impact.
Towards Practical Avoidance of Information Leakage in Enterprise Networks

Preventing exfiltration of sensitive data is a central challenge facing many modern networking environments. In this paper, we propose a network-wide method of confining and controlling the flow of sensitive data within a network. Our approach is based on black-box differencing - we run two logical copies of the network, one with private data scrubbed, and compare outputs of the two to determine if and when private data is being leaked. To ensure outputs of the two copies match, we build upon recent advances that enable computing systems to execute deterministically at scale and with low overheads. We believe our approach could be a useful building block towards building general-purpose schemes that leverage black-box differencing to mitigate leakage of private data.
From Mouth to Model: Combining in vivo and in vitro Oral Biofilm Growth

Background: Oral biofilm studies based on simplified experimental setups are difficult to interpret. Models are limited mostly by the number of bacterial species observed and the insufficiency of artificial media. Few studies have attempted to overcome these limitations and to cultivate native oral biofilm. Aims: This study aimed to grow oral biofilm in vivo before transfer to a biofilm reactor for ex situ incubation. The in vitro survival of this oral biofilm and the changes in bacterial composition over time were observed. Methods: Six human enamel-dentin slabs embedded buccally in dental splints were used as biofilm carriers. Fitted individually to the upper jaw of 25 non-smoking male volunteers, the splints were worn continuously for 48 h. During this time, tooth-brushing and alcohol-consumption were not permitted. The biofilm was then transferred on slabs into a biofilm reactor and incubated there for 48 h while being nourished in BHI medium. Live/dead staining and confocal laser scanning microscopy were used to observe bacterial survival over four points in time: directly after removal (T0) and after 1 (T1), 24 (T2), and 48 h (T3) of incubation. Bacterial diversity at T0 and T3 was compared with 454-pyrosequencing. Fluorescence in situ hybridization (FISH) was performed to show specific taxa. Survival curves were calculated with a specially designed MATLAB script. Acacia and QIIME 1.9. 1 were used to process pyrosequencing data. SPSS 21.0 and R 3.3. 1 were used for statistical analysis. Results: After initial fluctuations at T1, survival curves mostly showed approximation of the bacterial numbers to the initial level at T3. Pyrosequencing analysis resulted in 117 OTUs common to all samples. The genera Streptococcus and Veillonella (both Firmicutes) dominated at T0 and T3. They make up two thirds of the biofilm. Genera with lower relative abundance had grown significantly at T3. FISH analysis confirmed the pyrosequencing results, i.e., the predominant staining of Firmicutes. Conclusion: We demonstrate the in vitro survival of native primary oral biofilm in its natural complexity over 48 h. Our results offer a baseline for cultivation studies of native oral biofilms in (phyto-) pharmacological and dental materials research. Further investigations and validation of culturing conditions could also facilitate the study of biofilm-induced diseases.
Topic-Sensitive Tag Ranking

Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. In this paper, we propose a topic-sensitive tag ranking (TSTR) approach to rate the tags on the web. We employ a generative probabilistic model to associate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relationships and perform a topic-level random walk over the graph to suggest a ranking score for each tag at different topics. Experimental results validate the effectiveness of the proposed tag ranking approach.
Five additions to the list of Sepsidae Diptera for Vietnam: Perochaeta cuirassa sp. n., Perochaeta lobo sp. n., Sepsis spura sp. n., Sepsis sepsi Ozerov, 2003 and Sepsis monostigma Thompson, 1869

Abstract A recent collecting trip to Vietnam yielded three new species and two new records of Sepsidae (Diptera) for the country. Here we describe two new species in the species-poor genus Perochaeta (Perochaeta cuirassa sp. n. andPerochaeta lobo sp. n.) and one to the largest sepsid genus Sepsis (Sepsis spura sp. n.) which is also found in Sumatra and Sulawesi. Two additional Sepsis species are new records for Vietnam (Sepsis sepsi Ozerov, 2003; Sepsis monostigma Thompson, 1869). We conclude with a discussion of the distribution of Perochaeta and the three Sepsis species.
High-Risk Violent Prisoners’ Patterns of Change on Parole on the DRAOR’s Dynamic Risk and Protective Factors

Few studies have examined change after treatment completion; such investigations can enhance our understanding of how a rehabilitative intervention leads to reduced recidivism. The current study uses growth curve modeling to investigate change in dynamic risk factors in the community, following two samples of high-risk violent men for up to 12 months on parole. While in prison, one sample had completed intensive psychological treatment, whereas the other completed no programs or briefer programs. As expected, based on Dynamic Risk Assessment for Offender Re-Entry (DRAOR) scores, intensive program completers entered the community with higher protective and lower stable and acute dynamic factors, and showed less variability on acute risk factors, as did all of those with better initial scores. But the two samples improved at a similar rate over the course of parole. Those with initially poorer scores changed less on parole except for protective factors; those with stronger initial protective scores showed less change.