Minimal Features Research Articles

MEnDiGa: A Minimal Engine for Digital Games

Game engines generate high dependence of developed games on provided implementation resources. Feature modeling is a technique that captures commonalities and variabilities results of domain analysis to provide a basis for automated configuration of concrete products. This paper presents the Minimal Engine for Digital Games (MEnDiGa), a simplified collection of game assets based on game features capable of building small and casual games regardless of their implementation resources. It presents minimal features in a representative hierarchy of spatial and game elements along with basic behaviors and event support related to game logic features. It also presents modules of code to represent, interpret, and adapt game features to provide the execution of configured games in multiple game platforms. As a proof of concept, a clone of the Doodle Jump game was developed using MEnDiGa assets and compared with original game version. As a result, a new G-factor based approach for game construction is provided, which is able to separate the core of game elements from the implementation itself in an independent, reusable, and large-scale way.

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Abstract Laughter is one of the most important paralinguistic events, and it has specific roles in human conversation. The automatic detection of laughter occurrences in human speech can aid automatic speech recognition systems as well as some paralinguistic tasks such as emotion detection. In this study we apply Deep Neural Networks (DNN) for laughter detection, as this technology is nowadays considered state-of-the-art in similar tasks like phoneme identification. We carry out our experiments using two corpora containing spontaneous speech in two languages (Hungarian and English). Also, as we find it reasonable that not all frequency regions are required for efficient laughter detection, we will perform feature selection to find the sufficient feature subset.

An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network

Content Based Image Retrieval (CBIR) is a challenging research area due to increase in multimedia database and other image libraries day by day. With an intent to provide an efficient search and retrieval, we propose an enhanced Content Based Medical Image Retrieval (CBMIR) system to support the medical practitioners in their diagnosis task. For which, we introduce boosted feature extraction and retrieval phase for medical images using Edge GLCM (EGLCM) and Association Rule Mining (ARM) integrated with Artificial Neural Network (ANN). Improved Particle Swarm Optimization (IPSO) is deployed to optimize the weights of ANN. The system is put forth with four important phases; 1. Pre-Processing, 2. Feature Extraction using Edge Histogram Descriptor (EHD), Local Gabor XOR Pattern (LGXP) and EGLCM, 3. Association Rule Mining using Apriori and 4. Optimized Retrieval using IPSO based ANN and Euclidean distance. In ANN, 7,000 images are trained and 1,100 images are tested. On Comparison with the existing systems, our method has shown best results with improved accuracy of 95 % in addition to reduced computational complexity by pre-processing and dimensionality reduction through minimal feature vector.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for Fub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

COMPONENTIALANALYSIS OF MEANING: THEORYAND APPLICATIONS

There are many different ways to approach the problems of meaning, since meaning is related to many different functions of language. The meanings of words in a language are interrelated and they are defined in part by their relations with other words in the language. Analyzed in the same semantic domain, words can be classified according to shared and differentiating features. Breaking down the sense of a word into its minimal distinctive features, componential analysis of meaning can be a useful approach in the study of meaning, particularly in determining the meaning of a lexeme. Although componential analysis has some difficulties and limitations in its application, it is still used in modern linguistics.

Two-path all-pass based half-band infinite impulse response decimation filters and the effects of their non-linear phase response on ECG signal acquisition

Abstract This paper is based on the novel use of a very high fidelity decimation filter chain for Electrocardiogram (ECG) signal acquisition and data conversion. The multiplier-free and multi-stage structure of the proposed filters lower the power dissipation while minimizing the circuit area which are crucial design constraints to the wireless non-invasive wearable health monitoring products due to the scarce operational resources in their electronic implementation. The decimation ratio of the presented filter is 128, working in tandem with a 1-bit 3 rd order Sigma Delta (ΣD) modulator which achieves 0.04 dB passband ripples and −74 dB stopband attenuation. The work reported here investigates the non-linear phase effects of the proposed decimation filters on the ECG signal by carrying out a comparative study after phase correction. It concludes that the enhanced phase linearity is not crucial for ECG acquisition and data conversion applications since the signal distortion of the acquired signal, due to phase non-linearity, is insignificant for both original and phase compensated filters. To the best of the authors’ knowledge, being free of signal distortion is essential as this might lead to misdiagnosis as stated in the state of the art. This article demonstrates that with their minimal power consumption and minimal signal distortion features, the proposed decimation filters can effectively be employed in biosignal data processing units.

On the Nature of Wh-word in English Non-Restrictive Relative Clauses

646. This paper raises a question on exactly what the nature of Wh-word in English Non-Restrictive Relative Clauses (NRRCs) is, as opposed to that in Restrictive Relative Clauses (RRCs). Other than being equipped with a Wh-element in common, there exist overwhelming asymmetries between English RRCs and NRRCs. Based on the generalizations and disparities between RRCs and NRRCs brought forward in the literature (Quirk, et. al. 1972, 1985, Borsley 1992, Kayne 1994, Borsley 1997, McCawley, 1998, Lobeck, 2000, De Vries 2002, 2006, Aoun & Li 2003, Authier & Reed 2005, Arnold & Borsley 2008) this paper examines the nature of Wh-word in English NRRCs, which has long been neglected in the generative syntax field. We argue that the Wh-word heading NRRCs is not a Wh-Operator since it shows no signature properties of (linguistic) operators; no WCO effect, no Reconstruction effect, no quantificational properties (Aoun & Li 2003, Authier & Reed 2005), whereas the Wh-word in RRC is. The formal features of the Wh-phrase in NRRC are lacking (Law 2000), so that it is argued here to be a deficient Wh-exp, or Wh-expletive with the minimal features. All in all, we argue that the Wh-word heading NRRC is not Wh-operator and the construction is a relative in disguise.

Feature Selection with Multi-Cost Constraint

Cost-sensitive learning extends classical machine learning and data mining by considering various types of costs, of the data. Due to money limited, we also have a constraint on the cost for selecting feature and tradeoff between the test costs and misclassification costs. However, the precious works seldom involve the tradeoff problem under the multi-cost constraint. In this paper, we introduce the misclassification costs into the cost constraint problem firstly, and propose a quadratic heuristic algorithm to deal with the minimal feature selection with multi-cost constraint problem. The goal is to obtain a feature subset with minimal average total cost, which includes test costs and misclassification costs. Experimental results indicate the proposed algorithm is effective and efficient.

Minimum‐features‐based ANN‐PSO approach for islanding detection in distribution system

Islanding detection is important for the protection of any distribution system connected to distributed energy resources (DER's). This study proposes an intelligent islanding detection technique based on artificial neural network (ANN) that employs minimal features from the power system. The accuracy of the trained ANN is improved by optimising the learning rate, momentum and number of neurons in the hidden layers using evolutionary programming (EP) and particle swarm optimisation (PSO). The performance comparison between stand-alone ANN, ANN-EP and ANN-PSO in the form of regression value is performed to obtain the best feature combination for an efficient islanding detection. The proposed technique is tested on- and off-line for various islanding and non-islanding events. The simulation results indicate that the proposed technique can successfully distinguish islanding from other non-islanding events such as load variation, capacitor switching, faults, induction motor starting and DER tripping.

Single dose of rituximab in children with steroid-dependent minimal change nephrotic syndrome.

Rituximab (RTX) can be used in children with nephrotic syndrome, particularly in those with steroid-dependent nephrotic syndrome (SDNS). However, at present there is no unified standard of how to use RTX, with regard to the amount of doses and frequency, in children with nephrotic syndrome. The study aimed to investigate the therapeutic efficacy of a single dose of RTX in children with steroid-dependent minimal change nephrotic syndrome (SD-MCNS). The patients with biopsy-proven minimal change disease (MCD) and clinical features of SDNS received a single dose of RTX (375 mg/m2). The toxicity and side effects of RTX were also observed. The study included 19 patients (10 males and 9 females). Follow-up of the patients was 1-50 months (28.1±16.6 months). B-cell depletion was achieved with RTX infusion (CD20<0.5%) and lasted 1-6 months (mean, 2.92±1.57 months). During follow-up, 10 patients remained in complete remission and did not relapse without administration of oral steroids or immunosuppressants for 4-50 months (mean, 30.1±12.6 months), despite recovery of the B-cell count. Nine patients relapsed in the process of reducing steroids, thus, treatment was maintained at a lower dosage (T=0, P<0.05) than prior to use of RTX. The number of relapses also decreased significantly (T=95, P<0.05). Five of the patients relapsed after stopping steroid for several months. At the end of follow-up, the efficacy of a single induction of RTX was 47.4% (9/19). There were no significant side effects associated with administration of RTX. In conclusion, RTX is a safe and effective alternative for children with SD-MCNS. RTX is an effective treatment for the rapid induction of remission and reduces relapse and steroid dependency. A single dose of RTX for children with SD-MCNS is recommended for rapid induction of remission, reduction of long-term steroid dosage, and decrease in the number of relapses, as it has few side effects.

Chronic neural probe for simultaneous recording of single-unit, multi-unit, and local field potential activity from multiple brain sites

Objective. Drug resistant focal epilepsy can be treated by resecting the epileptic focus requiring a precise focus localisation using stereoelectroencephalography (SEEG) probes. As commercial SEEG probes offer only a limited spatial resolution, probes of higher channel count and design freedom enabling the incorporation of macro and microelectrodes would help increasing spatial resolution and thus open new perspectives for investigating mechanisms underlying focal epilepsy and its treatment. This work describes a new fabrication process for SEEG probes with materials and dimensions similar to clinical probes enabling recording single neuron activity at high spatial resolution. Approach. Polyimide is used as a biocompatible flexible substrate into which platinum electrodes and leads are integrated with a minimal feature size of 5 μm. The polyimide foils are rolled into the cylindrical probe shape at a diameter of 0.8 mm. The resulting probe features match those of clinically approved devices. Tests in saline solution confirmed the probe stability and functionality. Probes were implanted into the brain of one monkey (Macaca mulatta), trained to perform different motor tasks. Suitable configurations including up to 128 electrode sites allow the recording of task-related neuronal signals. Main results. Probes with 32 and 64 electrode sites were implanted in the posterior parietal cortex. Local field potentials and multi-unit activity were recorded as early as one hour after implantation. Stable single-unit activity was achieved for up to 26 days after implantation of a 64-channel probe. All recorded signals showed modulation during task execution. Significance. With the novel probes it is possible to record stable biologically relevant data over a time span exceeding the usual time needed for epileptic focus localisation in human patients. This is the first time that single units are recorded along cylindrical polyimide probes chronically implanted 22 mm deep into the brain of a monkey, which suggests the potential usefulness of this probe for human applications.

Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection

Recently, several efforts have been made in predicting Golgi-resident proteins. However, it is still a challenging task to identify the type of a Golgi-resident protein. Precise prediction of the type of a Golgi-resident protein plays a key role in understanding its molecular functions in various biological processes. In this paper, we proposed to use a mutual information based feature selection scheme with the general form Chou's pseudo-amino acid compositions to predict the Golgi-resident protein types. The positional specific physicochemical properties were applied in the Chou's pseudo-amino acid compositions. We achieved 91.24% prediction accuracy in a jackknife test with 49 selected features. It has the best performance among all the present predictors. This result indicates that our computational model can be useful in identifying Golgi-resident protein types.

Navigation of underwater robot based on dynamically adaptive harmony search algorithm

The current research work has employed an evolutionary based novel navigational strategy to trace the collision free near optimal path for underwater robot in a three-dimensional scenario. The population based harmony search algorithm has been dynamically adapted and used to search next global best pose for underwater robot while obstacle is identified near about robot’s current pose. Each pose is evaluated based on their respective value for objective function which incorporates features of path length minimization as well as obstacle avoidance. Dynamic adaptation of control parameters and new perturbation schemes for solution vectors of harmony search has been proposed to strengthen both exploitation and randomization ability of present search process in a balanced manner. Such adaptive tuning process has found to be more effective for avoiding early convergence during underwater motion in comparison with performances of other popular variants of Harmony Search. The proposed path planning method has also shown better navigational performance in comparison with improved version of ant colony optimization and heuristic potential field method for avoiding static obstacles of different shape and sizes during underwater motion. Simulation studies and corresponding experimental verification for three-dimensional navigation are performed to check the accuracy, robustness and efficiency of proposed dynamically adaptive harmony search algorithm.

Uzbekness and Islam: A Survey-based Analysis of Identity in Uzbekistan

The article ascertains orders of preference in Uzbek identity by interviewing just over 200 citizens of Uzbekistan belonging to traditionally Muslim ethnic groupings. While almost everyone considers himself or herself a Muslim, the great majority perceive themselves above all as citizens of Uzbekistan. Moreover, their Islam is not reflected primarily in Islamic practice but rather in a somewhat nebulous Islamic traditionalism. They think, too, that the state should incorporate some minimal Islamic features. In the international arena, they tend to prefer Muslim over non-Muslim peoples and communities, but not necessarily as designations for labor migration. Finally, the focus of their interest is manifestly directed toward other post-Soviet successor states.

"Sketch and Peel" Lithography for High-Resolution Multiscale Patterning.

We report a unique lithographic process, termed "Sketch and Peel" lithography (SPL), for fast, clean, and reliable patterning of metallic structures from tens of nanometers to submillimeter scale using direct writing technology. The key idea of SPL process is to define structures using their presketched outlines as the templates for subsequent selective peeling of evaporated metallic layer. With reduced exposure area, SPL process enables significantly improved patterning efficiency up to hundreds of times higher and greatly mitigated proximity effect compared to current direct writing strategy. We demonstrate that multiscale hierarchical metallic structures with arbitrary shapes and minimal feature size of ∼15 nm could be defined with high fidelity using SPL process for potential nanoelectronic and nano-optical applications.

A Conditional Equity Risk Model for Regulatory Assessment

We define and study in this work a simple model allowing for a prudential valuation of solvency capital requirement while avoiding over-assessment specifically after market disruption. The main idea is to include a dampener component in charge of refining risk assessment after a market failure. Rather than aiming at a realistic, and thus complex, description of equity prices movements, our model concentrates on minimal features enabling accurate computation of regulatory capital requirements. The model is defined both in a discrete and continuous fashion. In the latter case, we prove the existence, uniqueness and stability of the solution of the stochastic functional differential equation that specifies the model. One difficulty is that the proposed underlying stochastic process has neither stationary nor independent increments. We are however able to perform statistical analyses in view of its validation. Numerical experiments show that our model outperforms more elaborate ones of common use as far as medium term (between 6 months and 5 years) risk assessment is concerned. We believe that our approach offers an attractive alternative for insurance and reinsurance companies to assess their 1 year equity-risk solvency capital requirement with an internal model and their ORSA capital.

Research on the redesign of precision tools and their manufacturing process based on selective laser melting (SLM)

Purpose – This paper aims to verify that additive manufacturing technology could be used for the redesign and rapid manufacturing of tools and determine whether the mechanical performance of such tools can satisfy the practical operating requirements. Design/methodology/approach – A special key was selected as the research object in this paper. The special key was innovatively redesigned and manufactured directly using selective laser melting (SLM). The function and critical geometries of the special key were first analysed, which was followed by discussions on the geometrical constraints in the manufacturing of typical geometrical features using SLM technology. Next, the special key was redesigned based on the SLM geometrical constraints and the functional requirements. Finally, the key was manufactured using SLM, and the mechanical performance characteristics of the key were determined. Findings – The minimal geometrical feature was 0.2 mm when manufacturing thin walls using SLM. The reliable building angle of an overhanging surface was 40°. The top surface quality of the part could be greatly improved through laser surface re-melting. The volume of the redesigned special key based on the SLM process was only one-third to one-fourth of the original key. The mechanical properties, such as tensile strength and micro-hardness, of the samples manufactured using SLM were able to reach the practical operating requirements. Originality/value – It is completely feasible to redesign and manufacture precision tools based on the innovative approach of SLM. The advantages of the redesigned tools includes the lack of design restrictions that hinder traditional manufacturing methods, material savings, ability to produce tools that cannot be easily copied and rapid production speed for a small number of tools.

Contrast in Phonology, 1867–1967: History and Development

This article surveys the history of contrast in phonology from Bell's Visible Speech ( 1867 ) until Chomsky &amp; Halle's Sound Pattern of English ( 1968 ). Phonological contrast can be viewed at the segmental and subsegmental (feature) levels. As contrast at the segmental level involves the phoneme, whose later history has been extensively documented, I concentrate on the origins of the concept in the work of Sweet. Subsequently, I focus on subsegmental-level contrast. After a look at its treatment in phonological analyses that operated without an explicit theory of features, I turn to Trubetzkoy, in whose work we find the seeds of later approaches. The article explores the foundations of the main methods of computing contrastive features: minimal differences and hierarchical feature ordering. It concludes with a discussion of contrast in early generative phonology and reviews some of the reasons for its decline at the end of the 1960s.

Governance and the economy in Asia and the United States: institutions, instruments and reform

Effective governance involving the use of various institutions and instruments is very important for economic development. While many states fail to achieve even the minimal features of effective governance, state capitalism has proven to be a successful model economically. The problem is that state capitalism is frequently associated with authoritarian and corrupt regimes. Over time, such regimes limit economic efficiency, ignore the environment, and under-invest in social and health services. In the West and some Asian countries, these conditions have led to substantial reform in democratic governance. Singapore and possibly the People’s Republic of China offer alternative models of reform, reducing corruption and somewhat liberalising their economies in the absence of well-developed democratic governance.

Osteogenesis imperfecta type 111

Osteogenesis Imperfecta is a rare inherited disorder affecting connective tissue integrity, characterized by bone fragility of varying severity ranging from lethal forms to those with very minimal features. Here, we are presenting a case of 3 months old infant with multiple fractures sustained during routine handling .Baby was diagnosed as OI type III, which is the most severe non-lethal form of the disease. Early diagnosis and institution of new modalities of treatments like bisphosphonates and surgical stabilization gives better prognosis in these patients.