Miracle-dz/UniFAR-FTUs huggingface dataset:
Fully Data-driven Convolutional Filters with Deep Learning Models for Epileptic Spike Detection

Epilepsy is a chronic disorder that causes unprovoked, recurrent-seizures. Characteristic spikes are often observed in the electroencephalogram (EEG) of epileptic patients in order to diagnose the disorder. Several methods have been investigated to automatically detect such spikes. The most common methods employ sub-band decomposition with discrete wavelet transform (DWT) or other filters to preprocess the EEG data before feeding it into a machine learning model. This paper introduces a fully data-driven method that automatically determines EEG frequency bands of interest. The raw signal is fed into a convolutional layer to detect suitable frequency bands, followed by a feedforward convolutional neural network (CNN) model or recurrent neural network (RNN) models for epileptic spike and non-spike classification. Fitting data of six patients, annotated by an epilepsy specialist, resulted in a convolutional layer with a frequency characteristic similar to bandpass filters. This result strongly justifies limiting the bandwidth of a signal, as done in previous studies. Moreover, results of the cross-subject validation indicate that a classical support vector machine with fixed preprocessing achieves comparable performance in the classification with fully data-driven models.
Extraction of street trees from mobile laser scanning point clouds based on subdivided dimensional features

This paper proposes a method for automated extraction of street trees in a typical urban environment from 3D point cloud data acquired by the mobile laser scanning system. First, the algorithm utilizes the voxel-based method to remove the ground points from the scene. Second, the Euclidean distance clustering is adopted to cluster points into individual objects. The eigenvalues of neighborhood covariance matrix and the corresponding normalized centroid distance are computed for each point to obtain the subdivided dimensional features. Finally, the statistical component features and horizontal information are calculated for object detection. The experiment results show the feasibility of the proposed algorithm.
Wains: A Pattern-Seeking Artificial Life Species

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.
Real‐Time Cloud Rendering

This paper presents a method for realistic real‐time rendering of clouds suitable for flight simulation and games. It provides a cloud shading algorithm that approximates multiple forward scattering in a preprocess, and first order anisotropic scattering at runtime. Impostors are used to accelerate cloud rendering by exploiting frame‐to‐frame coherence in an interactive flight simulation. Impostors are shown to be particularly well suited to clouds, even in circumstances under which they cannot be applied to the rendering of polygonal geometry. The method allows hundreds of clouds and hundreds of thousands of particles to be rendered at high frame rates, and improves interaction with clouds by reducing artifacts introduced by direct particle rendering techniques.
Production planning and scheduling in Cyber-Physical Production Systems: a review

ABSTRACT The study of scheduling procedures has generated important contributions to the improvement of productivity in different industrial branches. In recent years, the incorporation of high technology to production systems brought the advent of a ‘fourth industrial revolution’, Industry 4.0. One of the mainstays of Industry 4.0 is the application of Cyber-Physical Systems (CPS), which are physical production systems that incorporate sophisticated computational tools. This implies embedding computers, enabling a real-time connection between workstations and Decision Support Systems. It seems natural, in this setting, to associate scheduling schemes to CPS. This allows streamlining the decision-making process, allowing more flexible and lean production lines. We review here the most salient contributions on scheduling in these environments. We distinguish between work on the basic issues of scheduling and that on scheduling as part of higher-level production planning activities. To frame correctly this distinction we analyse how CPS can embody the different levels of the ISA-95 structure and how this relates to the classical structure of production planning. Our review suggests that the real-time availability of information will have a significant impact in this area and that scheduling will be solved in the future in decentralised decision processes.
Heat-treated whole cottonseed: effect of dietary protein concentration on the performance and amino acid utilization by the mammary gland of dairy cows

The effects of replacing whole cottonseed by heated whole cottonseed at two levels of crude protein on amino acid (AA) utilization by the bovine mammary gland were investigated. Four Israeli Holstein cows were used in a 4×4 Latin square design with 2×2 factorial arrangements. Diets were formulated to contain two levels of crude protein (CP), normal (160 g CP/kg dry matter) and low (140 g CP/kg dry matter), and two levels of rumen undegradable protein (UDP), high (380 g UDP/kg CP) and low (350 g UDP/kg CP). Whole cottonseed was quantitatively substituted by heated whole cottonseed to formulate the high UDP diets. Intakes of dry matter and organic matter were similar for all treatments and averaged 15·9 and 14·4 kg/d respectively. Yields of milk and its constituents were similar for all treatments. Milk yield averaged 23·1 kg/d and this contained (per kg) 32·3 g CP, 25·4 g total casein and 47·5 g lactose. The milk fat content was lower in the cows given the high UDP diets, averaging 34·6 g/kg compared with 38·9 g/kg in the cows fed on the diets with low UDP. Plasma AA concentrations were similar for all treatments apart from Leu and Ile, which were higher in the cows given the high CP diets: 152 and 103 μm compared with 183·8 and 131·5 μm for the low CP diets. Net extraction and balance of essential AA across the half udder suggested that essential AA were supplied in amounts sufficient to meet milk protein requirements for all treatments. The surplus AA supplied as a result of feeding the high dietary CP were probably catabolized via an enhanced oxidative pathway, possibly in the liver, presumably as a passive response to their disposal.
Effects of Glucose, Metformin, and Protein on Formation of Flower-like Nanocomposites of Struvite in Infected Artificial Urine Medium by Methicillin-resistant Staphylococcus aureus (MRSA): New Report

Urinary tract infections (UTIs) are the reason for 15-70% of urinary stone disease (USD). Many diabetic patients suffer from USD resulting from bacterial infections by multi drug resistant (MDR) bacteria such as Staphylococcus aureus ATCC 43300. These bacteria can enhance struvite stones’ formation in diabetic patients with UTIs. In this regard, hyperglycemia, proteinuria, and using of metformin drug may be important factors. Three parameters including glucose, metformin, and protein with their three levels of concentrations were determined by experimental design of Taguchi method to obtain optimized formation of struvite stones. Artificial urine medium was utilized for simulation of natural human urine. Scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDAX) techniques were applied for evaluation of morphology and elemental compositions of formed calcium phosphate. This study showed highest concentration of struvite stones (0.512 g/L) under interaction conditions of 3×2×2 levels respectively for glucose, metformin, and protein. Results of SEM and EDAX analyses demonstrated clumped crystallite property and flower-like nanocomposites (NCs) of struvite stones with contribution of calcium (160.8) and phosphate (131.4) elements. It may be concluded from this investigation that therapy of MDR bacteria, hyperglycemia, and proteinuria can decrease urinary stone formation in diabetic patients by having UTIs.
Education for sustainability: Creating Skills in Human Capital Analysis

This paper presents the role of an innovative university masters' degree programme as a basis for skills development in human capital analysis for finance and business students in their core degree programmes. The programme uses general systems theory to provide a framework for describing two components of the sustainable organisation: firstly, the inter-relationships of organisations to their context; and secondly, to describe the configuration of internal management systems. In the subject, the students take the role of human capital analysts in order to focus on defining, assessing and measuring the human elements of corporate sustainability. This postgraduate business subject has been developed within the framework of the UNSW guidelines on teaching and learning, designed to encourage innovation in both content and process. The authors conclude that the role of education is instrumental in creating a community of practice which uses sustainability principles as a basis for investment decision making amongst business students, who will be the next generation of executives and financial analysts.
Room Temperature Processed Highly Efficient Large‐Area Polymer Solar Cells Achieved with Molecular Engineering of Copolymers

The room temperature (RT) processability of the photoactive layers in polymer solar cells (PSCs) from halogen‐free solvent along with their highly reproducible power conversion efficiencies (PCEs) and intrinsic thickness tolerance are extremely desirable for the large‐area roll‐to‐roll (R2R) production. However, most of the photoactive materials in PSCs require elevated processing temperatures due to their strong aggregation, which are unfavorable for the industrial R2R manufacturing of PSCs. These limiting factors for the commercialization of PSCs are alleviated by synthesizing random terpolymers with components of (2‐decyltetradecyl)thiophen‐2‐yl)naphtho[1,2‐c:5,6‐c′]bis[1,2,5]thiadiazole and bithiophene substituted with methyl thiophene‐3‐carboxylate (MTC). In contrast to the temperature‐dependent PNTz4T polymer, the resulting random terpolymers (PNTz4T‐MTC) show better solubility, slightly reduced crystallinity and aggregation, and weaker intermolecular interaction, thus enabling PNTz4T‐MTC to be processed at RT from a halogen‐free solvent. Particularly, the PNTz4T‐5MTC‐based photoactive layer exhibits an excellent PCE of 9.66%, which is among the highest reported PCEs for RT and ecofriendly halogen‐free solvent processed fullerene‐based PSCs, and a thickness tolerance with a PCE exceeding 8% from 100 to 520 nm. Finally, large‐area modules fabricated with the PNTz4T and PNTz4T‐5MTC polymer have shown 4.29% and 6.61% PCE respectively, with an area as high as 54.45 cm2 in air.
In vivo muscarinic 2 receptor imaging in cognitively normal young and older volunteers

The precise effects of normal aging on the cholinergic system are unknown, as both in vitro and PET studies have shown conflicting results. In vivo determination of muscarinic receptor distribution and density has been hampered by both poor subtype selectivity and/or blood–brain barrier permeability of known ligands. Previous in vitro and in vivo work with the F‐18 labeled muscarinic agonist, 3‐(3‐ (3‐[18F]Flouropropyl)thio)‐1,2,5‐thiadiazol‐4‐yl)‐1,2,5,6‐tetrahydro‐1‐methylpyridine (18FP‐TZTP) suggested the use of 18FP‐TZTP to selectively quantify M2 receptors in humans. In this study, we used 18FP‐TZTP to infer M2 receptor avidity in the brains of 15 healthy younger subjects (mean age = 28.3 ± 5.5 years) and 20 healthy older subjects (mean age = 62.1 ± 7.7 years). Corrections for subject motion during the 120‐min acquisition and partial voluming (PVC) were performed. A one‐tissue compartment model was used to estimate the volumes of distribution (VT) of 18FP‐TZTP. Within both groups of subjects, volumes of distribution (K1/k2) in cortical, subcortical, and cerebellar areas were consistent with M2 receptor topography. Compared to younger subjects older subjects had significantly higher means and standard deviations for the volumes of distribution of 18FP‐TZTP throughout much of the cerebellum, cortex, and subcortex (Global Gray VT = 742 ± 163 in older subjects and 645 ± 74 in younger subjects, P < 0.03). Across all subjects 18FP‐TZTP, regional, and Global Gray distribution volumes were significantly correlated to age (Global Gray VT, r = 0.41, P < 0.01). A lower concentration of acetylcholine in the synapse of some older subjects is one possible explanation for the data. Synapse 48:39–44, 2003. Published 2003 Wiley‐Liss, Inc.
A study of Mixed-Membership Models for Complex Networks Analysis. (Etude des modèles à composition mixée pour l'analyse de réseaux complexes)

Relational data are ubiquitous in the nature and their accessibility has not ceased to increase in recent years. Those data, see as a whole, form a network, which can be represented by a data structure called a graph, where each vertex of the graph is an entity and each edge a connection between pair of vertices. Complex networks in general, such as the Web, communication networks or social network, are known to exhibit common structural properties that emerge through their graphs. In this work we emphasize two important properties called *homophilly* and *preferential attachment* that arise on most of the real-world networks. We firstly study a class of powerful *random graph models* in a Bayesian nonparametric setting, called *mixed-membership model* and we focus on showing whether the models in this class comply with the mentioned properties, after giving formal definitions in a probabilistic context of the latter. Furthermore, we empirically evaluate our findings on synthetic and real-world network datasets. Secondly, we propose a new model, which extends the former Stochastic Mixed-Membership Model, for weighted networks and we develop an efficient inference algorithm able to scale to large-scale networks.
Autonomous landing system for aerial mobile robot cooperation

In vision-based autonomous landing, the accuracy of GPS signal and the efficiency of target tracking and detecting will affect the performance in autonomous landing system. This paper presents a method of cooperation among two aerial mobile robots at high altitude and low altitude for autonomous navigation and landing. Fully utilizing the extensive vision and high flexibility on the high-altitude aerial mobile robot to control the low-altitude aerial mobile robot to finish tracking and landing procedures. The flight controller achieves to track target and attitude control in real time. The flight controller using high level control which are fuzzy logic and neural network to give to low level control. The aerial mobile robots use the fuzzy logic control and neural network for positioning and maneuvering, and image-based visual servoing for tracking the low-altitude aerial mobile robot and landing target. By applying these methods, we have achieved a scalable and robust procedures for autonomous landing.
Impact of Version History Metrics on Maintainability

In this study we present how some version control history based metrics affect maintainability of the source code. These metrics cover intensity of modifications, code ownership and code aging. We determine the order of source files based on each analyzed metrics, and compare it with their maintainability based order. As a cross-check we perform a comparison test with post-release defects as well. We performed the analysis on 14 versions of 4 well-known open source software systems. The results show high correlation between the version control metrics and relative maintainability indexes, in each case. The comparison with post-release defects also support the results in most of the cases.
High-resolution and large dynamic range nanomechanical mapping in tapping-mode atomic force microscopy

High spatial resolution imaging of material properties is an important task for the continued development of nanomaterials and studies of biological systems. Time-varying interaction forces between the vibrating tip and the sample in a tapping-mode atomic force microscope contain detailed information about the elastic, adhesive, and dissipative response of the sample. We report real-time measurement and analysis of the time-varying tip–sample interaction forces with recently introduced torsional harmonic cantilevers. With these measurements, high-resolution maps of elastic modulus, adhesion force, energy dissipation, and topography are generated simultaneously in a single scan. With peak tapping forces as low as 0.6 nN, we demonstrate measurements on blended polymers and self-assembled molecular architectures with feature sizes at 1, 10, and 500 nm. We also observed an elastic modulus measurement range of four orders of magnitude (1 MPa to 10 GPa) for a single cantilever under identical feedback conditions, which can be particularly useful for analyzing heterogeneous samples with largely different material components.
In vivo regulation of the mandibular organ in the edible crab, Cancer pagurus

Considerable evidence indicates that methyl farnesoate (MF) production by the crustacean mandibular organs is negatively regulated by neuropeptides from the sinus gland (SG) in the eyestalk. In the crab Cancer pagurus, two neuropeptides (MO–IH–1 and –2) have been isolated from the SG that inhibit MF synthesis by mandibular organs of female crabs in vitro. To test their activity in vivo, we treated eyestalk–ablated male crabs with SG extracts (SGEs) or MO–IH–1 and –2. SGEs reduced haemolymph levels of MF by 60–80%, while MO–IH–1 and–2 had little effect. Protease treatment of SGEs destroyed the in vivo activity, suggesting that the extract contains an additional peptide responsible for the in vivo activity. When separated by reversed–phase high performance liquid chromatography (HPLC), the in vivo activity eluted in fractions prior to MO–IH–1 and –2. When mandibular organs were removed from animals previously treated in vivo with these active fractions, they had reduced levels of MF synthesis and activity of farnesoic acid O–methyl transferase compared with mandibular organs from animals treated with saline. Together, these results indicate that the regulation of the crustacean mandibular organ is complex and may involve several SG compounds. Some of these compounds (i.e., MO–IH–1 and–2) act directly on the tissue while others affect the mandibular organ indirectly.
Estimating Comma Placement in Natural Language

We study the feasibility of identifying comma locations using both n-gram models and stochastic contextfree grammars (SCFGs). Specifically, our algorithms take an input sentence without commas and returns the positions where commas should be inserted, along with probability or confidence estimates. This can be generalized to correcting comma placement with minor modifications. However, we focus on this simpler comma insertion problem. Two widely used tools for processing natural language are n-gram models and SCFG parsers. N-grams provide a linear Markov model, while SCFG parsers build a rich hierarchical structure. In English, commas are typically used to separate phrases in a sentence. Hence, it would seem logical that SCFGs should have better performance than the n-gram model, which examines sentences in n-gram length chunks. However, n-grams can easily be trained on very large data sets, and thus can provide a rich source of statistical information. Since these language models are very di↵erent, this paper evaluates both.
Novel Methods for Reasoning with Uncertain Hard and Soft Data using Probabilistic and Belief Theoretic Methods

of a dissertation at the University of Miami. Dissertation supervised by Associate Professor Manohar N. Murthi. No. of pages in text. (124) Effectively combining multiple and complementary sources of information is becoming one of the most promising paths for increased accuracy and more detailed analysis in numerous applications. Neuroscience, business analytics, military intelligence, and sociology are among the areas that could significantly benefit from properly processing diverse data sources. However, traditional methods for combining multiple sources of information are based on slow or impractical methods that rely either on vast amounts of manual processing or on suboptimal representations of data. Moreover, most of the existing methods are not well suited for dealing with the increasing amount of human-generated data. We introduce an analytical framework that allows automatic and efficient processing of both hard (e.g., physics-based sensors) and soft (e.g., human-generated) information, leading to enhanced decision-making in multisource environments. This framework is based on the Dempster-Shafer (DS) Theory of Evidence as the common language for data representation and inference. To model and track uncertainties in soft data, our framework introduces Uncertain Logic, a classically consistent first order logic environment. In addition, our framework defines a filtering and tracking environment for incorporating both hard and soft data, where the probability posterior can be decomposed into a product of combining functions over subsets of the state and measurement variables. This combining function approach offers a framework for the development and incorporation of more sophisticated uncertainty modeling and tracking/estimation models, and at the same time allows incorporating and enhancing existing Bayesian methods. Future work is aimed at increasing the computational efficiency of the overall hard and soft data fusion framework.
The effects of multimedia communication technology on non-collocated teams: a case study

Collaborative teams are becoming increasingly important for industry, both within and across companies. There is a need for communication technology to support teams because many teams are non-collocated, or ‘virtual’. Two automotive supply chain teams were observed while they were experimenting with multimedia conferencing in order to determine what support non-collocated teams need and the potential effects of introducing technologies on their group processes. The observations included meeting recordings and other sources that show the organizational factors affecting teams. Working in teams requires very close collaboration. Communication technology can help teams if it is used to foster close and relatively informal person-to-person interaction. Organizational constraints on how the technology is introduced favour high-technology, specialpurpose installations, but teams can best be supported using relatively modest equipment with desktop access.