Miracle-dz/UniFAR-FTUs huggingface dataset:
Ray tracing surfaces of revolution: an old problem with a new perspective

We present a new subdivision scheme that is shown to improve the performance of ray tracing surfaces of revolution over Kajiya's (1983) classical work. This is based on a monotonic interval partitioning of a generatrix of a surface of revolution. The algorithm has a search complexity upper bound of O(log(m n)) for m monotonic intervals and n subdivisions for each interval and runs up to three times faster on large scenes. This method also suggests a novel hybrid bounding volume scheme that reduces this number of intersection tests between a ray and the actual object surface.
Monthly Low-Flow Forecasting Using a Stochastic Model and Adaptive Network Based Fuzzy Inference System

Surface water management practices are directly influenced by the streamflow forecasting, especially for the low-flow context. In this paper, the monthly low- flow time series were modeled and forecasted using a traditional stochastic model (Autoregressive Integrated Moving Average-ARIMA) and an artificial intelligence based model (Adaptive Network based Fuzzy Inference System-ANFIS). Low-flow in each month was defined as the minimum value of one, three, and seven day moving averages of daily streamflow. The performance of the stochastic model was compared to the neuro- fuzzy model through application to the streamflow data from the Navrood River basin in the Guilan state, northern Iran. The results showed that the stochastic model resulted in more accurate forecasted values than the neuro-fuzzy model for one, three, and seven day low-flow time series. Furthermore, in all neuro-fuzzy and stochastic models the error in forecasting three-day low-flow is less than those for one- and seven-day low- flow.
Evaluation of Ultrasonography to Measure Fetal Size and Heart Rate as Predictors of Fetal Age in Hair Sheep

There is little information available on methods to estimate fetal age of hair sheep. The objective of this study was to determine whether crown-rump length (CRL) and/or fetal heart rate (FHR) could be used to predict fetal age in hair sheep using transrectal B-mode and Doppler ultrasonography. St Croix White, Barbados Blackbelly and Dorper X St. Croix White ewes (n = 54) were scanned weekly beginning 28 d after a successful mating. Linear array B-mode ultrasonography (5 MHz) was used to measure CRL and visual FHR, and Doppler ultrasonography was used to measure audible FHR. Due to the size of the fetus CRL was not measurable after 42 d of gestation, and visual FHR was Evaluation of Ultrasonography to Measure Fetal Size and Heart Rate as Predictors of Fetal Age in Hair Sheep
Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar

In this paper, a novel sparsity-aware direction of arrival (DOA) estimation scheme for a noncircular source is proposed in multiple-input multiple-output (MIMO) radar. In the proposed method, the reduced-dimensional transformation technique is adopted to eliminate the redundant elements. Then, exploiting the noncircularity of signals, a joint sparsity-aware scheme based on the reweighted l1 norm penalty is formulated for DOA estimation, in which the diagonal elements of the weight matrix are the coefficients of the noncircular MUSIC-like (NC MUSIC-like) spectrum. Compared to the existing l1 norm penalty-based methods, the proposed scheme provides higher angular resolution and better DOA estimation performance. Results from numerical experiments are used to show the effectiveness of our proposed method.
The Practices of Unpaid Third-Party Developers - Implications for API Design

To draw on the innovation capabilities of third-party developers many organizations are currently deploying open application programming interfaces (API’s). While third-party services may offer commercial opportunities for independent software firms, a large portion of existing third-party software are undertaken without any financial compensation. Although unpaid developers offers a potential source for innovation of end-user services, the current literature has largely overlooked how these unpaid actors use and appropriate the technology provided by organizations. To this end, this research pays specific attention to the specific practices of unpaid developers. The data used for analysis were collected through a programming contest – a hackathon – where unpaid developers gather to craft end-user services. Through an ethnographic lens we present a number of recurrent activities and patterns of action employed by developers and from this analysis we present implications for API designers seeking to attract unpaid developers.
Lifting proof-relevant unification to higher dimensions

In a dependently typed language such as Coq or Agda, unification can be used to discharge equality constraints and detect impossible cases automatically. By nature of dependent types, it is necessary to use a proof-relevant unification algorithm where unification rules are functions manipulating equality proofs. This ensures their correctness but simultaneously sets a high bar for new unification rules. In particular, so far no-one has given a satisfactory proof-relevant version of the injectivity rule for indexed datatypes. In this paper, we describe a general technique for solving equations between constructors of indexed datatypes. We handle the main technical problem—generalization over equality proofs in the indices—by introducing new equations between equality proofs. Borrowing terminology from homotopy type theory, we call them higher-dimensional equations. To apply existing one-dimensional unifiers to these higher-dimensional equations, we show how unifiers can be lifted to a higher dimension. We show the usefulness of this idea by applying it to the unification algorithm used by Agda, though it can also be applied in languages that support identity types but not general indexed datatypes.
A Focused Multiple Reaction Monitoring (MRM) Quantitative Method for Bioactive Grapevine Stilbenes by Ultra-High-Performance Liquid Chromatography Coupled to Triple-Quadrupole Mass Spectrometry (UHPLC-QqQ)

Grapevine stilbenes are a family of polyphenols which derive from trans-resveratrol having antifungal and antimicrobial properties, thus being considered as phytoalexins. In addition to their diverse bioactive properties in animal models, they highlight a strong potential in human health maintenance and promotion. Due to this relevance, highly-specific qualitative and quantitative methods of analysis are necessary to accurately analyze stilbenes in different matrices derived from grapevine. Here, we developed a rapid, sensitive, and specific analysis method using ultra-high-performance liquid chromatography coupled to triple-quadrupole mass spectrometry (UHPLC-QqQ) in MRM mode to detect and quantify five grapevine stilbenes, trans-resveratrol, trans-piceid, trans-piceatannol, trans-pterostilbene, and trans-ε-viniferin, whose interest in relation to human health is continuously growing. The method was optimized to minimize in-source fragmentation of piceid and to avoid co-elution of cis-piceid and trans-resveratrol, as both are detected with resveratrol transitions. The applicability of the developed method of stilbene analysis was tested successfully in different complex matrices including cellular extracts of Vitis vinifera cell cultures, reaction media of biotransformation assays, and red wine.
DATA-BASED MECHANISTIC MODELLING OF ENVIRONMENTAL SYSTEMS

The paper discusses the problems associated with environmental modelling and the need to consider uncertainty in the formulation, identification, estimation and validation of environmental models. It introduces the concept of Data-Based Mechanistic (DBM) modelling and contrasts its inductive approach with the hypotheticodeductive approaches that dominate most environmental modelling research at the present time. The major methodological procedures utilized in DBM modelling are outlined and two practical examples illustrate how it has been applied in a hydrological and water quality context. Keynote paper presented at the International Federation on Automatic Control (IFAC) Workshop on Environmental Systems, Tokyo, Japan, August 21st, 2001.
Estimating Private Rate Of Retun On Higher Education In Pakistan

The current study focuses primarily on estimating private rate of return for two successive degrees of higher education in Pakistan namely M.Phil and PhD. Sensitivity analysis on calculations of the average private rate of return to university education is undertaken. This involves checking how estimates of the rate of return vary with key assumptions used to calculate the rate of return. Further, to estimate internal rate of returns, explicit & implicit costs and earnings on BPS-2005 scale to which a degree holder is eligible to apply. Estimates of the rate of return to a university degree would provide one measure of the net monetary benefits from higher education. Result reveals that private rate of return is extremely low for M.Phil and PhD degrees that can explain dismay situation of higher education in the country. To boost higher education, private rate of return must increase that can be achieved effectively either by raising pay scales, or by giving interest-free scholarships or by increasing retirement age.
A CCN-based multi-source and multi-path transport mechanism for wireless mobile networks

Due to the massive explosion of social networking and content sharing, it is becoming challenging to transmit a huge amount of multimedia content over wireless mobile networks. The existing paradigm of mobile multimedia sharing has yet to effectively address the energy and bandwidth limitations of wireless devices. Content-Centric Networking (CCN) has been proposed as a possible way to drive the current host-centric Internet paradigm towards a novel content-centric behavior. CCN still has some limitations in providing high-quality content, highly-reliable, faster, and efficient content sharing from multiple content sources, and no existing solution is available to retrieve contents over multiple paths. In this paper, we propose a content delivery and retrieval mechanism using multipath forwarding on top of CCN where contents are divided into multiple chunks and published from multiple mobile sources. It provides a new approach to allow multiple sources for content and to retrieve them chunk by chunk in parallel. We also present an implementation of our mechanism's basic operations and demonstrate the feasibility and performance of content retrieval from multiple content sources.
Sequential Labeling with online Deep Learning

Deep learning has attracted great attention recently and yielded the state of the art performance in dimension reduction and classification problems. However, it cannot effectively handle the structured output prediction, e.g. sequential labeling. In this paper, we propose a deep learning structure, which can learn discriminative features for sequential labeling problems. More specifically, we add the inter-relationship between labels in our deep learning structure, in order to incorporate the context information from the sequential data. Thus, our model is more powerful than linear Conditional Random Fields (CRFs) because the objective function learns latent non-linear features so that target labeling can be better predicted. We pretrain the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize our objective function with online learning algorithm, a mixture of perceptron training and stochastic gradient descent. We test our model on different challenge tasks, and show that our model outperforms significantly over the completive baselines.
Audio-visual multimodal fusion for biometric person authentication and liveness verification

In this paper we propose a multimodal fusion framework based on novel face-voice fusion techniques for biometric person authentication and liveness verification. Checking liveness guards the system against spoof/replay attacks by ensuring that the biometric data is captured from an authorised live person. The proposed framework based on bi-modal feature fusion, cross-modal fusion as well as 3D shape and texture fusion techniques, allow a significant improvement in system performance against impostor attacks, type-1 replay attacks (still photo and prerecorded audio), and challenging type-2 replay attacks(CG animated video from a still photo and pre-recorded audio) and robustness to pose and illumination variations.
Rapid Multiplex Nested PCR for Detection of Respiratory Viruses

ABSTRACT Respiratory tract infections can be caused by a heterogeneous group of viruses and bacteria that produce similar clinical presentations. Specific diagnosis therefore relies on laboratory investigation. This study developed and evaluated five groups of multiplex nested PCR assays that could simultaneously detect 21 different respiratory pathogens: influenza A virus (H1N1, H3N2, and H5N1); influenza B virus; parainfluenza virus types 1, 2, 3, 4a, and 4b; respiratory syncytial virus A and B; human rhinoviruses; human enteroviruses; human coronaviruses OC43 and 229E; severe acute respiratory syndrome coronavirus; human metapneumoviruses; Mycoplasma pneumoniae; Chlamydophila pneumoniae; Legionella pneumophila; and adenoviruses (A to F). These multiplex nested PCRs adopted fast PCR technology. The high speed of fast PCR (within 35 min) greatly improved the efficiency of these assays. The results show that these multiplex nested PCR assays are specific and more sensitive (100- to 1,000-fold) than conventional methods. Among the 303 clinical specimens tested, the multiplex nested PCR achieved an overall positive rate of 48.5% (95% confidence interval [CI], 42.9 to 54.1%), which was significantly higher than that of virus isolation (20.1% [95% CI, 15.6 to 24.6%]) and that of direct detection by immunofluorescence assay (13.5% [95% CI, 9.7 to 17.4%]). The improved sensitivity was partly due to the higher sensitivity of multiplex nested PCR than that of conventional methods in detecting cultivatable viruses. Moreover, the ability of the multiplex nested PCR to detect noncultivatable viruses, particularly rhinoviruses, coronavirus OC43, and metapneumoviruses, contributed a major gain (15.6%) in the overall positive rate. In conclusion, rapid multiplex nested PCR assays can improve the diagnostic yield for respiratory infections to allow prompt interventive actions to be taken.
Porous tissue grafts sandwiched with multilayered mesenchymal stromal cell sheets induce tissue regeneration for cardiac repair.

AIMS To provide the basis for uniform cardiac tissue regeneration, a spatially uniform distribution of adhered cells within a scaffold is a prerequisite. To achieve this goal, a bioengineered tissue graft consisting of a porous tissue scaffold sandwiched with multilayered sheets of mesenchymal stromal cells was developed. METHODS AND RESULTS This tissue graft (sandwiched patch) was used to replace the infarcted wall in a syngeneic Lewis rat model with an experimentally chronic myocardial infarction (MI). There were four treatment groups (n >/= 10): sham, MI, empty patch, and sandwiched patch. After a 7 day culture of the sandwiched patch, a tissue graft with relatively uniform cell concentrations was obtained. The cells were viable and tightly adhered to the tissue scaffold, as the endogenous extracellular matrix inherent with multilayered cell sheets can act as an adhesive agent for cell attachment and retention. At retrieval, the area of the empty patch was relatively enlarged, suggesting reduced structural support, while that of the sandwiched patch remained about the same (P = 0.56). In the immunofluorescent staining, host cells together with neo-microvessels were clearly observed in the empty patch; however, there were still a large number of unfilled pores within the patch. In the sandwiched patch, besides host cells, originally seeded cells were populated within the entire patch. No apparent evidence of apoptotic cell death was found in both studied patches. Thus, the sandwiched-patch-treated hearts demonstrated a better heart function to the empty-patch-treated hearts (P < 0.05). CONCLUSION The results demonstrated that this novel bioengineered tissue graft can serve as a useful cardiac patch to restore the dilated left ventricle and stabilize heart functions after MI.
Maximum Length Weighted Nearest Neighbor approach for electricity load forecasting

In this paper we present a new approach for time series forecasting, called Maximum Length Weighted Nearest Neighbor (MLWNN), which combines prediction based on sequence similarity with optimization techniques. MLWNN predicts the 24 hourly electricity loads for the next day, from a time sequence of previously electricity loads up to the current day. We evaluate MLWNN using electricity load data for two years, for three countries (Australia, Portugal and Spain), and compare its performance with three state-of-the-art methods (weighted nearest neighbor, pattern sequence-based forecasting and iterative neural network) and with two baselines. The results show that MLWNN is a promising approach for one day ahead electricity load forecasting.
An efficient structured P2P overlay over MANET

In traditional structured P2P file sharing network, a peer establishes and maintains connection with a number of neighbor peers according to its ID. This would lead to mismatch between the overlay and physical network in mobile ad hoc network (MANET) due to unique characteristics of MANET. We propose an approach to construct an efficient structured P2P overlay over MANET using underlying OLSR routing. One of the peers in the P2P network is used as a root-peer to connect all peers. Each peer maintains connection with physically closer neighbor peers such that it can reach the root-peer. The peer constructs a minimum-spanning tree consisting of itself, its directly connected neighbor peers and 2-hop away neighbor peers to remove far away redundant links and to build an overlay closer to the physical network. This structure of interconnection among the peers is used to systematically distribute the identifier space among the peers for which a peer is responsible. We can show by simulation that our approach performs better in comparison with the existing approach.
A low kickback noise and offset calibrated dynamic comparator for 2B/C SAR ADC

A low kickback noise and offset calibrated dynamic comparator used in a high speed capacitive 2-bit-per-cycle (2b/C) SAR ADC was presented. This paper discusses the sources of kickback noise and offset voltage of the dynamic comparator. And then the proposed comparator decreases the kickback noise to the range without affecting the performance of comparator. The next, a calibration unit is shown to decrease the offset voltage to σ± 0.5 mV with the range σ± 16 mV to meet the demand of the 8-b 2b/C SAR ADC. The proposed comparator is composed and simulated in 65 nm technology. The proposed comparator has 0.01 mV kickback noise at 7.9 mV differential input voltage and the calibration unit could calibrate the offset to -0.4±0.5 mV at 66 times Monte-Carlo simulations.
A Column Generation Approach to Scheduling of Real-Time Networks

We present an algorithm based on column generation for the real-time scheduling problem of allocating periodic tasks to electronic control units in multiple subsystems connected by a global bus. The allocation has to ensure that tasks can be scheduled, and messages between tasks in different subsystems can be transmitted over the global bus and meet their deadlines. Also tasks and messages occurring in a task chain must be scheduled in a way such that the sequence of execution meets their endto-end deadline. We show that our approach computes the optimal allocation in our model and due to the column generation approach early provides lower bounds on the optimal value.
Noise Attenuation for Seismic Signal by Non-Linear Thresholding in Curvelet Domain

Noise has traditionally been suppressed or eliminated in seismic data sets by the use of Fourier analysis filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they produce undesirable effects when denoising features of moderate to large amplitude and spatial extent, especially for the low S/N data. In this paper, a new method of de-noise in curvelet domain with non-linear thresholding is proposed: on the basis of curvelet multi-scale decomposition in good approximation of the curve variation characteristics, the author used non-linear threshold to address seismic data in curvelet domain. Through calculation of seismic data, we find out the method can suppress the random noise effectively while the effective wave can be maintained, the signal to noise ratio of the result is higher than the traditional method's. At the same time, it overcomes the drawback that the conventional filtering approach may affect the effective wave when suppressing noise.
Structural and proteomic analyses reveal regional brain differences during honeybee aging

SUMMARY Among insects, learning is particularly well studied in the fruit fly Drosophila melanogaster and the honeybee Apis mellifera. A senescence-dependent decline in classic pavlovian conditioning is demonstrated for both species. To understand how aging affects learning, genetic approaches used with Drosophila can benefit from complementary studies in Apis. Specifically, honeybees have a larger brain size allowing for compartment-specific approaches, and a unique life-history plasticity. They usually perform within-nest tasks early in life (nest bees) and later they collect food (foragers). Senescence of learning performance is a function of the bees' foraging duration but underlying causes are poorly understood. As cognitive aging is commonly associated with structural and biochemical changes in the brain, we hypothesized that brain areas implicated in learning change in synaptic and biochemical composition with increased foraging duration. First, we used synapse-specific immunohistochemistry and proteomics to screen for alterations in the calyx region of the mushroom body, a key structure for memory formation. Using proteomics, we next profiled the central brain, which comprises all higher-order integration centers. We show that, with increased foraging duration, levels of kinases, synaptic- and neuronal growth-related proteins decline in the central brain while the calyx region remains intact both in structure and biochemistry. We suggest that proteome-level changes within major anatomical sites of memory formation other than the calyx region could be central to learning dysfunction. These include large compartments of the central brain, such as the mushroom body's output regions and the antennal lobes. Our data provide novel information toward heterogeneity in the aging insect brain, and demonstrate advantages of the honeybee for invertebrate neurogerontological research.
A Variable Neighborhood Search Algorithm for Continuous Location Routing Problem with Pickup and Delivery

Location Routing Problem (LRP) is combination of Facility location Problem and Vehicle Routing Problem. The classic LRP considers a discrete number of locations as possible candidates for facilities locations. However, the continuous location routing problem (CLRP) as an extension to the classic LRP allows any location for the facilities and thus the solution space is continuous. There is a very scarce literature available for CLRP. In this paper, the CLRP with pickup and delivery is first formulated and to solve the problem, a Variable Neighborhood Search algorithm is used considering different neighborhoods and acceptation levels. Problems from small to large size can be solved and the experimentation results are promising.