Miracle-dz/UniFAR-FTUs huggingface dataset:
TLP-NEGCN: Temporal Link Prediction via Network Embedding and Graph Convolutional Networks

—Temporal link prediction (TLP) is a prominent problem in network analysis that focuses on predicting the existence of future connections or relationships between entities in a dynamic network over time. The predictive capabilities of existing models of TLP are often constrained due to their difficulty in adapting to the changes in dynamic network structures over time. In this paper, an improved temporal link prediction model, denoted as TLP-NEGCN, is introduced by leveraging network embedding, graph convolutional networks (GCN), and bidirectional long short-term memory (BiLSTM). This integration provides a robust model of TLP that leverages historical network structures and captures temporal dynamics leading to improved performances. We employ graph embedding with self-clustering (GEMSEC) to create lower-dimensional vector representations for all nodes of the network at the initial timestamps. The node embeddings are fed into an iterative training process using GCNs across times-tamps in the dataset. This process enhances the node embeddings by capturing the network’s temporal dynamics and integrating neighborhood information. We obtain edge embeddings by concatenating the node embeddings of the end nodes of each edge, encapsulating the information about the relationships between nodes in the network. Subsequently, these edge embeddings are processed through a BiLSTM architecture to forecast upcoming links in the network. The performance of the proposed model is compared against several baselines and contemporary temporal link prediction models on various real-life temporal datasets. The obtained results based on various evaluation metrics demonstrate the superiority of the proposed work.
TANSEN: A QUERY-BY-HUMMING BASED MUSIC RETRIEVAL SYSTEM

Music information retrieval is a field of rapidly growing commercial interest. This paper describes TANSEN, a query-by-humming based music retrieval system under development at IIT, Bombay. Named after the legendary musician (and a tenuous acronym for “TA-Note Song ExtractorNavigator), the system is designed to accept acoustic queries in the form of sung fragments, to search a database of Indian film songs. Algorithms for the extraction of melody from the query signal, and pattern matching for search and retrieval from the database are presented. The user interface is described, and experimental results obtained on a prototype version are reported.
Multiuser channel estimation for detection of cochannel signals

Estimation of the channel impulse responses of multiple cochannel users is a key requirement of all multiuser detection and interference cancellation techniques, though little attention has been paid to subject in the context of TDMA systems. This paper addresses a pilot-based technique for multiuser channel estimation in a TDMA system, and makes several new contributions: it allows for time variation of the channels within and between training sequences, it accounts for colouration of the sampled noise sequence as well as correlation between the channel taps, and it considers users to be asynchronous resulting in a technique whereby explicit timing recovery is unnecessary. In addition, it addresses selection of appropriate training sequences.
In Vivo Dose-related Receptor Binding of the Vitamin D Analogue [3H]-1,25-dihydroxy-22-oxavitamin D3 (OCT) in Rat Parathyroid, Kidney Distal and Proximal Tubules, Duodenum, and Skin, Studied by Quantitative Receptor Autoradiography

1,25-Dihydroxy-22-oxavitamin D3 (OCT) is a new synthetic analogue of 1,25(OH)2D3 with a low calcemic effect. This study utilized quantitative receptor autoradiography to determine the dose-related receptor binding and saturation among the vitamin D target cells: parathyroid chief cells, kidney distal and proximal tubule epithelium, duodenal absorptive epithelium, and epidermal keratinocytes. Rats were injected with 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, or 16.0 μg/kg bw of [26-3H]-OCT and sacrificed 1 hr afterwards. Then autoradiographs were prepared under identical conditions. In these target cells, nuclear uptake of radioactivity increased with dose and then achieved a plateau. However, their saturation doses showed differences: parathyroid chief cells 1-2 μg; duodenal absorptive epithelium, distal tubule epithelium, and epidermal keratinocytes 4-6 μg; proximal tubule epithelium 8 μg (per kg bw). In contrast, in nontarget cells, such as liver and duodenal smooth muscle, radioactivity did not concentrate in the nuclei but increased in the cytoplasm with dose, without plateauing. These results provide the first information on the relative saturabilities of various target cell populations with a vitamin D ligand. Parathyroid chief cells required the relatively lowest receptor saturation dose. This suggests a high sensitivity and response to OCT treatment with related therapeutic potential for the regulation of parathyroid function.
Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual’s cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable “patients” demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.
Hair meshes

Despite the visual importance of hair and the attention paid to hair modeling in the graphics research, modeling realistic hair still remains a very challenging task that can be performed by very few artists. In this paper we present hair meshes, a new method for modeling hair that aims to bring hair modeling as close as possible to modeling polygonal surfaces. This new approach provides artists with direct control of the overall shape of the hair, giving them the ability to model the exact hair shape they desire. We use the hair mesh structure for modeling the hair volume with topological constraints that allow us to automatically and uniquely trace the path of individual hair strands through this volume. We also define a set of topological operations for creating hair meshes that maintain these constraints. Furthermore, we provide a method for hiding the volumetric structure of the hair mesh from the end user, thus allowing artists to concentrate on manipulating the outer surface of the hair as a polygonal surface. We explain and show examples of how hair meshes can be used to generate individual hair strands for a wide variety of realistic hair styles.
Transportation application of social media: Travel mode extraction

At the present time, social media is not only used for connecting people in a virtual environment, but is also considered as a reach source of information for organizations and public service agencies to facilitate their policy and decision making processes. As an example, such crowdsourced data can be considered as a complementary source for analyzing people choices. In this study, we attempt to show how social media data can be used (and utilized) in order to extract travel mode choice which can be used as complementary source of information to improve traditional costly methods such as House Travel Surveys (HTS). The contents of Twitter data posted in Melbourne metropolitan areas have been analyzed to determine travel mode choices information. The results show, walking and driving modes are the most frequent travel modes extracted from Twitter data while public mode of transportations such as bus and taxi are rarely detected. Future research is required to extend this approach by considering and validating socio-demographic metrics of social media users so as to utilize social media data as complementing source of information for HTS.
Forest Biomass and Paper Industry, a Pathway to Green Biofuels

The components of wood, celluloses, hemicelluloses and lignin, can be separated and converted into new non paper products. This paper treats the specific case of lignin and hemicellulose extraction from a Kraft pulping process. Two processes have been investigated for the extraction of lignin from black liquor. One is an enhanced version of precipitation of lignin under low pH conditions achieved by carbonation of Kraft black liquor. The second process focuses on the acidification of the black liquor by electrodialysis with a bipolar membrane. This process produces clean lignin and valuable caustic soda. The extraction of hemicelluloses from wood chips and their partitioning into a mixture of C5 and C6 sugars can be accomplished by a two-step hydrolysis. The sugars can then be converted into a large number of derivatives. The case of fermentation into butanol is presented. The energy intensification of the site eliminates the requirement for fossil fuel thus enhancing the feasibility of the green integrated forest biorefinery.
Towards Increasing Test Compaction Abilities of SAT-based ATPG through Fault Detection Constraints

Automatic Test Pattern Generation (ATPG) based on Boolean Satisfiability (SAT) is a robust alternative to classical structural ATPG. Due to the powerful reasoning engines of modern SAT solvers, SAT-based algorithms typically provide a high test coverage because of the ability to reliably classify hardto-detect faults. However, a weak point of SAT-based ATPG is the test compaction ability. In this paper, we propose a new methodology which combines the classical SAT-based ATPG formulation with additional fault detection constraints resulting in a pseudoBoolean optimization problem. This leads to an increasing test compaction ability of SAT-based ATPG. Experiments show that the resulting test set generated by pure SAT-based ATPG without any test compaction techniques can significantly be decreased by up to 49%.
Investigation on the Thermal Performance of Green Facade in Tropical Climate Based on the Modelling Experiment

This study presents an in situ experimental study of buildings covered by green facades in the tropical country of Indonesia. A scaled model called house miniature was used as measurement object. The aim of this study is to examine the influence of green facades on the thermal performance of buildings. Data measurement is recorded during rainy season where the average of humidity is 69.35% and the average of ambience temperature is 24.48◦C. Considering overall thermal performance, model with green facade has lower surface temperature and indoor air temperature than model without green facade. On the surface temperature, the differences with model without green facade are 6◦C for exterior facade and 6.3◦C for interior facade. Compared to model without green facade, in the morning, indoor air temperature of model with green facade tends to increase up to 25.6◦C. Meanwhile, in the afternoon until evening, indoor air temperature tends to decrease up to 25.6◦C. Thus, it is beneficial to create indoor thermal comfort. However, green facade will produce higher humidity in the model and becomes a disadvantage. Based on the data measurement, indoor air humidity of model with green facade is 42.93% higher than model without green facade. This condition can be a reason of discomfort for building occupants.
A hierarchical access control model for video database systems

Content-based video database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient video analysis for supporting semantic visual concept representation; (b) effective video database indexing structure; (c) the development of suitable video database models; and (d) the development of access control models tailored to the characteristics of video data. In this paper, we propose a novel approach to support multilevel access control in video databases. Our access control technique combines a video database indexing mechanism with a hierarchical organization of visual concepts (i.e., video database indexing units), so that different classes of users can access different video elements or even the same video element with different quality levels according to their permissions. These video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a video scene, a video shot, a video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel video access units. We also propose a hierarchical video database indexing technique to support our multilevel video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to video elements. Moreover, the application of our multilevel video database modeling, representation, and indexing for MPEG-7 is discussed.
Behaviour of tracer diffusion in simple atmospheric boundary layer models

1-D profiles and time series from an idealised atmospheric boundary layer model are presented, which show agreement with boundary layer measurements of polar NO x . Diffusion models are increasingly being used as the framework for studying tropospheric air chemistry dynamics. Models based on standard boundary layer diffusivity profiles have an intrinsic behaviour that is not necessarily intuitive, due to the variation of turbulent diffusivity with height. The simple model presented captures the essence of the evolution of a trace gas released at the surface, and thereby provides both a programming and a conceptual tool in the analysis of observed trace gas evolution. A time scale inherent in the model can be tuned by fitting model time series to observations. This scale is then applicable to the more physically simple but chemically complex zeroth order or box models of chemical interactions.
Falls and health status in elderly women following second eye cataract surgery: a randomised controlled trial.

OBJECTIVE first eye cataract surgery reduces the risk of falling. Most cataracts in elderly people are bilateral. Some binocular functions (e.g. stereopsis) are associated with falls. We aimed to determine if second eye cataract surgery reduces the risk of falling and to measure associated health gain. STUDY DESIGN AND SETTING we randomised 239 women over 70, who had been referred to a hospital ophthalmology department, with one unoperated cataract, to expedited (approximately 4 weeks) or routine (12 months wait) surgery. Falls were ascertained by diary, with follow-up every 3 months for 12 months. We measured health status after 6 months. RESULTS visual function (especially stereopsis) improved in the operated group. Over 12 months follow-up, 48 (40%) operated participants fell at least once, 22 (18%) fell more than once. Forty-one (34%) unoperated participants fell at least once, 22 (18%) fell more than once. Rate of falling was reduced by 32% in the operated group, but this was not statistically significant (rate ratio 0.68, 95% CI 0.39, 1.19, P = 0.18). Confidence, visual disability and handicap all improved in the operated compared with the control group. CONCLUSION second eye cataract surgery improves visual disability and general health status. The effect on rate of falling remains uncertain.
Genetic and environmental influences on obsessive-compulsive symptoms in adults: a population-based twin-family study

Abstract Background The contribution of genetic factors to obsessive-compulsive (OC) symptoms has not been examined using a large population-based sample of adults. Furthermore, the extent to which there are qualitative and quantitative differences in genetic architecture between men and women with OC symptoms has not been elucidated. Method We obtained the Young Adult Self Report Obsessive-Compulsive Scale (YASR-OCS) from a group of 5893 monozygotic (MZ) and dizygotic (DZ) twins, and 1304 additional siblings from the population-based Netherlands Twin Register. Structural equation modelling was used to decompose the variation in OC behaviour into genetic and environmental components and analyse quantitative and qualitative sex differences. Results Familial resemblance was the same for DZ twins and non-twin siblings, which means that there was no evidence for a special twin environment. The same genetic risk factors for OC behaviour were expressed in men and women. Depending on the choice of fit index, we found small (39% for men and 50% for women) or no sex differences (47% for both men and women) in heritability. The remaining variance in liability was due to individual-specific environment. Conclusions OC behaviour showed a moderate heritability. At most, small quantitative sex differences were found in the genetic architecture of OC behaviour, and no qualitative sex differences.
Knowledge compilation using constraint inheritance

Abstract Design knowledge is continually refined and expanded through experience. This research is concerned with design knowledge expressed as constraints. A simple learning mechanism simulates an expert designer's ability to incrementally adjust her knowledge when presented with slightly new problems. In response to unsatisfied expectations during the design process the system will examine its general knowledge about the design artifact, discover some relevant constraining knowledge, and convert that knowledge into a design constraint for future use. This process, referred to as constraint inheritance, should automatically improve the problem-solving performance.
Handling derivative filterbank features in bounded-marginalization-based missing data automatic speech recognition

This paper extends the familiar missing-data bounded-marginalization technique from static to dynamic ﬁlterbank features for noise robust automatic speech recognition. Based on a well-known theorem from Statistics it is shown how the reliability of derivative ﬁlterbank features can be expressed in form of a probability density function. As another contribution, the corresponding HMM state emission likelihood equation (bounded-marginalization rule) for dynamic features is derived in closed-form. On the CHiME corpus the new approach showed a superior accuracy compared to previously proposed heuristics for handling missing dynamic features. To the author’s best knowledge, the achieved average accuracy of 92.58% is the best result so far reported for the 2011 CHiME Challenge task.
The Implementation Of Machine Learning In Demand Forecasting : A Review Of Method Used In Demand Forecasting With Machine Learning

Demand Forecasting is essentials in making production decisions. Demand forecasting accuracy affects supply chain management and can reduce its costs. The development of information technology, especially artificial intelligence, has many benefits in many industrial sectors. The development of artificial intelligence is also applied to demand forecasting. The development of Artificial Intelligence technology in forecasting can produce better accuracy than conventional methods that do not use Artificial Intelligence. The use of machine learning in demand forecasting is in various industrial sectors ranging from small-scale industry to large-scale industry. This article will discuss research on the use of machine learning in demand forecasting for the things discussed, including machine learning models, data processing methods, and research variables. The purpose of this review is to see a comparison of the accuracy of each model, method, and variable used in demand forecasting using machine learning. The results of the review show that the characteristics of different product fluctuations require a different demand forecasting model approach. An appropriate approach can produce higher forecasting accuracy. Mistake in choosing a demand forecasting model can reduce the accuracy of demand forecasting. The demand forecasting model must also need to be updated to improve accuracy.
Line based reduced memory, wavelet image compression

In this work we propose a novel algorithm for wavelet based image compression with very low memory requirements. The wavelet transform is performed progressively and we only require that a reduced number of lines from the original image be stored at any given time. The result of the wavelet transform is the same as if we were operating on the whole image, the only difference being that the coefficients of different subbands are generated in an interleaved fashion. We begin encoding the (interleaved) wavelet coefficients as soon as they become available. We classify each new coefficient in one of several classes, each corresponding to a different probability model, with the models being adapted on the fly for each image. Our scheme is fully backward adaptive and it relies only on coefficients that have already been transmitted. Our experiments demonstrate that our coder is still very competitive with respect to similar state-of-the-art coders. It is noted that schemes based on zero trees or bit plane encoding basically require the whole image to be transformed (or else have to be implemented using tiling). The features of the algorithm make it well suited for a low memory mode coding within the emerging JPEG2000 standard.
Diagnosing and Management of Middle Mesial Canal in Mandibular Molar Using Cone Beam Computerized Tomography-A Case Report

The main objective of root canal treatment is the thorough mechanical and chemical cleansing of the entire pulp space followed by complete obturation with inert filling material. The root canal treatment of a mandibular molar with aberrant canal configuration can be diagnostically and technically challenging. This clinical case report describes the management of the mandibular molar with three separate mesial canals including middle mesial canal, in which the diagnosis was confirmed with the help of cone beam computerized tomography and it also highlights the management of the unusual canal morphology using CBCT.