Miracle-dz/UniFAR-FTUs huggingface dataset:
Twister Tries: Approximate Hierarchical Agglomerative Clustering for Average Distance in Linear Time

Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms' scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n2), these algorithms have a space complexity of O(n2), which can be reduced to O(n) if the time complexity is allowed to rise to O(n2 log2n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only linear space. Furthermore, its time complexity is linear in the number of items to be clustered, making it feasible to apply it on a larger scale. We evaluate the approach both analytically and by applying it to several data sets.
Rapid Animation of Laser-scanned Humans

We present a simple and efficient approach to turn laser-scanned human geometry into a realistically moving virtual avatar. Instead of relying on the classical skeleton-based animation pipeline, our method uses a mesh-based Laplacian editing scheme to drive the motion of the scanned model. Our framework elegantly solves the motion retargeting problem and produces realistic non-rigid surface deformation with minimal user interaction. Realistic animations can easily be generated from a variety of input motion descriptions, which we exemplify by applying our method to both marker-free and marker-based motion capture data
A Framework of Dynamic Data Structures for String Processing

In this paper we present DYNAMIC, an open-source C++ library implementing dynamic compressed data structures for string manipulation. Our framework includes useful tools such as searchable partial sums, succinct/gap-encoded bitvectors, and entropy/run-length compressed strings and FM-indexes. We prove close-to-optimal theoretical bounds for the resources used by our structures, and show that our theoretical predictions are empirically tightly verified in practice. To conclude, we turn our attention to applications. We compare the performance of four recently-published compression algorithms implemented using DYNAMIC with those of state-of-the-art tools performing the same task. Our experiments show that algorithms making use of dynamic compressed data structures can be up to three orders of magnitude more space-efficient (albeit slower) than classical ones performing the same tasks.
Development and Testing of the Elements of the Nature Curiosity Scale

In this article I presented a new tool—the Elements of Nature Curiosity Scale (ENCS)—for diagnosing people’s curiosity about hazardous events caused by elements of nature: such as volcanic eruptions, thunderstorms, avalanches, and floods. The validity of the ENCS was evaluated using exploratory and confirmatory factor analyses. The ENCS had adequate fit to a 1-dimensional factor structure. The scale’s reliability and stability over time were also evaluated (Cronbach’s α = .87; test–retest r = .78). The ENCS correlates with openness to experience, sensation seeking, curiosity, and exploration. The results indicate that the ENCS is a valid and reliable tool.
Speaker Matching A Third Professional Project

This report examines a problem faced by Axent Audio Products Limited, a New Zealand speaker manufacturer, in forming stereo speaker pairs from a batch of similar speakers in such a way that the sound quality of the pairs is maximised. The applications of the maximum cardinality algorithm and sum matching algorithm to this problem are discussed in detail. A new heuristic is developed for matching with a lexicographic objective function, and results of applying the heuristic are given.
INTEGRATING ICT WITH EDUCATION: DESIGNING AN EDUCATIONAL COMPUTER GAME FOR TEACHING FUNCTIONS IN UNDERGRADUATE MATHEMATICS

This research seeks to look into the design process that promotes the development of an educational computer game that supports teaching and learning processes. The research specifically looks at the design of an educational computer game for teaching and learning of the topic of functions. The topic is essential in the teaching and learning of Mathematics courses such as Discrete Mathematics, Real Analysis and Calculus among others at Jomo Kenyatta University of Agriculture and Technology (JKUAT) Kenya. The computer game was developed using the Basic Unified process (BUP) which is a streamlined version of the rational unified process (RUP). This is an object oriented methodology mostly used for small projects with few end users. Due to the few numbers of end users we used interview method of data collection to gather requirements for the computer game. A paper prototype was used to validate the requirements. Use cases were used for both analysis and design of the game while Class diagrams and activity diagrams were purely used for the design of the game. Owens’ six top level design anatomy aided in the design of the computer game. The overall computer game design was based on Crawfords’ computer game design sequence model. The well designed and developed game met all its user requirements and was able to facilitate the teaching and learning of functions to Bachelor of Science in Mathematics and Computer Science students who were taking Discrete mathematics in their first year of study at JKUATs’ Taita/Taveta campus. Development of heuristics for measuring interest, fun and motivation are recommendations given to aid in the evaluation of user satisfaction of educational computer games.
D strings and F strings from string loops.

Since the background fields of the string low energy action are supposed to be the long range manifestation of a condensate of strings, the addition of world sheet actions to the low energy effective action needs some string theoretic explanation. In this paper we suggest that this may be understood as being due to string loop effects. We first present arguments using an equation due to Tseytlin and then more rigorously in the particular case of type IIB theory by invoking the Fischler-Susskind effect. The argument provides further justification for SL(2,{ital Z}) duality between {ital D}-strings and {ital F} (fundamental)-strings. In an Appendix we comment on recent attempts to relate the type IIA membrane to the 11-dimensional membrane. {copyright} {ital 1996 The American Physical Society. }
Improving ad relevance in sponsored search

We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs. Our relevance predictions are then applied to multiple sponsored search applications in both offline editorial evaluations and live online user tests. The predicted relevance score is used to improve the quality of the search page in three areas: filtering low quality ads, more accurate ranking for ads, and optimized page placement of ads to reduce prominent placement of low relevance ads. We show significant gains across all three tasks.
Flower Choice and Learning in Foraging Bumblebees: Effects of Variation in Nectar Volume and Concentration

Bees collect food from flowers that differ in morphology, color, and scent. Nectar-seeking foragers can rapidly associate a flower’s cues with its profitability, measured as caloric value or ‘net energy gain,’ and generally develop preferences for more profitable species. If two flower types are equally easy to discover and feed from, differences in profitability will arise from differences in the volume or the sugar concentration of their nectar crops. Although there has been much study of how bees respond to one or the other of these two kinds of nectar variation, few studies have considered both at once. We presented free-foraging bumblebees with two different types of equally rewarding artificial flowers. After a period of familiarization, we made one type more rewarding than the other by increasing its nectar concentration, volume, or both. Bees responded more rapidly to a change in the reward’s sugar concentration than to a change in its volume, even if the profitability differences were approximately equal. Sucrose concentration differences (40% vs. 13%) caused bees to virtually abandon the more dilute flower type, whether both types offered the same volume (2 ll) or the less concentrated reward offered higher volume (7 ll vs. 0.85 ll). When the two types of flower differed only in nectar volume (7 ll vs. 0.85 ll), the less rewarding type continued to receive 22% of the visits. We propose three different hypotheses to explain the stronger response of the bees to changes in sugar concentration: (i) their response threshold to sucrose concentration might change; (ii) less time is needed to assess the concentration of a reward than its volume; and (iii) a smaller sample size may be needed for reliable estimation of profitability when flowers differ in concentration.
An Extension of the Concordet Criterion and Kemeny Orders

The usual Condorcet Criterion says that if an alternative is ranked ahead of all other alternatives by an absolute majority of voters, it should be declared the winner. The following partial extension of this criterion to other ranks is proposed: If an alternative is consistently ranked ahead of another alternative by an absolute majority of voters, it should be ahead in the final ranking. The term "consistently" refers to the absence of cycles in the majority relation involving these two alternatives. If there are cycles, this criterion gives partial orders that can be completed with the Kemeny rule. An algorithm to construct Kemeny orders is presented. It is based on a result saying that a complete Kemeny order over all alternatives can be obtained by splicing together Kemeny orders on the subsets of an admissible partition of the alternatives underlying the Extended Condorcet Criterion. Le critere usuel de Condorcet exige que, si une alternative est classee avant toutes les autres par une majorite de votants, elle devrait etre declaree vainqueur. Une extension partielle de ce critere aux autres rangs est proposee: Si une alternative est classee avant une autre de maniere coherente par une majorite de votants, elle devrait l'etre dans le classement final. La coherence refere a l'absence de cycle dans la relation majoritaire impliquant ces deux alternatives. En cas de cycles, ce critere donne des ordres partiels, qui peuvent etre completes avec la regle de Kemeny. Un algorithme pour la construction des ordres de Kemeny est presente. Il s'appuie sur un resultat affirmant qu'un ordre de Kemeny peut etre obtenu en juxtaposant des ordres de Kemeny sur les sous-ensembles d'une partition des alternatives sous-jacente au critere de Condorcet generalise.
Fuzzy Inference Modeling Methodology for the Simulation of Population Growth

This paper presents the use of fuzzy inference to provide a viable modeling and simulation methodology for the estimation of population growth in any country or region. The study is motivated by the classical complex and time-consuming growth modeling and prediction methods. The related design issues are presented and the fuzzy inference model for population growth is derived. The human social and economic factors which affect the growth and which underly the parameters used in the classical population projection methods are fuzzified. They are then used as inputs to a fuzzy population growth model based on fuzzy inferences so as the population growth rate is evaluated. The fuzzy population model is simulated using an existing CAD tool for fuzzy inference which has been developed and described elsewhere by the authors. The results obtained using different existing defuzzification strategies and a recently introduced one are compared with the actual population growth rates in some countries.
Generation of phase-amplitude coupling of neurophysiological signals in a neural mass model of a cortical column

Phase-amplitude coupling (PAC), the phenomenon where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important neurophysiological indicator of short- and long-range information transmission in the brain. Although recent evidence suggests that PAC might play a functional role during sensorimotor, and cognitive events, the neurobiological mechanisms underlying its generation remain imprecise. Thus, a realistic but simple enough computational model of the phenomenon is needed. Here we propose a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). While experimental studies often focus in only one or two PAC combinations (e.g., theta-gamma or alpha-gamma) our simulations show that the cortical column can generate almost all possible couplings of phases and amplitudes, which are influenced by connectivity parameters, time constants, and external inputs. Furthermore, our simulations suggest that the effective connectivity between neuronal populations can result in the emergence of PAC combinations with frequencies different from the natural frequencies of the oscillators involved. For instance, simulations of oscillators with natural frequencies in the theta, alpha and gamma bands, were able to produce significant PAC combinations involving delta and beta bands.
The Parallel Corpus Approach to Building the Syntactic Tree Transfer Set in the English-to-Vietnamese Machine Translation

Abstract: Recently, with the machine learning trend, most of the machine translation systems on over the world use two syntax tree sets of two relevant languages to learn syntactic tree transfer rules. However, for the English-Vietnamese language pair, this approach is impossible because until now we have not had a Vietnamese syntactic tree set which is correspondent to English one. Building of a very large correspondent Vietnamese syntactic tree set (thousands of trees) requires so much work and take the investment of specialists in linguistics. To take advantage from our available English-Vietnamese Corpus (EVC) which was tagged in word alignment, we choose the SITG (Stochastic Inversion Transduction Grammar) model to construct English-Vietnamese syntactic tree sets automatically. This model is used to parse two languages at the same time and then carry out the syntactic tree transfer. This English-Vietnamese bilingual syntactic tree set is the basic training data to carry out transferring automatically from English syntactic trees to Vietnamese ones by machine learning models. We tested the syntax analysis by comparing over 10,000 sentences in the amount of 500,000 sentences of our English-Vietnamese bilingual corpus and first stage got encouraging result (analyzed about 80%)[5]. We have made use the TBL algorithm (Transformation Based Learning) to carry out automatic transformations from English syntactic trees to Vietnamese ones based on that parallel syntactic tree transfer set[6].
Trash Day: Coordinating Garbage Collection in Distributed Systems

Cloud systems such as Hadoop, Spark and Zookeeper are frequently written in Java or other garbage-collected languages. However, GC-induced pauses can have a significant impact on these workloads. Specifically, GC pauses can reduce throughput for batch workloads, and cause high tail-latencies for interactive applications. In this paper, we show that distributed applications suffer from each node's language runtime system making GC-related decisions independently. We first demonstrate this problem on two widely-used systems (Apache Spark and Apache Cassandra). We then propose solving this problem using a Holistic Runtime System, a distributed language runtime that collectively manages runtime services across multiple nodes. We present initial results to demonstrate that this Holistic GC approach is effective both in reducing the impact of GC pauses on a batch workload, and in improving GC-related tail-latencies in an interactive setting.
Individuals' Interaction with Organizational Knowledge under Innovative and Affective Team Climates: A Multilevel Approach to Knowledge Adoption and Transformation

Although organizational learning can be explained as mutual learning between an organization and the individuals in it, it has been mostly understood from the organization side. To understand how this mutual learning occurs from the side of the actual learners as the learning entity, we study how individuals learn organizational knowledge by adopting and transforming it and how they are influenced by the technical and social subsystems of an organization. From 350 responses within 66 teams from two companies, we investigate the effects of KMS use for strategic decision support and operational support on knowledge adoption and transformation and how innovative and affective climates moderate these relationships. Our findings show that individuals improve their work performance through both knowledge adoption and transformation and that different types of KMS use and different team climates play different roles in shaping knowledge adoption and transformation.
Protein Function Prediction Using Neighbor Counting with Dynamic Threshold from Protein-Protein Interaction Network

In recent years, a large number of proteins of different organisms have been discovered but due to high experimental cost and uncertain time boundary, yet it is not possible to find out all of the functionalities of those proteins. With the recent advent of huge protein-protein interactions, it becomes an opportunity to computationally predict a protein’s functionality based on its interacting partners. In this work, we mainly try to find out a way by which we can predict functionality of a target protein with low computational complexity. We propose a simple approach for protein function prediction based on Classical Neighbor Counting method. We also investigate the functional dependency of a protein to its direct neighbors in the interaction network. We find that when majority of its interacting partners have more experimentally known annotation, then more accurately we can predict a protein’s functionality using Neighbor Counting technique.
Genes Differentially Expressed in Conidia and Hyphae of Aspergillus fumigatus upon Exposure to Human Neutrophils

Background Aspergillus fumigatus is the most common etiologic agent of invasive aspergillosis in immunocompromised patients. Several studies have addressed the mechanism involved in host defense but only few have investigated the pathogen's response to attack by the host cells. To our knowledge, this is the first study that investigates the genes differentially expressed in conidia vs hyphae of A. fumigatus in response to neutrophils from healthy donors as well as from those with chronic granulomatous disease (CGD) which are defective in the production of reactive oxygen species. Methodology/Principal Findings Transcriptional profiles of conidia and hyphae exposed to neutrophils, either from normal donors or from CGD patients, were obtained by using the genome-wide microarray. Upon exposure to either normal or CGD neutrophils, 244 genes were up-regulated in conidia but not in hyphae. Several of these genes are involved in the degradation of fatty acids, peroxisome function and the glyoxylate cycle which suggests that conidia exposed to neutrophils reprogram their metabolism to adjust to the host environment. In addition, the mRNA levels of four genes encoding proteins putatively involved in iron/copper assimilation were found to be higher in conidia and hyphae exposed to normal neutrophils compared to those exposed to CGD neutrophils. Deletants in several of the differentially expressed genes showed phenotypes related to the proposed functions, i.e. deletants of genes involved in fatty acid catabolism showed defective growth on fatty acids and the deletants of iron/copper assimilation showed higher sensitivity to the oxidative agent menadione. None of these deletants, however, showed reduced resistance to neutrophil attack. Conclusion This work reveals the complex response of the fungus to leukocytes, one of the major host factors involved in antifungal defense, and identifies fungal genes that may be involved in establishing or prolonging infections in humans.
Complete Response after Treatment with Neoadjuvant Chemoradiation with Prolonged Chemotherapy for Locally Advanced, Unresectable Adenocarcinoma of the Pancreas

Surgery is the only chance for cure in pancreatic ductal adenocarcinoma. In unresectable, locally advanced pancreatic cancer (LAPC), the National Comprehensive Cancer Network (NCCN) suggests chemotherapy and consideration for radiation in cases of unresectable LAPC. Here we present a rare case of unresectable LAPC with a complete histopathological response after chemoradiation followed by surgical resection. A 54-year-old female presented to our clinic in December 2013 with complaints of abdominal pain and 30-pound weight loss. An MRI demonstrated a mass in the pancreatic body measuring 6.2 × 3.2 cm; biopsy revealed proven ductal adenocarcinoma. Due to splenic vein/artery and contiguous celiac artery encasement, she was deemed surgically unresectable. She was started on FOLFIRINOX therapy (three cycles), intensity modulated radiation to a dose of 54 Gy in 30 fractions concurrent with capecitabine, followed by FOLFIRI, and finally XELIRI. After 8 cycles of ongoing XELIRI completed in March 2015, restaging showed a remarkable decrease in tumor size, along with PET-CT revealing no FDG-avid uptake. She was reevaluated by surgery and taken for definitive resection. Histopathological evaluation demonstrated a complete R0 resection and no residual tumor. Based on this patient and literature review, this strategy demonstrates potential efficacy of neoadjuvant chemoradiation with prolonged chemotherapy, followed by surgery, which may improve outcomes in patients deemed previously unresectable.
An investigation of the relationship between motivational factors andperformance of education staff

This study examined the relationship between motivational factors and the performance of education staff in Miyandoab. The study population was all employees of the education department in Miyandoab in 2012-13 including 90 people and farmers which according to Morgan table 74 employees of the society were selected. Simple random sampling was used to select the sample. The study was descriptive- correlation. The research instrument was a questionnaire that included five components derived from the theoretical and empirical literature and comparative studies set on the Likert scale. Reliability of the questionnaire was calculated 0/83 using Cronbach's alpha. The findings showed a significant direct relationship between moderate elements of authority, participation and decision-making systems, salary system and employee performance and a weak significant relationship between job satisfaction and performance evaluation of staff education in Miyandoab. Generally, motivational factors predict employees’ performance by 59 percent.
Impact de l’audit externe sur la qualité du résultat comptable : cas des entreprises tunisiennes cotées

Cet article vise a examiner l’importance du role de controle exerce par l’audit externe sur la fiabilite des etats financiers dans les entreprises tunisiennes cotees. Plus precisement, nous etudions l’impact de la qualite de l’auditeur externe sur la qualite du resultat comptable. Nous avons choisi la qualite des accruals et la pertinence comme mesure de la qualite du resultat comptable. A partir d’un echantillon de 108 observations sur la periode 2000-2005, nos resultats confirment l’hypothese selon laquelle les « BIG » et le secteur de specialisation de l’auditeur ameliore la qualite du resultat comptable. Les resultats montrent aussi l’existence d’une association significativement positive entre la duree de la relation d’audit et la qualite du resultat comptable.