Miracle-dz/UniFAR-FTUs huggingface dataset:
IEEE 802.11 downlink traffic shaping scheme for multi-user service enhancement

In IEEE 802.11 wireless LAN (WLAN), due to the combination of a first-in-first-out (FIFO) transmission queue and the retransmission policy in the serving access point (AP), a single station that is moving away from the AP, e.g., thus handing off to a neighbouring AP, can have a significant negative impact on the services other users receive in the same basic service set (BSS). In this paper, we present a traffic shaping algorithm to control this undesirable effect. Experimental results show that our algorithm manages to maintain stable throughput levels in networking applications that otherwise would have experienced high throughput degradation. This algorithm constitutes an innovative implementation of a traffic shaper that can be easily adopted by the current state-of-the-art IEEE 802.11 AP products to enhance the performance of WLANs, since the algorithm can be implemented in the device driver and no specific modification of hardware is needed.
TSD: Detecting Sybil Accounts in Twitter

Fake identities and user accounts (also called "Sybils") in online communities represent today a treasure for adversaries to spread fake product reviews, malware and spam on social networks, and Astroturf political campaigns. State-of-the-art in the defense mechanisms includes Automated Turing Tests (ATTs such as CAPTCHAs) and graph-based Sybil detectors. Sybil detectors in social networks leverage the assumption that Sybils will find it hard to befriend real users which leads to Sybils being connected to each other forming strongly connected sub graphs that can be detected using graph theory. However, the large majority of Sybils are in fact successful in integrating themselves into real user communities (such as the case in Twitter and Facebook). In this paper, we first study and compare the current detection mechanisms of Sybil accounts. We also explore various types of Twitter Sybil accounts detection features with the objective of building an effective and practical classifier. In order to build and evaluate our classifier, we collect and manually label a dataset of twitter accounts, including human users, bots, and hybrid (i.e., Tweets are posted by both human and bots). We believe this Twitter Sybils corpus will help researchers in conducting sound measurement studies. We also develop a browser plug-in (that we call Twitter Sybils Detector or TSD for short) that utilizes our classifier and warns the user about possible Sybil accounts before accessing them, upon clicking on a Twitter account.
Comparative analysis of human NK cell activation induced by NKG2D and natural cytotoxicity receptors

NKG2D and natural cytotoxicity receptors (NCR) are essential recognition structures that mediate NK cell activation. NKG2D and NCR signaling is achieved through membrane association with signaling adaptors. The adaptors that associate with NCR — such as CD3ζ, FcRγ and KARAP/DAP12 — bear intracytoplasmic immunoreceptor tyrosine‐based activation motifs that activate Syk protein tyrosine kinases. Human NKG2D associates with the DAP10 transmembrane adaptor, which bears a YxxM motif and activates the phosphatidylinositol 3‐kinase pathway. In the mouse, a short NKG2D‐S isoform, generated by Nkg2d alternative splicing, can associate with either DAP10 or KARAP/DAP12. Here, we report that neither short human NKG2D alternative transcripts nor NKG2D association with KARAP/DAP12 was detected in activated human NK cells. Despite these results, NK cell triggering by both recombinant soluble NKG2D ligands (MICA and ULBP‐1) and anti‐NCR cross‐linking antibodies inducedsimilar CD25 expression, NK cell proliferation and cytokine production. In contrast, NKG2D triggering by anti‐NKG2D antibodies did not lead to any detectable activation signals. These data thus show that target recognition via NKG2D or NCR triggers all aspects of NK activation, and pave the way for further dissection of the signaling pathways induced by NK cell recognition of ULBP‐1 and MICA.
Inferring Causal Direction from Relational Data

Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d.), but little is understood about this problem in the more general relational setting with multiple types of interacting entities. This work examines the task of inferring the causal direction of peer dependence in relational data. We show that, in contrast to the i.i. d. setting, the direction of peer dependence can be inferred using simple procedures, regardless of the form of the underlying distribution, and we provide a theoretical characterization on the identifiability of direction. We then examine the conditions under which the presence of confounding can be detected. Finally, we demonstrate the efficacy of the proposed methods with synthetic experiments, and we provide an application on real-world data.
AKANE System : Protein-Protein Interaction 1 AKANE System : Protein-Protein Interaction Pairs in the BioCreAtIvE 2 Challenge , PPI-IPS subtask

This report summarizes the participation of the Tsujii-lab group in the 2006 BioCreative2 text mining challenge. It describes the systems used, the results attained, and the lessons learned. The basic idea was to see how well the AKANE system could perform on a full-text ProteinProtein Interaction (PPI) Information Extraction (IE) task. AKANE system is a recently developed, sentence-level PPI system that achieved a 57.3 F-score on the AImed corpus. In order to use the AKANE system for the BioCreative task, the given training data had to be preprocessed. The BioCreative training data contained just a list of interacting protein pair identifiers for each given full-text article, while the expected input for the AKANE system is annotated sentences like in the AImed corpus. In order to transform the full-text articles into AImed sentence-level annotations, the text was first stripped of all HTML coding to get a plain text representation. Then, each mention of protein names were tagged by a Named Entity Recognizer (NER), and all interacting and co-occurring pairs in single sentences were used for training. A pipeline architecture was made to deal with each of these challenges. Some postprocessing was also necessary, in order to transform the results from the AKANE system into the expected format for the BioCreative2 challenge. The postprocessing included filtering and ranking the results, and balancing precision and recall to maximize the F-score.
Style-by-Demonstration: Using Broomsticks and Tangibles to Show Robots How to Follow People

Robots are poised to enter our everyday environments such as our homes and offices. These contexts present unique human demands, including questions of the style and personality of the robot's actions. Style-oriented characteristics are difficult to define programmatically, and as such, are often out of reach from the designers involved in creating robotic technologies. This problem is particularly prominent for a robot’s interactive behaviors, those that must react accordingly to dynamic environments and actions of people. In this paper, we present the concept of programming robotic style by demonstration through the use of broomsticks and tangibles, such that non-technical designers can directly create the style of actions using their existing skill sets. We developed a working system as a proof-of-concept, and present two novel interfaces for directly demonstrating the style of motions to robots. Our current focus is on the style of a robot following a person, but we envision that simple physical interfaces like ours can be used by non-technical people to design the style of a wide range of robotic behaviors.
Genotyping of Endocervical Chlamydia trachomatis Strains and Detection of Serological Markers of Acute and Chronic Inflammation in Their Host

Background Chlamydia trachomatis (C. trachomatis) is the most prevalent cause of bacterial sexually transmitted infections (STI) recognized throughout the world. The aim of this study is to determine different genotypes of genital C. trachomatis and the association between the serological markers of inflammation and genotypes of C. trachomatis in sexually active women (n=80) attending Shahid Beheshti Hospital in Isfahan, Iran. Materials and Methods In this descriptive study, endocervical swabs were collected from 80 women. There were 17 endocervical samples that showed positivity for C. trachomatis by plasmid polymerase chain reaction (PCR) using KL1 and KL2 primers. The omp1 gene was directly amplified in 17 plasmid PCR positive samples and was used to differentiate the clinical genotypes by omp1 gene PCR-restriction fragment length polymorphism (PCR-RFLP). The levels of IgG and IgA specific to C. trachmatis and C-reactive protein (CRP) were evaluated. Results Based on restriction-digestion patterns, four genotypes were identified. Genotypes E (35.3%) and F (35.3%) were the most prevalent, followed by D/Da (23.5%) and K (5.9%). There was no significant association between genotypes and the presence of IgG and CRP. Patients infected with genotype E showed a serological marker of chronic inflammation, i.e. IgA seropositivity, significantly more than patients infected with other genotypes (p=0.042). Conclusion Nested PCR could increase the sensitivity of omp1 amplification. Based on the presence of IgA, chronic C. trachomatis infections were observed more frequently among genotype E-infected patients in our population.
Rapid prototyping of the sphere decoder for MIMO systems

The use of multiple input-multiple output (MIMO) technology is rapidly becoming the new frontier of wireless communication systems increasing their capacity and spectral efficiency. In order to validate this technology from an implementation point of view, field-programmable gate arrays (FPGAs), with their high level of parallelism, high densities and embedded multipliers, are a suitable platform for the study and prototyping of MIMO algorithms. This paper presents an FPGA implementation of the sphere decoder (SD) for MIMO detection. This algorithm provides optimal maximum likelihood (ML) performance with reduced complexity, compared to the maximum likelihood detector (MLD). (7 pages)
Multi Agent-Based Environmental Landscape (Mabel) - An Artificial Intelligence Simulation Model: Some Early Assessments

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.
Would Smaller Classes Help Close the Black-White Achievement Gap?

Turri and Jayne Zaharias for providing data, and David Card and Jens Ludwig for helpful discussions; they are not responsible for any mistakes we may have made. Executive Summary This paper examines the effect of reducing class-size on student achievement, with particular attention to differential effects by race. A review of the literature suggests that low-income and black students tend to benefit more from attending a smaller class than white students. We extend the literature by providing new results from a long-term follow-up of students who participated in Tennessee's Project STAR. Project STAR was an experiment that randomly assigned 11,600 elementary school students and their teachers to a small class (target of 13-17 students), regular-size class (22-25 students) or regular-size class wit h a teacher-aide. The experiment began with the wave of students who entered kindergarten in 1985, and lasted for four years. After third grade, all students returned to regular-size classes. We analyze the effect of past attendance in a small class on standardized test scores through the eighth grade, on whether students took the ACT or SAT college entrance exam, performance on the ACT or SAT exam, criminal conviction rates, and teen birth rates. The results indicate that, while students are in small classes, average test scores increase by 7-10 percentile points for black students and by 3-4 percentile points for white students. After all students are returned to regular-size classes in 4 th grade, the gains from having attended a small class fall to about 5 points for black students and 1.5 points for white students, and persist at around that level. If all students were in a small class in grades K-3 for one to four years, we estimate that the black-white test-score gap would fall by 38 percent in grades K-3, and by 15 percent thereafter. Combining estimates of the effect of small classes on 3 rd grade test scores from the STAR experiment with national trends in the pupil-teacher ratio for black and white students since 1971, we find that historical movements in the pupil-teacher ratio can account for almost all of the narrowing of the black-white test score gap as measured by the National Assessment of Educational Progress (NAEP) exam. We also find that having attended a small class compared to regular-size class raises the likelihood that black students take the ACT or SAT college entrance exam from 31.8 to 41.3 percent, and …
HST study of the LMC compact star-forming region N83B ?

High resolution imaging with the Hubble Space Telescope has uncovered the thus far hidden stellar content and the nebular features of the high excitation compact H ii region N83B in the Large Magellanic Cloud (LMC). We discover that the H ii region is powered by the most recent massive starburst in the OB association LH 5 and the burst has created about 20 blue stars spread over30 00 on the sky (7.5 pc). Globally N83B displays a turbulent environment typical of newborn massive star formation sites. It contains an impressive ridge, likely created by a shock and a cavity with an estimated age of only 30 000 yr, sculpted in the ionized gas by the powerful winds of massive stars. The observations bring to light two compact H ii blobs, N83B-1 and N83B-2, and a small arc-nebula, N83B-3, lying inside the larger H ii region. N83B-1, only 2 00 . 8 (0.7 pc) across, is the brightest and most excited part of N83B. It harbors the presumably hottest star of the burst and is also strongly aected by dust with an extinction of AV =2 :5 mag. The second blob, N83B-2, is even more compact, with a size of only1 00 (0.3 pc). All three features are formed in the border zone between the molecular cloud and the ionized gas possibly in a sequential process triggered by the ionization front of an older H ii region. Our HST imaging presents an interesting and rare opportunity to observe details in the morphology of star formation on very small spatial scales in the LMC which are in agreement with the concept of the fractal structure of molecular star-forming clouds. A scenario which supports hierarchical massive star formation in the LMC OB association LH 5 is presented.
The parallel processing of spatial selection for very large geo-spatial databases

Earth science (ES) applications handle very large geo-spatial data sets and interactive response time is required by its query processing. Spatial selection is one of the very important basic operations for geo-spatial databases. It retrieves all the objects that intersect with a given point or rectangle. We present a novel approach for the parallel processing of spatial selection of very large geo-spatial databases using partitioned parallelism. To evaluate this approach, we use the Extended Sequoia 2000 benchmark, which has real world data and real queries. In addition, we use an actual object database management system, ShusseUo, which we developed previously. The experimental results of parallel processing of spatial selection show good speed-up.
Semantic Classification of Noun Phrases Using Web Counts and Learning Algorithms

This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz's (2003) dataset of 600 modifier-noun compounds. We find that by using a Support Vector Machine classifier we can obtain better performance on this dataset than a current state-of-the-art system; even with a relatively small set of prepositional joining terms.
Significant differences when using creatinine, modification of diet in renal disease, or cystatin C for estimating glomerular filtration rate in ICU patients

Abstract Background. Renal dysfunction is associated with increased morbidity and mortality in intensive care patients. In most cases the glomerular filtration rate (GFR) is estimated based on serum creatinine and the Modification of Diet in Renal Disease (MDRD) formula, but cystatin C-estimated GFR is being used increasingly. The aim of this study was to compare creatinine and MDRD and cystatin C-estimated GFR in intensive care patients. Methods. Retrospective observational study was performed, on patients treated within the general intensive care unit (ICU) during 2004–2006, in a Swedish university hospital. Results. GFR markers are frequently ordered in the ICU; 92% of the patient test results had cystatin C-estimated GFR (eGFRcystatinC) ≤ 80 mL/min/1.73 m2, 75% had eGFR ≤ 50 mL/min/1.73 m2, and 30% had eGFR ≤ 20 mL/min/1.73 m2. In contrast, only 46% of the patients had reduced renal function assessed by plasma creatinine alone, and only 47% had eGFRMDRD ≤ 80 mL/min/1.73 m2. The mean difference between eGFRMDRD and eGFRcystatinC was 39 mL/min/1.73 m2 for eGFRcystatinC values ≤ 60 mL/min/1.73 m2. Conclusions. GFR is commonly assessed in the ICU. Cystatin C-estimated GFR yields markedly lower GFR results than plasma creatinine and eGFRMDRD. Many pharmaceuticals are eliminated by the kidney, and their dosage is adjusted for kidney function. Thus, the differences in GFR estimates by the methods used indicate that the GFR method used in the intensive care unit may influence the treatment.
DTI Fiber Clustering and Cross-subject Cluster Analysis

S. Zhang, D. H. Laidlaw Computer Science Department, Brown University, Providence, RI, United States Introduction – We report on unsupervised clustering of tractography paths and automated matching of the resulting clusters across two brains. DTI tractography methods generate paths that correlate well with large WM structures. However, it is not clear how reliably they can identify anatomical tracts. Recently, several approaches have been proposed to cluster these paths [1,2,3]. To avoid errors, our approach aims at reliably identifying only relatively large and unambiguous sets of paths; we remove paths that are ambiguous before clustering and only find matches between clusters when they are relatively compelling.
An ICA design of intraday stock prediction models with automatic variable selection

Independent component analysis (ICA) provides a mechanism of decomposing non-Gaussian data signals into statistically independent components. In this paper, ICA is used to extract the underlying news factors from intraday stock data. A, prediction algorithm is developed to improve stock index predictions using such extracted "news". Both linear regression model and nonlinear artificial neural network model are proposed to predict stock indexes of Open, Close, High and Low using the ICA extracted "news". These models are compared with models using only raw intraday data as "news". It is demonstrated that ICA helps in extracting market underlying affecting "news", and thus improves the stock prediction accuracy. It shows that the proposed ICA prediction algorithm is a simple to use and versatile algorithm that automatically extracts the most relevant news for different stock index predictions.
Interactions between Obesity-Related Copy Number Variants and Dietary Behaviors in Childhood Obesity

Copy number variants (CNVs) have been implicated as an important genetic marker of obesity, and gene-environment interaction has been found to modulate risk of obesity. To evaluate the associations between CNVs and childhood obesity, as well as the interactions between CNVs and dietary behaviors, we recruited 534 obese children and 508 controls from six cities in China and six candidate CNVs were screened through published genome-wide studies (GWAS) on childhood obesity. We found three loci (10q11.22, 4q25 and 11q11) to be significantly associated with obesity after false discovery rate (FDR) correction (all the p ≤ 0.05). Cumulative effect of the three positive loci was measured by the genetic risk score (GRS), showing a significant relationship with the risk of obesity (Ptrend < 0.001). The OR of obesity increased to 21.38 (95% CI = 21.19–21.55) among the 10q11.22 deletion carriers who had meat-based diets, indicating prominent multiplicative interaction (MI) between deletions of 10q11.22 and preference for a meat-based diet. Simultaneous deletions of 5q13.2 and duplications of 6q14.1 had significant MI with a preference for salty foods. Our results suggested that CNVs may contribute to the genetic susceptibility of childhood obesity, and the CNV-diet interactions modulate the risk of obesity.
99-19 Production and Maintenance of Software Measurement Models

Production and maintenance of measurement models are crucial in the context of an advanced software development organization. The paper suggests a goal-oriented measurement model life cycle that, starting out from the highlevel organizational goals, drives software organizations towards a structured process to deal with measurement models. The life cycle results into an integrated component of the management activities pointed to generate, refine, and fulfill the organizational goals, whereas the final measurement models become items of a purpose fitting measurement plan. For each phase of the life cycle, the main activities, with their inputs and outputs, are identified and some driving templates defined. The paper analyzes their interactions with the other software production and management-related tasks and proposes a possible allocation within an Experience Factory-based software organization.
THE ALCOVE PATH MODEL AND TABLEAUX

The second author and Postnikov have recently constructed a simple combinatorial model for the characters of the irreducible representations of a complex semisimple Lie group, that is referred to as the alcove path model. In this paper we relate the alcove path model to the the semistandard Young tableaux in type A and the KashiwaraNakashima tableaux in type C. More explicitly, we construct bijections between the objects in the alcove path model (certain saturated chains in the Bruhat order on the corresponding Weyl group) and the corresponding tableaux. We show that this bijection preserves the corresponding crystal structures, and we give applications to Demazure characters and basis constructions.