Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Use of Doppler Shift for Sea Surface Wind Retrieval From SAR

The synthetic aperture radar (SAR) Doppler centroid has been used to estimate the scatter line-of-sight radar velocity. In weak to moderate ocean surface current environment, the SAR Doppler centroid is dominated by the directionality and strength of wave-induced ocean surface displacements. In this paper, we show how this sea state signature can be used to improve surface wind retrieval from SAR. Doppler shifts of C-band radar return signals from the ocean are thoroughly investigated by colocating wind measurements from the ASCAT scatterometer with Doppler centroid anomalies retrieved from Envisat ASAR. An empirical geophysical model function (CDOP) is derived, predicting Doppler shifts at both VV and HH polarization as function of wind speed, radar incidence angle, and wind direction with respect to radar look direction. This function is used into a Bayesian inversion scheme in combination with wind from a priori forecast model and the normalized radar cross section (NRCS). The benefit of Doppler for SAR wind retrieval is shown in complex meteorological situations such as atmospheric fronts or low pressure systems. Using in situ information, validation reveals that this method helps to improve the wind direction retrieval. Uncertainty of the calibration of Doppler shift from Envisat ASAR hampers the inversion scheme in cases where NRCS and model wind are accurate and in close agreement. The method is however very promising with respect of future SAR missions, in particular Sentinel-1, where the Doppler centroid anomaly will be more robustly retrieved.
A Strategy to Identify Dominant Point Mutant Modifiers of a Quantitative Trait

A central goal in the analysis of complex traits is to identify genes that modify a phenotype. Modifiers of a cancer phenotype may act either intrinsically or extrinsically on the salient cell lineage. Germline point mutagenesis by ethylnitrosourea can provide alleles for a gene of interest that include loss-, gain-, or alteration-of-function. Unlike strain polymorphisms, point mutations with heterozygous quantitative phenotypes are detectable in both essential and nonessential genes and are unlinked from other variants that might confound their identification and analysis. This report analyzes strategies seeking quantitative mutational modifiers of ApcMin in the mouse. To identify a quantitative modifier of a phenotype of interest, a cluster of test progeny is needed. The cluster size can be increased as necessary for statistical significance if the founder is a male whose sperm is cryopreserved. A second critical element in this identification is a mapping panel free of polymorphic modifiers of the phenotype, to enable low-resolution mapping followed by targeted resequencing to identify the causative mutation. Here, we describe the development of a panel of six “isogenic mapping partner lines” for C57BL/6J, carrying single-nucleotide markers introduced by mutagenesis. One such derivative, B6.SNVg, shown to be phenotypically neutral in combination with ApcMin, is an appropriate mapping partner to locate induced mutant modifiers of the ApcMin phenotype. The evolved strategy can complement four current major initiatives in the genetic analysis of complex systems: the Genome-wide Association Study; the Collaborative Cross; the Knockout Mouse Project; and The Cancer Genome Atlas.
Adjustable quantizers for joint estimation of location and scale parameters

An adaptive algorithm to estimate jointly unknown location and scale parameters of a sequence of symmetrically distributed independent and identically distributed random variables using quantized measurements from a quantizer with adjustable input gain and input offset is presented. The asymptotic variance of estimation is obtained, simulations under Cauchy and Gaussian distributions are presented to validate the asymptotic results and they are compared to the continuous optimal estimator performance.
On the Use of an Intermediate Class in Boolean Crowdsourced Relevance Annotations for Learning to Rank Comments

In many Information Retrieval tasks, the boundary between classes is not well defined, and assigning a document to a specific class may be complicated, even for humans. For instance, a document which is not directly related to the user's query may still contain relevant information. In this scenario, an option is to define an intermediate class collecting ambiguous instances. Yet some natural questions arise. Is this annotation strategy convenient? how should the intermediate class be treated? To answer these questions, we explored two community question answering datasets whose comments were originally annotated with three classes. We re-annotated a subset of instances considering a binary good vs bad setting. Our main contribution is to show empirically that the inclusion of an intermediate class to assess Boolean relevance is not useful. Moreover, in case the data is already annotated with a 3-class strategy, the instances from the intermediate class can be safely removed at training time.
What Makes It Difficult to Understand a Scientific Literature?

In the artificial intelligence area, one of the ultimate goals is to make computers understand human language and offer assistance. In order to achieve this ideal, researchers of computer science have put forward a lot of models and algorithms attempting at enabling the machine to analyze and process human natural language on different levels of semantics. Although recent progress in this field offers much hope, we still have to ask whether current research can provide assistance that people really desire in reading and comprehension. To this end, we conducted a reading comprehension test on two scientific papers which are written in different styles. We use the semantic link models to analyze the understanding obstacles that people will face in the process of reading and figure out what makes it difficult for human to understand a scientific literature. Through such analysis, we summarized some characteristics and problems which are reflected by people with different levels of knowledge on the comprehension of difficult science and technology literature, which can be modelled in semantic link network. We believe that these characteristics and problems will help us re-examine the existing machine models and are helpful in the designing of new one.
Impact of test point insertion on silicon area and timing during layout

This paper presents an experimental investigation on the impact of test point insertion on circuit size and performance. Often test points are inserted into a circuit in order to improve the circuit's testability, which results in smaller test data volume, shorter test time, and higher fault coverage. Inserting test points however requires additional silicon area and influences the timing of a circuit. The paper shows how placement and routing is affected by test point insertion during layout generation. Experimental data for industrial circuits show that inserting 1% test points in general increases the silicon area after layout by less than 0.5% while the performance of the circuit may be reduced by 5% or more.
Quantifying ADHD classroom inattentiveness, its moderators, and variability: a meta-analytic review.

BACKGROUND Most classroom observation studies have documented significant deficiencies in the classroom attention of children with attention-deficit/hyperactivity disorder (ADHD) compared to their typically developing peers. The magnitude of these differences, however, varies considerably and may be influenced by contextual, sampling, diagnostic, and observational differences. METHODS Meta-analysis of 23 between-group classroom observation studies using weighted regression, publication bias, goodness of fit, best case, and original metric analyses. RESULTS Across studies, a large effect size (ES = .73) was found prior to consideration of potential moderators. Weighted regression, best case, and original metric estimation indicate that this effect may be an underestimation of the classroom visual attention deficits of children with ADHD. Several methodological factors-classroom environment, sample characteristics, diagnostic procedures, and observational coding schema-differentially affect observed rates of classroom attentive behavior for children with ADHD and typically developing children. After accounting for these factors, children with ADHD were on-task approximately 75% of the time compared to 88% for their classroom peers (ES = 1.40). Children with ADHD were also more variable in their attentive behavior across studies. CONCLUSIONS The present study confirmed that children with ADHD exhibit deficient and more variable visual attending to required stimuli in classroom settings and provided an aggregate estimation of the magnitude of these deficits at the group level. It also demonstrated the impact of situational, sampling, diagnostic, and observational variables on observed rates of on-task behavior.
Initialization of the Cartesian Ad hoc Routing Protocols (CARP)

The Cartesian ad hoc routing protocols (CARP) is a set of efficient broadcast control mechanism for geographical position aware nodes. In CARP, when a source node tries to transmit a packet in the direction of the destination, a logical transmission area is formed. To reduce the flooding traffic, only nodes located inside this area will forward the packet. The CARP assume that a source knows the Cartesian address of the mobile ad hoc destination node and nodes have some knowledge about the density from previous activity of the network. This research paper presents an initialization mechanism for (CARP), to find the destination logical address through controlled broadcast. In this paper, an initialization mechanism based on controlled retransmission and geographical position is presented. To validate these algorithms, the simulation results are also presented.
Provider–Customer Coalitional Games

Efficacy of commercial wireless networks can be substantially enhanced through large-scale cooperation among involved entities such as providers and customers. The success of such cooperation is contingent upon the design of judicious resource allocation strategies that ensure that the individuals' payoffs are commensurate to the resources they offer to the coalition. The resource allocation strategies depend on which entities are decision-makers and whether and how they share their aggregate payoffs. Initially, we consider the scenario where the providers are the only decision-makers and they do not share their payoffs. We formulate the resource allocation problem as a nontransferable payoff coalitional game and show that there exists a cooperation strategy that leaves no incentive for any subset of providers to split from the grand coalition, i.e., the core of the game is nonempty. To compute this cooperation strategy and the corresponding payoffs, we subsequently relate this game and its core to an exchange market setting and its equilibrium, which can be computed by several efficient algorithms. Next, we investigate cooperation when customers are also decision-makers and decide which provider to subscribe to based on whether there is cooperation. We formulate a coalitional game in this setting and show that it has a nonempty core. Finally, we extend the formulations and results to the cases where the payoffs are vectors and can be shared selectively.
Efficient non-uniform filter-bank equalizer

An efficient realization of a low delay filter-bank will be proposed, which can be regarded as a filter-bank used as equalizer with either fixed or time-varying spectral gain factors. The proposed filter-bank equalizer (FBE) results in a time-domain filter whose coefficients are adapted in the (short-term) spectral-domain. Perfect signal reconstruction is obtained for a broad class of spectral transforms, including the generalized discrete Fourier transform (GDFT), the Walsh and Hadamard transform, with less restrictions compared to a common analysis-synthesis filter-bank (AS FB). A non-uniform frequency resolution can be achieved by frequency warping based on an allpass transformation. In this case, the filter-bank equalizer can achieve near perfect signal reconstruction with lower effort than for an allpass transformed analysis-synthesis filter-bank. The filterbank equalizer is especially useful for adaptive filtering requiring a low signal delay with coefficient adaptation based on frequency domain algorithms.
Pneumonia treatment by photodynamic therapy with extracorporeal illumination ‐ an experimental model

Infectious pneumonia is a major cause of morbidity/mortality, mainly because of the increasing rate of microorganisms resistant to antibiotics. Photodynamic Therapy (PDT) is emerging as a promising approach, as effects are based on oxidative stress, preventing microorganism resistance. In two previous studies, the in vitro inactivation of Streptococcus pneumoniae using indocyanine green (ICG) and infrared light source was a success killing 5 log10 colony‐forming units (CFU/mL) with only 10 μmol/L ICG. In this work, a proof‐of‐principle protocol was designed to treat lung infections by PDT using extracorporeal illumination with a 780 nm laser device and also ICG as photosensitizer. Hairless mice were infected with S. pneumoniae and PDT was performed two days after infection. For control groups, CFU recovery ranged between 103–104/mouse. For PDT group, however, no bacteria were recovered in 80% of the animals. Based on this result, animal survival was evaluated separately over 50 days. No deaths occurred in PDT group, whereas 60% of the control group died. Our results indicate that extracorporeal PDT has the potential for pneumonia treatment, and pulmonary decontamination with PDT may be used as a single therapy or as an antibiotics adjuvant.
Hybrid recommendation system based on collaborative filtering and fuzzy numbers

Online retail stores face great challenges to recommend products due to the size and sparsity of the databases, as well as the variety of new users and items. As current techniques, based on collaborative filtering, address those issues with only partial success, the present paper proposes the use of a hybrid system of recommendation in online stores. This system makes use of collaborative filtering and of a fuzzy number model based on marketing concepts. Experimental results show that the proposed system presents great invariance to sparse databases, which is of great value for retail companies.
Evaluation of Lapatinib and Trastuzumab for Ototoxic Effects.

OBJECTIVE Trastuzumab and lapatinib are widely used chemotherapeutic agents. Our aim in this study was to assess the possible ototoxicity of these chemotherapeutic agents. MATERIALS AND METHODS Forty-eight rats were divided into six groups: Group 1 (control, n=8) received intraperitoneal saline for 7 days. Group 2 (n=8) and Group 3 (n=8) received 10 mg/kg and 30 mg/kg single doses of intraperitoneal trastuzumab, respectively. Lapatinib was administered by oral gavage to Group 4 (n=8) at 100 mg/kg/day and to group 5 (n=8) at 300 mg/kg/day for 7 days. Group 6 (n=8) received only one dose of 10 mg/kg intraperitoneal trastuzumab; subsequently, Group 6 received one dose of lapatinib at 100 mg/kg/day by oral gavage for 7 days. Before any medication was administered, distortion product emissions (DPOAE) were obtained. DPOAE tests were performed again on the rats on day 7, after which the mastoid bullas were harvested. The apoptosis degree was evaluated by the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) procedure. RESULTS The lapatinib 300 and lapatinib+trastuzumab groups (p=0.008 and p=0.001, respectively) were significantly different from the control group according to the spiral ganglion TUNEL. Apoptosis in the organ of corti was statistically different compared with the control group in the lapatinib 100, lapatinib 300, and lapatinib+trastuzumab groups (p=0.035, p=0.001, and p<0.001, respectively). Trastuzumab induced damage in only the organ of corti; however, lapatinib induced damage in both the organ of corti and spiral ganglion. The degree of the damage in the organ of corti was high when trastuzumab and lapatinib were concomitantly used. Supporting this data, a reduction in DPOAE amplitudes was observed during the combined usage of the drugs. CONCLUSION Administering trastuzumab and lapatinib causes ototoxic effects.
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their widespread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.
Mitigating the compiler optimization phase-ordering problem using machine learning

Today's compilers have a plethora of optimizations to choose from, and the correct choice of optimizations can have a significant impact on the performance of the code being optimized. Furthermore, choosing the correct order in which to apply those optimizations has been a long standing problem in compilation research. Each of these optimizations interacts with the code and in turn with all other optimizations in complicated ways. Traditional compilers typically apply the same set of optimization in a fixed order to all functions in a program, without regard the code being optimized. Understanding the interactions of optimizations is very important in determining a good solution to the phase-ordering problem. This paper develops a new approach that automatically selects good optimization orderings on a per method basis within a dynamic compiler. Our approach formulates the phase-ordering problem as a Markov process and uses a characterization of the current state of the code being optimized to creating a better solution to the phase ordering problem. Our technique uses neuro-evolution to construct an artificial neural network that is capable of predicting beneficial optimization ordering for a piece of code that is being optimized. We implemented our technique in Jikes RVM and achieved significant improvements on a set of standard Java benchmarks over a well-engineered fixed order.
Key Factors Affecting User Experience of Mobile Crowdsourcing Applications

With the increasing popularity of information communication technologies, crowdsourcing has become a significant method for companies to involve customers in various enterprises’ activities. Also, since user experience (UX) emerges as an important element in the design of interactive products, it is essential to indentify the main determinants that can improve UX. Based on recent studies, this paper provides a conceptual framework indicating key factors that influence the UX of mobile crowdsourcing applications. These factors range from easiness of tasks in crowdsourcing applications, users’ attitudes towards crowdsourcing companies, social influences to economic reward in crowdsourcing activities.
Taxonomy for 3 D Content-based Object Retrieval Methods

The use of three-dimensional (3D) image and model databases throughout the Internet is growing both in number and size. The emergence of 3D media is also directly related to the emergence of the 3D acquisition technologies. Indeed, recent advances in 3D scanner acquisition and 3D graphics rendering technologies boost the creation of 3D model archives for several application domains. Therefore, the development of efficient search mechanisms is required for the effective retrieval of 3D objects from large repositories. Over the last years, a large number of competing techniques have been developed for the purpose of content-based retrieval of 3D objects and consequently, more research is required to be conducted on surveying and comparing such proposed techniques. In this paper, we provide a comprehensive survey on the recent methods for content-based 3D object retrieval guided by a proposed taxonomy based on different shape descriptors. Finally, a performance comparison is carried out among selected methods based on qualitative measures.
Polycarbonate Crystallization by Vapor‐Grown Carbon Fiber with and without Magnetic Field

Polycarbonate (PC)/vapor-grown carbon fiber (VGCF) composite was prepated through melt compounding. It was unexpectedly found from differential scanning calorimetry (DSC) and wide angle X-ray diffraction (WAXD) that the crystallization of PC was substantially accelerated in the presence of the ordered graphite surface of VGCF. To make an aligned structure of PC crystallization together with the orientation of VGCF, a magnetic field of 2.4 T was applied to the composite under several temperature profiles. The WAXD pattern revealed that not only dispersed VGCF but also matrix PC orystallization was magnetically aligned through the optimization of processes. The evidence for PC crystallization by VGCF with and without magnetic force is described.
Boosting RDF Adoption in Ruby with Goo

For the last year, the BioPortal team has been working on a new iteration that will incorporate major modifications to the existing services and architecture. As part of this work, we transitioned BioPortal to an architecture where RDF is the main data model and where triple stores are the main database systems. We have a component (called "Goo") that interacts with RDF data using SPARQL, and provides a clean API to perform CRUD operations on RDF stores. Using RDF and SPARQL for a real-world large-scale application creates challenges in terms of both scalability and technology adoption. In BioPortal, Goo helped us overcome that barrier using the technology that developers were familiar with, an ORM-alike API.
Hierarchical Interconnection Networks for Multicomputer Systems

A performance analysis of a class of hierarchical interconnection networks is presented. The analysis includes both static analysis (i.e. queueing delays are neglected) and queueing analysis. In both cases, the hierarchical networks are shown to have better cost-benefit ratios. The queueing analysis is also validated by several simulation experiments. The impact of two performance enhancement schemes-replication of links and improved routing algorithms-on hierarchical interconnection network performance is also presented. >
Symmetry and Reduction for Coordinated Rigid Bodies

Motivated by interest in the collective behavior of autonomous agents, we lay foundations for a study of networks of rigid bodies and, specifically, the problem of aligning orientation and controlling relative position across the group. Our main result is the reduction of the (networked) system for the case of two individuals coupled by control inputs that depend only on relative configuration. We use reduction theory based on semi-direct products, yielding Poisson spaces that enable efficient formulation of control laws. We apply these reduction results to satellite and underwater vehicle dynamics, proving stability of coordinated behaviors such as two underwater vehicles moving at constant speed with their orientations stably aligned.