Miracle-dz/UniFAR-FTUs huggingface dataset:
Every sperm is sacred: fertilization in Caenorhabditis elegans.

The nematode Caenorhabditis elegans is an attractive model system for the study of fertilization. C. elegans exists as a self-fertilizing hermaphrodite or as a male. This unusual situation provides an excellent opportunity to identify and maintain sterile mutants that affect sperm and no other cells. Analysis of these mutants can identify genes that encode proteins required for gamete recognition, adhesion, signaling, fusion, and/or activation at fertilization. These genes can also provide a starting point for the identification of additional molecules required for fertility. This review describes progress in the genetic and molecular dissection of fertilization in C. elegans and related studies on sperm competition.
Deterministic annealing for semi-supervised kernel machines

An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For margin-based loss functions, one can view this approach as attempting to learn low-density separators. However, this is a hard optimization problem to solve in typical semi-supervised settings where unlabeled data is abundant. The popular Transductive SVM algorithm is a label-switching-retraining procedure that is known to be susceptible to local minima. In this paper, we present a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked. Our approach is motivated from deterministic annealing techniques and involves a sequence of convex optimization problems that are exactly and efficiently solved. We present empirical results on several synthetic and real world datasets that demonstrate the effectiveness of our approach.
Monitoring interstitial m‐THPC‐PDT in vivo using fluorescence and reflectance spectroscopy

In order to understand the mechanisms of photodynamic therapy (PDT) it is important to monitor parameters during illumination that yield information on deposited PDT dose. The aim of this study is to investigate the possibility of monitoring implicit parameters, such as photobleaching, in addition to monitoring explicit parameters (fluence (rate), oxygenation, photosensitizer concentration) directly or indirectly. These parameters are monitored during PDT without interrupting the therapeutic illumination.
Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms

Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.
Creating a Dutch Information Retrieval Test Corpus

This paper describes the first large-scale evaluation of information retrieval systems using Dutch documents and queries. We describe in detail the characteristics of the Dutch test data, which is part of the official CLEF multilingual test corpus, and give an overview of the experimental results of companies and research institutions that participated in the first official Dutch CLEF experiments. Judging from these experiments, the handling of languagespecific issues of Dutch, like for instance simple morphology and compound nouns, significantly improves the performance of information retrieval systems in many cases. Careful examination of the test collection shows that it serves as a reliable tool for the evaluation of information retrieval systems in the future.
Current status of research on transgenic animal models for Alzheimer ’ s disease brain tissue of the Alzheimer patients tends to shrivel like a dried walnut as the patients ’ behavior worsens ( Desikan

Each and every pathological visible effect is mostly due to molecular or biochemical reactions on cellular and intracellular level. This was the reason why in the last decade many incurable or poorly understood diseases were at least partially described. This is also the Alzheimer’s disease case, extremely aggressive and highly unpredictable. Many studies showed various conclusions used in further research to develop treatments and biochemical therapies in order to slow the degenerative effects or to eradicate the means and motives of this disease. Generally, in order to cure a certain disease, one must surely know the mechanisms of actions involved in its pathology and the start point, also very important. In Alzheimer’s disease, there has been studied an entire cohort of hypothesis and by using animal models the researchers reached possible cause variants. Because of the unknown idiopathic Alzheimer’s disease etiology, animal models were based on genetic mutations associated with familial Alzheimer, supposing that following events are identical. These genetic models are highly valuable to elucidate the molecular mechanisms which determine the disease’s progression, although no animal model can replicate the entire disease symptomatology. For this reason, every animal model type can be used to study one aspect of the disease. For example, transgenic mice overexpressing APP gene exhibit a similar pathology as humans. Age dependence and beta-amyloid accumulation speed was determined also by using transgenic mice. Beta-amyloid plaques discovered in transgenic mice brains were extremely similar to humans ones. Using the same manner of research, it was found a link between beta-amyloid plaques and alfa-synuclein. Plaques coloring method was also developed by various coloring techniques used on histological transgenic mice brain tissue smears. Alzheimer’s disease pathology was well studied using transgenic animal models in order to characterize the molecular deficiencies observed in humans. Because of the incapacity of study on human subjects due to ethical reasons and specific human characteristics, several animal models have been developed, which facilitated the study Alzheimer’s disease features one at a time.
Exact Bayesian estimation in constrained Triplet Markov Chains

The Jump Markov state-space system (JMSS) is a well known model for representing dynamical models with jumps. However inference in a JMSS model is NP-hard, even in the conditionally linear and Gaussian case. Suboptimal solutions include Sequential Monte Carlo (SMC) and Interacting Multiple Models (IMM) methods. In this paper, we build a constrained Triplet Markov Chain (TMC) model which is close to the given JMSS model, and in which moments of interest can be computed exactly (without resorting to numerical nor Monte Carlo approximations) and at a computational cost which is linear in the number of observations. Additionally, a side advantage of our technique is that it can be used easily in a partially known model context.
Cost-Sensitive Exploration in Bayesian Reinforcement Learning

In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward. In order to formalize cost-sensitive exploration, we use the constrained Markov decision process (CMDP) as the model of the environment, in which we can naturally encode exploration requirements using the cost function. We extend BEETLE, a model-based BRL method, for learning in the environment with cost constraints. We demonstrate the cost-sensitive exploration behaviour in a number of simulated problems.
Learning Non-Linear Functions for Text Classification

In this paper, we show that generative clas-siﬁers are capable of learning non-linear decision boundaries and that non-linear generative models can outperform a number of linear classiﬁers on some text categorization tasks. We ﬁrst prove that 3-layer multinomial hierarchical generative (Bayesian) classi-ﬁers, under a particular independence assumption, can only learn the same linear decision boundaries as a multinomial naive Bayes classiﬁer. We then go on to show that making a different independence assumption results in nonlinearization, thereby enabling us to learn non-linear decision boundaries. We ﬁnally evaluate the performance of these non-linear classiﬁers on a series of text classiﬁcation tasks.
Genetic Variability among Complete Human Respiratory Syncytial Virus Subgroup A Genomes: Bridging Molecular Evolutionary Dynamics and Epidemiology

Human respiratory syncytial virus (RSV) is an important cause of severe lower respiratory tract infections in infants and the elderly. In the vast majority of cases, however, RSV infections run mild and symptoms resemble those of a common cold. The immunological, clinical, and epidemiological profile of severe RSV infections suggests a disease caused by a virus with typical seasonal transmission behavior, lacking clear-cut virulence factors, but instead causing disease by modifying the host’s immune response in a way that stimulates pathogenesis. Yet, the interplay between RSV-evoked immune responses and epidemic behavior, and how this affects the genomic evolutionary dynamics of the virus, remains poorly understood. Here, we present a comprehensive collection of 33 novel RSV subgroup A genomes from strains sampled over the last decade, and provide the first measurement of RSV-A genomic diversity through time in a phylodynamic framework. In addition, we map amino acid substitutions per protein to determine mutational hotspots in specific domains. Using Bayesian genealogical inference, we estimated the genomic evolutionary rate to be 6.47×10−4 (credible interval: 5.56×10−4, 7.38×10−4) substitutions/site/year, considerably slower than previous estimates based on G gene sequences only. The G gene is however marked by elevated substitution rates compared to other RSV genes, which can be attributed to relaxed selective constraints. In line with this, site-specific selection analyses identify the G gene as the major target of diversifying selection. Importantly, statistical analysis demonstrates that the immune driven positive selection does not leave a measurable imprint on the genome phylogeny, implying that RSV lineage replacement mainly follows nonselective epidemiological processes. The roughly 50 years of RSV-A genomic evolution are characterized by a constant population size through time and general co-circulation of lineages over many epidemic seasons – a conclusion that might be taken into account when developing future therapeutic and preventive strategies.
Software Reliability Assurance Using a Framework in Weapon System Development: A Case Study

In embedded systems like weapon systems, the proportion of software has been growing and the quality of software, which provides complicated functionalities of a system and controls hardware, affects the quality of the entire system. Besides, to implement/acquire reliable software, we need the efforts for assuring process quality as well as product quality in the whole software development life cycle. In this paper, we propose a framework to assure software reliability of weapon systems in Korean defense domain. The framework provides a guideline for software reliability evaluation to software organizations and pursues the improvement of software engineering process which supports activities and indicators for quantitative project management in the software development process. We also present the empirical study of the application of the proposed framework and the analysis results on the effectiveness of the proposed framework.
Multimodal Interaction Metaphors for Manipulation of Distant Objects in Immersive Virtual Environments

In this paper we discuss direct interaction metaphors for selection and manipulation of distant objects in immersive virtual environments and we propose extensions of the improved virtual pointer (IVP) metaphor. In particular, we describe how the process of object selection with the IVP metaphor can be enhanced by modifying the distance calculation used to determine the closest object to be selected. Furthermore we introduce direct 6 DOF manipulations of virtual objects using the IVP metaphor. We demonstrate how the task of object selection can be improved by combining existing interaction metaphors with multimodal feedback.
Incremental development of multiple tool models for robotic reaching through autonomous exploration

Autonomy and flexibility are two major requirements for modern robots. In particular, humanoid robots should learn new skills incrementally through autonomous exploration, and adapt to different contexts. In this paper we consider the problem of learning forward models for task space control under dynamically varying kinematic contexts: the robot learns incrementally and autonomously its forward kinematics under different contexts, represented by the inclusion of different tools, and exploits the learned model to realize reaching with those tools. We model the forward kinematics as a multi-valued function, in which different outputs for the same input query are related to different tools (i.e. contexts). The model is estimated using IMLE, a recent online learning algorithm for multi-valued regression, and used for control. No information is given about the tool changes, nor any assumption is made about the tool kinematics. Results are provided both in simulation and with a full-body humanoid. In the latter case we show how the robot successfully performs reaching using a flexible tool, a clear example of complex kinematics.
PAID: Prioritizing app issues for developers by tracking user reviews over versions

User review analysis is critical to the bug-fixing and version-modification process for app developers. Many research efforts have been put to user review mining in discovering app issues, including laggy user interface, high memory overhead, privacy leakage, etc. Existing exploration of app reviews generally depends on static collections. As a result, they largely ignore the fact that user reviews are tightly related to app versions. Furthermore, the previous approaches require a developer to spend much time on filtering out trivial comments and digesting the informative textual data. This would be labor-intensive especially to popular apps with tremendous reviews. In the paper, we target at designing a framework in Prioritizing App Issues for Developers (PAID) with minimal manual power and good accuracy. The PAID design is based on the fact that the issues presented in the level of phrase, i.e., a couple of consecutive words, can be more easily understood by developers than in long sentences. Hence, we aim at recommending phrase-level issues of an app to its developers by tracking reviews over the release versions of the app. To assist developers in better comprehending the app issues, PAID employs ThemeRiver to visualize the analytical results to developers. Finally, PAID also allows the developers to check the most related reviews, when they want to obtain a deep insight of a certain issue. In contrast to the traditional evaluation methods such as manual labeling or examining the discussion forum, our experimental study exploits the first-hand information from developers, i.e., app changelogs, to measure the performance of PAID. We analyze millions of user reviews from 18 apps with 117 app versions and the results show that the prioritized issues generated by PAID match the official changelogs with high precision.
Speech emotion recognition based on a hybrid of HMM/ANN

Speech emotion recognition, as a vital part of affective human computer interaction, has become a new challenge to speech processing. In this paper, a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) has been proposed to classify emotions, combining advantage on capability to dynamic time warping of HMM and pattern recognition of ANN. HMMs, which export likelihood probabilities and optimal state sequences, have been used to model speech feature sequences, while ANN has been employed to make a decision. The recognition result of the hybrid classification has been compared with the isolated HMMs by two speech corpora, Germany database and Mandarin database, and the average recognition rates have reached 83.8% and 81.6% respectively.
Efficiently Enumerating Minimal Triangulations

We present an algorithm that enumerates all the minimal triangulations of a graph in incremental polynomial time. Consequently, we get an algorithm for enumerating all the proper tree decompositions, in incremental polynomial time, where ``proper'' means that the tree decomposition cannot be improved by removing or splitting a bag. The algorithm can incorporate any method for (ordinary, single result) triangulation or tree decomposition, and can serve as an anytime algorithm to improve such a method. We describe an extensive experimental study of an implementation on real data from different fields. Our experiments show that the algorithm improves upon central quality measures over the underlying tree decompositions, and is able to produce a large number of high-quality decompositions.
On the Application of the Cyc Ontology to Word Sense Disambiguation

This paper describes a novel, unsupervised method of word sense disambiguation that is wholly semantic, drawing upon a complex, rich ontology and inference engine (the Cyc system). This method goes beyond more familiar semantic closeness approaches to disambiguation that rely on string cooccurrence or relative location in a taxonomy or concept map by 1) exploiting a rich array of properties, including higher-order properties, not available in merely taxonomic (or other first-order) systems, and 2) appealing to the semantic contribution a word sense makes to the content of the target text. Experiments show that this method produces results markedly better than chance when disambiguating word senses in a corpus of topically unrelated documents.
Characterizing Non-Wetting Fluid in Natural Porous Media Using Synchrotron X-Ray Microtomography

The objective of this study was to characterize non-wetting fluid in multi-phase systems comprising a range of fluid and porous medium properties. Synchrotron X-ray microtomography was used to obtain high-resolution, three-dimensional images of fluids in natural porous media. Images were processed to obtain quantitative measurements of fluid distribution, morphology, and interfacial area. Column-flooding experiments were conducted with four enhanced-solubilization (ES) solutions to examine their impact on entrapped organic liquid. Mobilization caused a change in organic-liquid morphology and distribution for most experiments. The effect of ES-solution flooding on fluid-fluid interfacial area was similar to that of water flooding. Organic-liquid mobilization was observed at total trapping numbers that were smaller than expected. This was attributed to pore-scale mobilization of blobs that were re-trapped prior to being eluted from the column. Pore-scale mobilization was also observed during water-flooding experiments for which trapping numbers varied over several orders of magnitude. Water-flooding and surfactant-flooding experiments were compared to investigate the impact of interfacial tension, viscosity, and fluid velocity on entrapped organic liquid. For similar total trapping numbers, flooding at larger velocities appeared to have a greater effect on the distribution of non-wetting blobs than lowering interfacial tension or increasing the viscosity of the wetting fluid. The fluid-normalized interfacial area was generally independent of the total trapping number. Finally, the impact of fluid type on the interfacial area between different pairs of non-wetting fluids was investigated during drainage and imbibition in four natural porous media. Interfacial areas were similar 10
Increasing Performances of En AB-46000 by Squeeze Casting

The evolution of the mold temperature during squeeze casting of EN-AB46000 aluminum alloy has been correlated with the final mechanical performances of cast ingots. Starting from a material model which expresses hardness and yielding stress of cast aluminum alloys as a function of the cooling rate during the melt solidification, an experimental approach has been used to provide a useful tool for process monitoring. As a result, the mold temperature increase during the melt squeezing phase is directly correlated with the main mechanical and microstructural parameters. Experiments were made by squeeze cast small cylinders (14 mm in diameter and 18 mm height) at different values of squeezing pressure, mold pre-heating temperature, and melt temperature. Microscopic observations of the sample sections were made as well as hardness measurements and indentation tests. In conclusion, because of the material solidification, a temperature gradient has been observed in the sample which can be directly related with the aluminum alloy dendrite size and, in turn, with microhardness and yielding stress.
Computing Circumscription Revisited: Preliminary Report

We provide a general method which can be used in an algorithmic manner to reduce certain classes of 2nd-order circumscription axioms to logically equivalent 1st order formulas The algorithm takes as input an arbitrary 2nd-order formula and either returns as output an equivalent 1st order formula, or terminates with failure In addition to demonstrating the algorithm by applying it to various circumscriptive theories, we analyze its strength and provide formal subsumption results based on compan son with existing approaches.
Should Soft Tissue Sarcomas be Treated at a Specialist Centre?

Objective. We have investigated whether there is evidence that patients with soft tissue sarcomas do better if treated in a specialist centre compared with district general hospitals. Patients. All patients diagnosed with soft tissue sarcomas who were residents of WMRHA between 1994 and 1996, with minimum follow up of 5 years, excluding head and neck or retroperitoneal tumors. Methods. We reviewed data from the Royal Orthopaedic Hospital Oncology Service (ROHOS) database and the Cancer Intelligence Unit (CIU) Database, with medial record review where necessary. Main outcome measures were local recurrence and overall survival. Results. A total of 260 patients were diagnosed as having STS over the 3-year period (incidence=1.62per 100000 per year): 37% of patients had the majority of treatment at the specialist centre under the care of three surgeons, whilst the other 63% were treated at a total of 38 different hospitals. The rate of local recurrence was 39% at the district general hospitals compared with 19% at the specialist centre despite the fact that tumours treated at the district hospitals were smaller and of lower grade. The most significant factors affecting survival were grade (high versus low) and depth of the tumour. Patients treated at the specialist centre had a small survival advantage after multivariate testing. Conclusion. Soft tissue sarcomas are rare. Centralization of treatment improves local control in all patients and survival in some. Appropriate mechanisms for ensuring that patients with soft tissue sarcomas are seen and treated at specialist centres should be developed.