Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional analyses of tiptop and Antennapedia in the embryonic development of Oncopeltus fasciatus suggests an evolutionary pathway from ground state to insect legs

In insects, selector genes are thought to modify the development of a default, or `ground state', appendage into a tagma-specific appendage such as a mouthpart, antenna or leg. In the best described example, Drosophila melanogaster, the primary determination of leg identity is thought to result from regulatory interactions between the Hox genes and the antennal-specifying gene homothorax. Based on RNA-interference, a functional analysis of the selector gene tiptop and the Hox gene Antennapedia in Oncopeltus fasciatus embryogenesis is presented. It is shown that, in O. fasciatus, tiptop is required for the segmentation of distal leg segments and is required to specify the identity of the leg. The distal portions of legs with reduced tiptop develop like antennae. Thus, tiptop can act as a regulatory switch that chooses between antennal and leg identity. By contrast, Antennapedia does not act as a switch between leg and antennal identity. This observation suggests a significant difference in the mechanism of leg specification between O. fasciatus and D. melanogaster. These observations also suggest a significant plasticity in the mechanism of leg specification during insect evolution that is greater than would have been expected based on strictly morphological or molecular comparisons. Finally, it is proposed that a tiptop-like activity is a likely component of an ancestral leg specification mechanism. Incorporating a tiptop-like activity into a model of the leg-specification mechanism explains several mutant phenotypes, previously described in D. melanogaster, and suggests a mechanism for the evolution of legs from a ground state.
Regression Testing Ajax Applications: Coping with Dynamism

There is a growing trend to move desktop applications towards the web using advances made in web technologies such as Ajax. One common way to provide assurance about the correctness of such complex and evolving systems is through regression testing. Regression testing classical web applications has already been a notoriously daunting task because of the dynamism in web interfaces. Ajax applications pose an even greater challenge since the test case fragility degree is higher due to extensive run-time manipulation of the DOM tree and asynchronous client/server interactions. In this paper, we propose a technique, in which we automatically generate test cases and apply pipelined oracle comparators along with generated DOM templates, to deal with dynamic non-deterministic behavior in Ajax user interfaces. Our approach, implemented in Crawljax, is open source and provides a set of generic oracle comparators, template generators, and visualizations of test failure output. We describe two case studies evaluating the effectiveness, scalability, and required manual effort of the approach.
Design of a Customized Multipurpose Nano-Enabled Implantable System for In-Vivo Theranostics

The first part of this paper reviews the current development and key issues on implantable multi-sensor devices for in vivo theranostics. Afterwards, the authors propose an innovative biomedical multisensory system for in vivo biomarker monitoring that could be suitable for customized theranostics applications. At this point, findings suggest that cross-cutting Key Enabling Technologies (KETs) could improve the overall performance of the system given that the convergence of technologies in nanotechnology, biotechnology, micro&nanoelectronics and advanced materials permit the development of new medical devices of small dimensions, using biocompatible materials, and embedding reliable and targeted biosensors, high speed data communication, and even energy autonomy. Therefore, this article deals with new research and market challenges of implantable sensor devices, from the point of view of the pervasive system, and time-to-market. The remote clinical monitoring approach introduced in this paper could be based on an array of biosensors to extract information from the patient. A key contribution of the authors is that the general architecture introduced in this paper would require minor modifications for the final customized bio-implantable medical device.
Identifying Strategic Information from Scientific Articles through Sentence Classification

We address here the need to assist users in rapidly accessing the most important or strategic information in the text corpus by identifying sentences carrying specific information. More precisely, we want to identify contribution of authors of scientific papers through a categorization of sentences using rhetorical and lexical cues. We built local grammars to annotate sentences in the corpus according to their rhetorical status: objective, new things, results, findings, hypotheses, conclusion, related_word, future work. The annotation is automatically projected automatically onto two other corpora to test their portability across several domains. The local grammars are implemented in the Unitex system. After sentence categorization, the annotated sentences are clustered and users can navigate the result by accessing specific information types. The results can be used for advanced information retrieval purposes.
The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple- perspective method increases the interpretability and accountability of both ACA and DCA networks.
Effect of sodium bicarbonate on [HCO3-], pH, and gastrointestinal symptoms.

CONTEXT Sodium bicarbonate (NaHCO₃) is often ingested at a dose of 0.3 g/kg body mass (BM), but ingestion protocols are inconsistent in terms of using solution or capsules, ingestion period, combining NaHCO₃ with sodium citrate (Na₃C₆H₅O₇), and coingested food and fluid. PURPOSE To quantify the effect of ingesting 0.3 g/kg NaHCO₃ on blood pH, [HCO₃-], and gastrointestinal (GI) symptoms over the subsequent 3 hr using a range of ingestion protocols and, thus, to determine an optimal protocol. METHODS In a crossover design, 13 physically active subjects undertook 8 NaHCO₃ experimental ingestion protocols and 1 placebo protocol. Capillary blood was taken every 30 min and analyzed for pH and [HCO₃-]. GI symptoms were quantified every 30 min via questionnaire. Statistics used were pairwise comparisons between protocols; differences were interpreted in relation to smallest worthwhile changes for each variable. A likelihood of >75% was a substantial change. RESULTS [HCO₃-] and pH were substantially greater than in placebo for all other ingestion protocols at almost all time points. When NaHCO3 was coingested with food, the greatest [HCO₃-] (30.9 mmol/kg) and pH (7.49) and lowest incidence of GI symptoms were observed. The greatest incidence of GI side effects was observed 90 min after ingestion of 0.3 g/kg NaHCO₃ solution. CONCLUSIONS The changes in pH and [HCO₃-] for the 8 NaHCO₃-ingestion protocols were similar, so an optimal protocol cannot be recommended. However, the results suggest that NaHCO₃ coingested with a high-carbohydrate meal should be taken 120-150 min before exercise to induce substantial blood alkalosis and reduce GI symptoms.
Named Entity Filtering Based on Concept Association Graphs

In this paper, we introduce a novel technique for named entity filtering, focused on the analysis of word association networks. We present an approach for modelling concepts which are distinctively related to specific named entity. We evaluated our approach in the context of the TREC Knowledge Base Acceleration track, and we obtained significantly better performance than the top-ranked systems. For this task, given the set of all named entities and nouns, our approach proved betterperforming for named entity filtering than the baseline SVM classifier. This performance is the result of the ability to disambiguate entities, by taking into account the concepts relevant to a specific named entity.
Viral phylodynamics and the search for an ‘effective number of infections’

Information on the dynamics of the effective population size over time can be obtained from the analysis of phylogenies, through the application of time-varying coalescent models. This approach has been used to study the dynamics of many different viruses, and has demonstrated a wide variety of patterns, which have been interpreted in the context of changes over time in the ‘effective number of infections’, a quantity proportional to the number of infected individuals. However, for infectious diseases, the rate of coalescence is driven primarily by new transmissions i.e. the incidence, and only indirectly by the number of infected individuals through sampling effects. Using commonly used epidemiological models, we show that the coalescence rate may indeed reflect the number of infected individuals during the initial phase of exponential growth when time is scaled by infectivity, but in general, a single change in time scale cannot be used to estimate the number of infected individuals. This has important implications when integrating phylogenetic data in the context of other epidemiological data.
Discovery of a novel murine keratin 6 (K6) isoform explains the absence of hair and nail defects in mice deficient for K6a and K6b

The murine genome is known to have two keratin 6 (K6) genes, mouse K6 (MK6)a and MK6b. These genes display a complex expression pattern with constitutive expression in the epithelia of oral mucosa, hair follicles, and nail beds. We generated mice deficient for both genes through embryonic stem cell technology. The majority of MK6a/b−/− mice die of starvation within the first two weeks of life. This is due to a localized disintegration of the dorsal tongue epithelium, which results in the build up of a plaque of cell debris that severely impairs feeding. However, ∼25% of MK6a/b−/− mice survive to adulthood. Remarkably, the surviving MK6a/b−/− mice have normal hair and nails. To our surprise, we discovered MK6 staining both in the hair follicle and the nail bed of MK6a/b−/− mice, indicating the presence of a third MK6 gene. We cloned this previously unknown murine keratin gene and found it to be highly homologous to human K6hf, which is expressed in hair follicles. We therefore termed this gene MK6 hair follicle (MK6hf). The presence of MK6hf in the MK6a/b−/− follicles and nails offers an explanation for the absence of hair and nail defects in MK6a/b−/− animals.
Semantic Change Detection with Hypermaps

Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understand how different the two images. Therefore, a semantic understanding is required in the change detection research such as disaster investigation. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI Panoramic Change Detection dataset, and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.
Biotransport of organic pollutants to an inland Alaska Lake by migrating Sockeye Salmon (Oncorhynchus nerka)

Persistent organic pollutants such as polychlorinated biphenyls (PCBs) and the pesticide DDT, known to harm wildlife, have been shown to reach pristine Subarctic and Arctic areas by global atmospheric transport. Another transport route for pollutant entry into these ecosystems is provided by migrating salmon. Pollutant transport was studied in a population of sockeye salmon (Oncorhynchus nerka) in the Copper River, Alaska during their 410 km spawning migration. Pollutants accumulated by the salmon during their ocean life stage were not eliminated during migration, but were transported to the spawning lakes and accumulated in the freshwater food web there. The influence of the biotransported pollutants was investigated by comparing pollutant levels and compositions in atmospheric deposition as well as in two different populations of arctic grayling (Thymallus arcticus). One grayling population was in the salmon spawning lake and the other in a nearby lake not hosting anadromous fish, but receiving pollutants only via atmospheric deposition. The grayling in the salmon spawning lake were found to have concentrations of organic pollutants more than two times higher than those of the grayling in the salmon-free lake, and the pollutant composition resembled that found in salmon. Thus, in the studied Alaska river system, biotransport was found to have a far greater influence than atmospheric input on the PCB and DDT levels in lake biota.
Quest for relevant tags using local interaction networks and visual content

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naïve Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.
Study of Wireless Sensor Networks as a Software Engineering Approach

Software development for wireless sensor networks requires novel programming paradigms and technologies. This article describes the concept of new service oriented software architecture for mobile sensor networks. With this architecture, a flexible, scalable programming of applications based on an adaptive middleware is possible. The middleware supports mechanisms for cooperative data mining, self-organization, networking, and energy optimization to build higher-level service structures. The purpose of our research activities is the development of a framework, which radically simplifies the development of software for sensor network applications.
A Lightweight Fault-Tolerant Mechanism for Network-on-Chip

Survival capability is becoming a crucial factor in designing multicore processors built with on-chip packet networks, or networks on chip (NoCs). In this paper, we propose a lightweight fault-tolerant mechanism for NoCs based on default backup paths (DBPs) designed to maintain, in the presence of failures, network connectivity of both non-faulty routers as well as healthy processor cores which may be connected to faulty routers. The mechanism provides default paths as backup between certain router ports which serve as alternative datapaths to circumvent failed components within a faulty router. Along with a minimal subset of normal network channels, the set of default backup paths internal to faulty routers form - in the worst case - a unidirectional ring topology that provides network-wide connectivity to all processor cores. Routing using the DBP mechanism is proved to be deadlock-free with only two virtual channels even for fault scenarios in which regular networks degrade to irregular (arbitrary) topologies. Evaluation results show that, for a 2-D mesh wormhole NoC, only 12.6% additional hardware resources are needed to implement the proposed DBP mechanism in order to provide graceful performance degradation without chip-wide failure as the number of faults increases to the maximum needed to form ring.
Parsing Noun Phrases in the Penn Treebank

Noun phrases (nps) are a crucial part of natural language, and can have a very complex structure. However, this np structure is largely ignored by the statistical parsing field, as the most widely used corpus is not annotated with it. This lack of gold-standard data has restricted previous efforts to parse nps, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (nlp) tasks. We comprehensively solve this problem by manually annotating np structure for the entire Wall Street Journal section of the Penn Treebank. The inter-annotator agreement scores that we attain dispel the belief that the task is too difficult, and demonstrate that consistent np annotation is possible. Our gold-standard np data is now available for use in all parsers. We experiment with this new data, applying the Collins (2003) parsing model, and find that its recovery of np structure is significantly worse than its overall performance. The parser's F-score is up to 5.69% lower than a baseline that uses deterministic rules. Through much experimentation, we determine that this result is primarily caused by a lack of lexical information. To solve this problem we construct a wide-coverage, large-scale np Bracketing system. With our Penn Treebank data set, which is orders of magnitude larger than those used previously, we build a supervised model that achieves excellent results. Our model performs at 93.8% F-score on the simple task that most previous work has undertaken, and extends to bracket longer, more complex nps that are rarely dealt with in the literature. We attain 89.14% F-score on this much more difficult task. Finally, we implement a post-processing module that brackets nps identified by the Bikel (2004) parser. Our np Bracketing model includes a wide variety of features that provide the lexical information that was missing during the parser experiments, and as a result, we outperform the parser's F-score by 9.04%. These experiments demonstrate the utility of the corpus, and show that many nlp applications can now make use of np structure.
Adaptive Modeling of Composite Structures: Modeling Error Estimation

The accurate simulation of the behavior of composite materials depends OIl many factors such as the character, size, topology, and mechanical properties of the microstructure, the definition of the domain of interest and the loads as well as the specification of accuracy desired and the goal of the simulation. There is, therefore, a need to develop a systematic technique to adaptively select the most appropriate scale that governs specific features of the response that are of interest. The concept of hierarchical modeling provides such a framework. In this approach, the accuracy of a given mathematical model, compared to a model of finer scale, is evaluated with the use of a posteriori estimates of the "modeling error" and these form the basis of an adaptive procedure. This investigation focuses on the analysis of the equilibrium of linearly elastic heterogeneous bodies characterized by highly oscillatory elastic coefficients. The control of modeling error in such systems was studied in earlier works [37, 32] using the HDPM : the Homogenized Dirichlet Projection Method. There, estimates of the error in the homogenized solutions in energy norms were used as a basis for an adaptive process of model selection. Following a brief review of the HDPM, we introduce an alternate method of model adaptation : the Hierarchical Adaptive Method for Model Enhancement and Refinement (HAMMER). In this method, we seek to generate a sequence of elasticity tensors such that the solutions they produce converge to the solution produced by the fine-scale microstructure. We extend the theory of a posteriori estimation of modeling error to "quantities of interest", by which we mean local features of the response such as the average of stresses on material interfaces, boundary displacements, or mollified point-values of the displacement field, or any feature of the solution that can be characterized as a continuous linear functional on the space of functions to which the solution belongs. We establish computable upper and lower bounds on errors in such quantities of interest. This theory represents a significant departure from
Small Batch or Large Batch?: Gaussian Walk with Rebound Can Teach

Efficiency of large-scale learning is a hot topic in both academic and industry. The stochastic gradient descent (SGD) algorithm, and its extension mini-batch SGD, allow the model to be updated without scanning the whole data set. However, the use of approximate gradient leads to the uncertainty issue, slowing down the decreasing of objective function. Furthermore, such uncertainty may result in a high frequency of meaningless update on the model, causing a communication issue in parallel learning environment. In this work, we develop a batch-adaptive stochastic gradient descent (BA-SGD) algorithm, which can dynamically choose a proper batch size as learning proceeds. Particularly on the basis of Taylor extension and central limit theorem, it models the decrease of objective value as a Gaussian random walk game with rebound. In this game, a heuristic strategy of determining batch size is adopted to maximize the utility of each incremental sampling. By evaluation on multiple real data sets, we demonstrate that by smartly choosing the batch size, the BA-SGD not only conserves the fast convergence of SGD algorithm but also avoids too frequent model updates.