Miracle-dz/UniFAR-FTUs huggingface dataset:
Auxin Function in the Brown Alga Dictyota dichotoma1

Auxin plays a role in the apical-basal patterning of the multicellular brown alga Dictyota dichotoma, supporting auxin function in polarity control outside the green lineage. Auxin controls body plan patterning in land plants and has been proposed to play a similar role in the development of brown algae (Phaeophyta) despite their distant evolutionary relationship with land plants. The mechanism of auxin action in brown algae remains controversial because of contradicting conclusions derived from pharmacological studies on Fucus. In this study, we used Dictyota dichotoma as a model system to show that auxin plays a role during the apical-basal patterning of the embryo of brown algae. Indole-3-acetic acid was detectable in D. dichotoma germlings and mature tissue. Although two-celled D. dichotoma zygotes normally develop a rhizoid from one pole and a thallus meristem from the other, addition of exogenous auxins to one-celled embryos affected polarization, and both poles of the spheroidal embryo developed into rhizoids instead. The effect was strongest at lower pH and when variable extrinsic informational cues were applied. 2-[4-(diethylamino)-2-hydroxybenzoyl]benzoic acid, an inhibitor of the ABC-B/multidrug resistance/P-glycoprotein subfamily of transporters in land plants, affected rhizoid formation by increasing rhizoid branching and inducing ectopic rhizoids. An in silico survey of auxin genes suggested that a diverse range of biosynthesis genes and transport genes, such as PIN-LIKES, and the ATP-binding cassette subfamily (ABC-B/multidrug resistance/P-glycoprotein) transporters from land plants have homologs in D. dichotoma and Ectocarpus siliculosus. Together with reports on auxin function in basal lineages of green algae, these results suggest that auxin function predates the divergence between the green and brown lineage and the transition toward land plants.
A Data Dissemination Method Based on Region Type Correlations for Mobile Opportunistic Networks

In Mobile Opportunistic Networks (MONs), due to the node movements and the uncontrollable on/off switches of the carried communication devices, the contacts between nodes may be scarce and momentary, and thus a data packet should be transferred through some discrete hops. To avoid the costly flooding of data packets, the data packets are typically disseminated to some relay nodes selected by data holders. However, the mobility patterns of nodes will become different in different types of regions (such as residential regions, commercial regions, scenery regions, or industrial regions); i.e., the movement directions and movement ranges of nodes are frequently varied when the nodes move among various regions. At present, the issues regarding the region types and region type correlations have not been investigated for the data dissemination in existing works. To this end, we propose a Region Type based Data Dissemination Method (RTDDM) for MONs, which exploits the region type correlations and selects the proper relay nodes through a Markov decision model. To verify the performance of RTDDM, we give some theoretical analysis as well as an elaborated simulation study, the results of which show that RTDDM can improve the delivery ratio and reduce the delivery delay, especially in the applications with various region types.
Integration of Graphs and Representation Learning

Integrating information from many different data sources to provide better situational awareness is an essential Navy issue. Many data fusion models use statistical methods to reduce statistical errors. Machine learning and big data provide, on the other hand, provides a unique framework for information fusion through our ability to learn what added beneﬁts a different modality can provide. In this work, we provide a novel data fusion method that integrates relational data, provided to us in the form of a graph, and image data. We build an energy model that learns a representation of the data where different data sources are assumed to be similar using a graphical model. The energy model is a non-convex function which we optimize using stochastic gradient descent with momentum. The effectiveness of the model is demonstrated in an automated target recognition example.
Optimal Tabu K-Means Clustering Using Massively Parallel Architecture

Parallel discovery of inherent clusters using massively threaded architectures is the solution for handling computational challenges raised by fat datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a convincing platform to parallelize clustering algorithms. The parallel K-means algorithm aims at increasing the speedup, but often faces the hitch of falling into local minima. The heuristic search procedure to discover the global optima in the solution space is known as Tabu Search. The K-means clustering solution is fine-tuned by applying parallel implementation of Tabu Search K-means clustering in order to increase efficacy. The aim is to combine optimization characteristic of Tabu Search for calculation of centroids with clustering attitude of K-means and to enhance the solution using processing power of GPU. The parallelization strategy used exhibits increase in speedup. The parallel Tabu-KM algorithm is tested on standard datasets, and performance is compared with sequential K-means, parallel K-means and sequential Tabu K-means algorithms. The experimental results confirm yet another parallelization technique to unravel data clustering problems.
Improved Dynamic Memory Network for Dialogue Act Classification with Adversarial Training

Dialogue Act (DA) classification is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker’s intention. Currently, many existing approaches formulate the DA classification problem ranging from multi-classification to structured prediction, which suffer from two limitations: a) these methods are either handcrafted feature-based or have limited memories. b) adversarial examples can’t be correctly classified by traditional training methods. To address these issues, in this paper we first cast the problem into a question and answering problem and proposed an improved dynamic memory networks with hierarchical pyramidal utterance encoder. Moreover, we apply adversarial training to train our proposed model. We evaluate our model on two public datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus. Extensive experiments show that our proposed model is not only robust, but also achieves better performance when compared with some state-of-the-art baselines.
CORE: A Tool for Collaborative Ontology Reuse and Evaluation

Ontology evaluation can be defined as assessing the quality and the adequacy of an ontology for being used in a specific context, for a specific goal. In this work, a tool for Collaborative Ontology Reuse and Evaluation (CORE) is presented. The system receives an informal description of a semantic domain and determines which ontologies, from an ontology repository, are the most appropriate to describe the given domain. For this task, the environment is divided into three main modules. The first component receives the problem description represented as a set of terms and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository and determine which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques that combine the selected criteria. A third component of the system uses manual user evaluations of the ontologies in order to incorporate a human, collaborative assessment of the quality of ontologies.
Leveraging Variable Elimination for Efficiently Reasoning about Qualitative Constraints

We introduce, study, and evaluate a novel algorithm in the context of qualitative constraint-based spatial and temporal reasoning that is based on the idea of variable elimination, a simple and general exact inference approach in probabilistic graphical models. Given a qualitative constraint network [Formula: see text], our algorithm utilizes a particular directional local consistency, which we denote by [Formula: see text]-consistency, in order to efficiently decide the satisfiability of [Formula: see text]. Our discussion is restricted to distributive subclasses of relations, i.e., sets of relations closed under converse, intersection, and weak composition and for which weak composition distributes over non-empty intersections for all of their relations. We demonstrate that enforcing [Formula: see text]-consistency in a given qualitative constraint network defined over a distributive subclass of relations allows us to decide its satisfiability, and obtain similar useful results for the problems of minimal labelling and redundancy. Further, we present a generic method that allows extracting a scenario from a satisfiable network, i.e., an atomic satisfiable subnetwork of that network, in a very simple and effective manner. The experimentation that we have conducted with random and real-world qualitative constraint networks defined over a distributive subclass of relations of the Region Connection Calculus and the Interval Algebra, shows that our approach exhibits unparalleled performance against state-of-the-art approaches for checking the satisfiability of such constraint networks.
Policies for Active Learning from Demonstration

This paper focuses on Learning from Demonstration, and in particular, on the problem of Active Learning from Demonstration in settings where the amount of data that can be acquired from the demonstrator is limited. In this paper we pro-pose a novel Active Learning from Demonstration approach, SALT , as well as a brief exploration of using a different underlying learning algorithm. We evaluate this approach in two domains: the classic platform game Super Mario Bros. and a grid-based puzzle game called Thermometers .
Heterogeneities in utilization of antenatal care in Uttar Pradesh, India: the need to contextualize interventions to individual contexts

ABSTRACT Background: This paper explores the heterogeneities in antenatal care (ANC) utilization in India’s most populated state, Uttar Pradesh. Taking an intersectionality lens, multiple individual- and district-level factors are used to identify segments of any antenatal care usage in Uttar Pradesh Objective: This paper seeks to understand the multilevel contexts of ANC utilization. The planning and programming challenge is that such knowledge of contextual specificity is rarely known upfront at the initial stages of planning or implementing an intervention. Exploratory data analysis might be needed to identify such contextual specificity. Methods: Tree-structured regression methods are used to identify segments and interactions between factors. The results from the tree-structured regression were complemented with multilevel models that controlled for the clustering of individuals within districts. Results: Heterogeneities in utilization of any ANC were observed. The multiple segments of ANC utilization that were developed went from a low utilization of 23.7% for respondents who were not literate and did not have home ownership to a high of 82.4% for respondents who were literate and at the highest level of wealth. Key variables that helped define the segments of ANC utilization include: woman’s literacy, ownership of home, wealth index, and district-level sex ratio. Based on the multilevel model of any ANC utilization, cross-level interactions also were obtained between sex ratio and ownership of home as well as between sex ratio and literacy. Increases in sex ratio increased the influence of ownership of home on any ANC, while increases in sex ratio reduced the impact of woman’s literacy on receiving any ANC. Conclusion: We argue that a focus on heterogeneous segments of utilization can help build knowledge of the mechanisms that underlie inequities in maternal health utilization. Such knowledge of heterogeneity needs to be incorporated in contextualizing interventions to meet a variety of recipients’ needs.
Exploring the Scope of Unconstrained Via Minimization by Recursive Floorplan Bipartitioning

Random via failure is a major concern for post-fabrication reliability and poor manufacturing yield. A demanding solution to this problem is redundant via insertion during post-routing optimization. It becomes very critical when a multi-layer routing solution already incurs a large number of vias. Very few global routers addressed unconstrained via minimization (UVM) problem, while using minimal pattern routing and layer assignment of nets. It also includes a recent floorplan based early global routability assessment tool STAIRoute \cite{karb2}. This work addresses an early version of unconstrained via minimization problem during early global routing by identifying a set of minimal bend routing regions in any floorplan, by a new recursive bipartitioning framework. These regions facilitate monotone pattern routing of a set of nets in the floorplan by STAIRoute. The area/number balanced floorplan bipartitionining is a multi-objective optimization problem and known to be NP-hard \cite{majum2}. No existing approaches considered bend minimization as an objective and some of them incurred higher runtime overhead. In this paper, we present a Greedy as well as randomized neighbor search based staircase wave-front propagation methods for obtaining optimal bipartitioning results for minimal bend routing through multiple routing layers, for a balanced trade-off between routability, wirelength and congestion. Experiments were conducted on MCNC/GSRC floorplanning benchmarks for studying the variation of early via count obtained by STAIRoute for different values of the trade-off parameters ($\gamma, \beta$) in this multi-objective optimization problem, using $8$ metal layers. We studied the impact of ($\gamma, \beta$) values on each of the objectives as well as their linear combination function $Gain$ of these objectives.
PPM- and GMSK-based hybrid modulation technique for optical wireless communication cellular backhaul channel

In this study, a hybrid modulation technique called PPM–GMSK, based on the pulse position modulation (PPM) and phase-sampled Gaussian minimum shift keying (GMSK) technique, is investigated by considering log-normal and gamma–gamma turbulence as the channel-fading statistics. The analytical bit error rate (BER) and outage probability of the proposed hybrid modulation are derived. The results show that the hybrid modulation technique is an efficient technique to improve the performance of optical wireless communication for a cellular backhaul channel. Compared to 2-PPM and minimum shift keying (MSK), the proposed hybrid scheme has a less BER and a reduced amount of outage probability. Moreover, to achieve a reliable voice communication at 2 Gbps bit rate and BER of 10 − 3 under a weak turbulence effect, the PPM–GMSK has a coverage distance of about 3 km, whereas for PPM and MSK it is 2.6 and 2.4 km, respectively.
Machine Representation of Data Analyses: Towards a Platform for Collaborative Data Science

Artiﬁcial intelligence and data science play an increasingly important role in solving today’s scientiﬁc and social challenges. To be successful, the data-driven approach to social good requires effective collaboration between data scientists, subject-matter experts, policymakers, and other stakeholders. We envision a cloud platform for data science that would facilitate collaboration between stakeholders and possess AI capabilities for discovering, benchmarking, and organizing data analyses. Here we present a foundational technology motivated by this vision. Our system automatically extracts a high-level dataﬂow graph from a data analysis. The graph describes how data ﬂows through an analysis pipeline, including which statistical methods are used and how they ﬁt together. The system requires no special annotations from the data analyst and consumes analyses written in Python using standard tools, such as Scikit-learn and StatsModels. In this paper, we explain how our system works and how it ﬁts into our larger vision for a collaborative data science platform.
Learning to Predict Driver Behavior from Observation

This paper focuses on modeling and predicting human driving behavior, with the long term goal of anticipating the behavior of the driver before dangerous situations occur. We formulate this problem as a Learning from Demonstration problem, and show how standard supervised learning methods do not perform well in this task. The main contribution of this paper is a new approach we call indirect prediction . The key idea of indirect prediction is not to predict the behavior directly, but rather to build a model that predicts how certain features of the world state will change over time, and then use those to predict the necessary behavior in order to achieve those changes. We show how this apparently counterintuitive idea directly addresses one of the key reasons for which supervised learning does not perform well for LfD. In addition, we show how using ideas from context-based reasoning can also improve the accuracy of behavior modeling.
Anatomy of a Task: Towards a Tentative Taxonomy of the Mind

A standard model of mind will involve not only an architecture but also a set of capabilities. Ideally, the two should inform one another at a deep level, as an architecture is what both enables and constrains capabilities. In that spirit, we consider in some detail a routine and (deceptively) simple robotic task. From it, we build out a substantial list of capabilities that appear essential for a general-purpose execution of the task. We argue that this type of exercise is an indispensable step toward the establishment of a baseline for the comparison of cognitive architectures, and that the resulting taxonomy can inform the synthesis of a standard model of the mind.
Extraction of Named Entities from Semi-Structured Texts for Medical Domain

There are many unstructured text data stored in health information systems (HIS). To use this data for automatic processing it is necessary to be able to extract specific medical entities such as prescribed drugs, diagnosis, body conditions and so on. The article is concerned with building discriminatory models for solving the problem of named-entity extraction (NEE) for medical texts in Russian. Such models as Markov random fields and support vector machines (SVM) are considered. These methods showed better results in comparison with other NEE methods for English language corpuses. The application of these methods to text in Russian and moreover to medical text that burdened with specific medicine terminology is still a problem. To solve this problem the processes of feature extraction and models building are described in the context of said texts. Methods are evaluated on a corpus received from Federal Almazov North-West Medical Research Center. As a result, the most accurate method in according to F1-measure is chosen.
Measurement of Laser-lnduced Plasma: Stark Broadening �arameters of Pb(II) 2203.5 and 4386.5 A Spectral Lines

In this work, the Stark broadening parameters (widths and shifts) of the 2203.5 Á and 4386.5 Á Pb(II) spectral lines have been investigated and measured in laser-induced breakdown spectroscopy (LIBS), using a lead sample (99.999% purity). A Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser operating at its fundamental wavelength ( 1 O 640 Á), generating pulses of 290 mJ, 7 ns of duration, and a repeat frequency of 20 Hz, has been used far the ablation of said lead sample in vacuum and in a controlled argon atmosphere. A study to understand the expansion dynamics of the lead produced plasma was performed. The spectra have been obtained and measured at different time delays of the plasma evolution in the range of 0.15-9 µs, at which the temperature and electron number density are in the ranges of 28 200-8000 K and 1.3 x 10 1 7 to 3 x 10 15 cm- 3, respectively. A graphical representation of the evolution of temperature and electron number density versus 0.3 to 6.5 µs delay from the laser pulse is presented. The important effect of the different environment where the plasma expands has been pointed out. Local thermodynamic equilibrium conditions have been checked. The obtained results of the Stark widths and shifts at the different temperatures and densities of electrons have been compared with the limited data available in the literature. This study aims to obtain more accurate values far these parameters and also to establish regularities and similarities far said parameters.
IMPROMPTU: A Reactive and Distributed Resource Consolidation Manager for Clouds

In this paper, we present IMPROMPTU, a distributed resource consolidation manager for larger-scale commodity computing clouds. The main contribution of this work is two-fold. First, IMPROMPTU fully distributes the responsibility of resource consolidation management among autonomous node agents that have a one-to-one mapping with the physical machines in the cloud. Second, autonomous node agents manage virtual to physical machine resource consolidation using multiple criteria decision analysis (MCDA) through PROMETHEE II method. MCDA has been previously used within the context of computing systems, particularly in fields such as multi-agent systems, data mining, and wireless communications. However, to the best of our knowledge, IMPROMPTU represents the first fully distributed MCDA approach applied to the problem of autonomous resource consolidation management for commodity computing clouds. Moreover, IMPROMPTU improves on our previous studies by introducing key extensions to enhance the granularity of the MCDA model. Simulation results show that the proposed solution provides a strong alternative to prior resource consolidation management approaches for the key industry problem of mitigating SLA violations. This establishes solid groundwork for further applications and extensions of MCDA to this important problem domain.
Structure and reactivity of a siderophore-interacting protein from the marine bacterium Shewanella reveals unanticipated functional versatility

Siderophores make iron accessible under iron-limited conditions and play a crucial role in the survival of microorganisms. Because of their remarkable metal-scavenging properties and ease in crossing cellular envelopes, siderophores hold great potential in biotechnological applications, raising the need for a deeper knowledge of the molecular mechanisms underpinning the siderophore pathway. Here, we report the structural and functional characterization of a siderophore-interacting protein from the marine bacterium Shewanella frigidimarina NCIBM400 (SfSIP). SfSIP is a flavin-containing ferric-siderophore reductase with FAD- and NAD(P)H-binding domains that have high homology with other characterized SIPs. However, we found here that it mechanistically departs from what has been described for this family of proteins. Unlike other FAD-containing SIPs, SfSIP did not discriminate between NADH and NADPH. Furthermore, SfSIP required the presence of the Fe2+-scavenger, ferrozine, to use NAD(P)H to drive the reduction of Shewanella-produced hydroxamate ferric-siderophores. Additionally, this is the first SIP reported that also uses a ferredoxin as electron donor, and in contrast to NAD(P)H, its utilization did not require the mediation of ferrozine, and electron transfer occurred at fast rates. Finally, FAD oxidation was thermodynamically coupled to deprotonation at physiological pH values, enhancing the solubility of ferrous iron. On the basis of these results and the location of the SfSIP gene downstream of a sequence for putative binding of aerobic respiration control protein A (ArcA), we propose that SfSIP contributes an additional layer of regulation that maintains cellular iron homeostasis according to environmental cues of oxygen availability and cellular iron demand.