Miracle-dz/UniFAR-FTUs huggingface dataset:
Water in Comet C/2003 K4 (LINEAR) with Spitzer

We present sensitive 5.5-7.6 μm spectra of comet C/2003 K4 (LINEAR) obtained on 2004 July 16 (rh = 1.760 AU, ΔSpitzer = 1.409 AU, phase angle = 35.4°) with the Spitzer Space Telescope. The ν2 vibrational band of water is detected with a high signal-to-noise ratio (≳50). Model fitting to the best spectrum yields a water ortho-to-para ratio of 2.47 ± 0.27, which corresponds to a spin temperature of 28.5 K. Spectra acquired at different offset positions show that the rotational temperature decreases with increasing distance from the nucleus, which is consistent with evolution from thermal to fluorescence equilibrium. The inferred water production rate is (2.43 ± 0.25) × 1029 molecules s-1. The spectra do not show any evidence for emission from PAHs and carbonate minerals, in contrast to results reported for comets 9P/Tempel 1 and C/1995 O1 (Hale-Bopp). However, residual emission is observed near 7.3 μm, the origin of which remains unidentified.
A Stochastic Approach to Improve Macula Detection in Retinal Images

In this paper, we present an approach to improve detectors used in medical image processing by fine-tuning their parameters for a certain dataset. The proposed algorithm uses a stochastic search algorithm to deal with large search spaces. We investigate the effectiveness of this approach by evaluating it on an actual clinical application. Namely, we present promising results with outperforming four state-of-the-art algorithms used for the detection of the center of the sharp vision (macula) in digital fundus images.
Communication constraints in coordinated consensus problems

The interrelationship between control and communication theory is becoming of fundamental importance in many distributed control systems. Particular examples are systems comprised of multiple agents. When it comes to coordinately control a group of autonomous mobile agents in order to achieve a common task, communications constraints impose limits on the achievable control performance. Starting from an emerging problem, studied in the robotics and control communities, called consensus or state agreement problem, we characterize the relationship between the amount of information exchanged by the agents and the rate of convergence to the agreement. In particular we show that communication networks that exhibit particular symmetries yield slow convergence, if the amount of information exchanged does not scale with the number of agents. On the other hand, if we allow exchange of logarithmic quantized data, the control performance significantly improves with little growth of the required communication effort
Real time multi stage unsupervised intelligent engine for NIDS to enhance detection rate of unknown attacks

The most traditional technique for Network Intrusion Detection Systems (NIDSs) is misuse detection which only detects well-known attacks by matching the current behavior of network with pre-defined attacks' signatures. Providing attacks' signatures is costly, time consuming and with the explosive growing number of zero day attacks, using misuse detection mechanism is not an efficient solution. Other techniques which applied on NIDS are supervised and semi-supervised anomaly detection systems which can detect novel attacks by comparing the current behavior of the network to the training sample; however producing labeled or attack-free dataset is difficult for training the engine. Current NIDS solutions monitor bytes, packets' payload or network flows to detect intrusions. Today it is difficult to monitor the payload of packets in high speed network (1-10 Gbps) and recent network attacks are becoming more complex and analyzing only the payload of packets will not produce enough information for detection engine. In this paper we propose a new Real Time Unsupervised Network Intrusion Detection System (RTUNIDS) which monitor network flows in two windows with different sizes and detects network attacks by correlating outliers from multiple clusters. The proposed solution has the ability of detecting different types of intrusions in realtime such as DOS, DDOS, scanning, distribution of worms and any other network attacks which produce huge amount of network traffic and in the meanwhile it detects Bot-Master if the detected attack lunched by Bots.
Bayesian Phylogeography Finds Its Roots

As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
Sinners and Saints: The Role of Social Standing Evidence in Capital Sentencing

ABSTRACT The authors’ purpose was to examine how the social standing of victims and defendants impacted capital sentencing decision making. Participants were 305 death-qualified community members recruited through Amazon’s Mechanical Turk workforce who acted as mock jurors in the sentencing phase of a capital trial. This experiment was a 3 Victim Impact Statement Social Standing (none, low, high) × 3 Execution Impact Evidence Social Standing (none, low, high) fully crossed, between-groups factorial design. Social standing of the victim did not directly or indirectly impact sentencing decisions, but the introduction of any form of execution impact evidence resulted in more positive evaluations of the defendant and fewer death sentences. These effects were moderated by pro-death penalty attitudes. Our findings have implications for how prosecutors and defense attorneys use character evidence to humanize victims and defendants.
PAN@FIRE 2013: Overview of the Cross-Language !ndian News Story Search (CL!NSS) Track

The automatic alignment of documents in a quasi-comparable corpus is an important research problem for a resource poor cross-language technologies. News stories form one of the most prolific and abundant language resource. This edition of PAN@FIRE task, cross-language ! ndia news story search (CL! NSS), continues to address the news story linking task across languages English and Hindi. We present the overview of the track with results and analysis.
Asymptotic rate limits for randomized broadcasting with network coding

Motivated by peer-to-peer content distribution and media streaming applications, we study the broadcasting problem in a time-discretized model, with integer valued upload and download capacity constraints at nodes. We analyze both deterministic centralized and randomized decentralized protocols that can achieve optimal packet receiving rates at the nodes. In particular, we consider a simple scheme that requires each node, in each time slot, to transmit to a random neighbor that is not yet chosen by any other nodes in that slot. We prove that such a surprisingly simple scheme can asymptotically achieve the optimal receiving rates in complete graphs with homogeneous node capacity. The proof involves applying randomized network coding and deriving the max-flow bounds achieved in the resulting transmission schedule. We extend the results to more general topologies, and bound the performance of randomized neighbor selection with randomized network coding.
An exhaustive generation algorithm for Catalan objects and others

The paper presents a CAT generation algorithm for Dyck paths with fixed length n. It is the formalization of a method for the exhaustive generation of these paths which can be described by two equivalent strategies. The former uses a rooted tree, the latter lists the paths and provides a visit of the nodes of the tree, basing on three simple operations. These constructions are strictly connected with ECO method and can be encoded by a rule, very similar to the succession rule in ECO, with a finite number of labels for each n. Moreover, with a slight variation, this method can be generalized to other combinatorial classes like Grand Dyck or Motzkin paths.
Constructing Deal Networks: Brokers as Network “Architects” in the U.S. IPO Market and Other Examples

We introduce the concept of the network architect to extend theory explaining how brokers create and manage structural holes in mediated markets. We argue that a broker's social resources and dependence on the market, along with exogenous deal conditions, influence the broker's motivations and willingness to make tradeoffs between long-term and short-term considerations when constructing deal networks. We develop our model and propositions in the context of the U.S. initial public offerings market and then generalize these arguments to other market contexts.
INFORMATION TECHNOLOGY AND CONTROL NEEDS FOR IN-SITU RESOURCE UTILIZATION

With the rapidly increasing performance of information technologies, a new capability is being developed that holds the clear promise of greatly increased exploration possibilities, along with dramatically reduced design, development, and operating costs. In addition, specific technologies such as neural nets will provide a degree of machine intelligence and associated autonomy which has previously been unavailable to the mission and spacecraft designer and the system operator. One of the most promising applications of these new information technologies is to the area of in-situ resource utilization. Useful resources such as oxygen, carbon dioxide, methane, and water can be extracted and/or generated from planetary atmospheres, to be used for propulsion and life-support needs. This can provide significant savings in the launch mass and costs. This paper will present the concepts that are currently under investigation and evelopment for mining the Martian atmosphere, such as temperature-swing adsorption, zirconia electrolysis etc., to create propellants and life-support materials. This description will be followed by an analysis of the information technology and control needs for the reliable and autonomous operation of such processing plants in a fault tolerant manner. Finally, there will be a brief discussion of the software verification and validation process so crucial to the implementation of missioncritical software.
Effects of Aging and Spectral Shaping on the Sub-cortical (Brainstem) Differentiation of Contrastive Stop Consonants

Dania Rishiq, Ph.D., is an Assistant Professor of Audiology at the University of South Alabama. She received her doctoral degree in Speech and Hearing Science from the University of Tennessee in May 2013. Her field of specialty is physiological and neural correlates of auditory perception. Her recent research interests include speech-evoked auditory brainstem response, role of attention in audiovisual speech recognition, pediatric speech in noise tests, and the role of cognition in evaluating cochlear implant candidacy.
Ontogeny of O2 and CO2//H+ chemosensitivity in adrenal chromaffin cells: role of innervation

The adrenal medulla plays a key role in the physiological responses of developing and mature mammals by releasing catecholamines (CAT) during stress. In rodents and humans, the innervation of CAT-producing, adrenomedullary chromaffin cells (AMCs) is immature or absent during early postnatal life, when these cells possess ‘direct’ hypoxia- and CO2/H+-chemosensing mechanisms. During asphyxial stressors at birth, these mechanisms contribute to a CAT surge that is critical for adaptation to extra-uterine life. These direct chemosensing mechanisms regress postnatally, in parallel with maturation of splanchnic innervation. Here, we review the evidence that neurotransmitters released from the splanchnic nerve during innervation activate signaling cascades that ultimately cause regression of direct AMC chemosensitivity to hypoxia and hypercapnia. In particular, we consider the roles of cholinergic and opioid receptor signaling, given that splanchnic nerves release acetylcholine and opiate peptides onto their respective postsynaptic nicotinic and opioid receptors on AMCs. Recent in vivo and in vitro studies in the rat suggest that interactions involving α7 nicotinic acetylcholine receptors (nAChRs), the hypoxia inducible factor (HIF)-2α signaling pathway, protein kinases and ATP-sensitive K+ (KATP) channels contribute to the selective suppression of hypoxic chemosensitivity. In contrast, interactions involving μ- and/or δ-opiod receptor signaling pathways contribute to the suppression of both hypoxic and hypercapnic chemosensitivity, via regulation of the expression of KATP channels and carbonic anhydrase (CA I and II), respectively. These data suggest that the ontogeny of O2 and CO2/H+ chemosensitivity in chromaffin cells can be regulated by the tonic release of presynaptic neurotransmitters.
Least cost generation expansion planning with wind plant using differential evolution algorithm

The Generation Expansion Planning (GEP) problem is a large scale, mixed integer and the most complicated optimization problem. In this paper, an application of Differential Evolution (DE) algorithm for solving the GEP problem is presented. In the formulation, the objective function is to be minimized and find out the least cost GEP problem. The GEP problem is considered for a test system of 14-years planning horizon having five conventional type and one non-conventional type of plants. The results are obtained for GEP problem with and without using the non-conventional plant (Wind plant). The results obtained for GEP problem with and without the Wind plant are compared.
Complete Privacy Preserving Auditing for Data Integrity in Cloud Computing

Homomorphic encryption has long been acclaimed solution for data security and privacy problems in cloud computing. However, the high complexities of operations associated with the existing schemes have prohibited them from being used for practical applications. In this paper we propose a new protocol for verifying the integrity of the data stored at the remote cloud server, based on a practical version of integers based homomorphic encryption. The proposal is the first attempt in combining the data integrity and confidentiality in new ways to provide an impending solution. The practicality of the resulting scheme is demonstrated through exhaustive experimentation while the security of the scheme is proved with thorough cryptanalysis.
Encouraging Metacognition & Self-Regulation in MOOCs through Increased Learner Feedback

Learning analytics for learners has the ability to greatly improve learners’ self-regulation. Current learner dashboards are mostly providing learners with an isolated view of their learning behavior, while we believe learners will gain more from a comparison of their own behavior with that of successful peer learners. In this work-in-progress demonstration we describe our design of a Learning Tracker widget that provides MOOC learners with timely and goal-oriented (i.e. towards passing the course) feedback in a manner that encourages reﬂection and self-regulation. We also present some preliminary ﬁndings which show how exposure to feedback can signiﬁcantly increase student success and engagement.
Motivational orientations of American and Russian learners of French as a foreign language

Author: Vinogradova, Zoia Ph.D. Institution: Purdue University Degree Received: December 2016 Title: Motivational Orientations of American and Russian Learners of French as a Foreign Language Major Professor: Mariko Wei and Colleen Neary-Sundquist This study seeks to examine and compare motivational orientations of French learners across different dimensions: cultural background (USA vs. Russia), educational modality and age (college students vs. private courses learners), gender, and time of studying foreign language. 613 American and Russian learners of French completed the questionnaire addressing 10 motivational factors to study French language. Despite differences in nationality, age, educational background and learning experience, all groups of participants produced nearly identical motivational rankings. The rankings are topped by the Travelling orientation, which seems to be universally appealing, followed by the orientations within the Idealistic motivational cluster (Aesthetic Factor, Culture, Knowledge, and Ideal Self). The Pragmatic motivational cluster (Instrumental orientation, which is sometimes coupled to Emigration and Friendship dimensions) is by far less important. This disposition is also confirmed by the qualitative data. With regard to specific orientations it has been found that US learners score consistently higher in Sociability motivation, whereas Russians score higher in the Peers’ Encouragement and Aesthetic categories. In regard to gender differences, this study shows that male students appear to be more personable, e.g. among American learners males consistently outscore females in the Friendship category. Referring to age differences, it was found that the overall level of motivation tends to decline with age.
A S ECURE KEY COMPUTATION PROTOCOL FOR SECURE GROUP COMMUNICATION WITH PASSWORD BASED AUTHENTICATION

Providing security in group communication is more essential in this new network environment. Authentication and Confidentiality are the major concerns in secure group communication. Our proposed approach uses an authenticated group key transfer protocol that relies on trusted key generation center (KGC). KGC computes group pair for each individual and transport the pair of values to all group members in a secured manner. Password based authentication mechanism is used to avoid the illegal member access in a group Also, the proposed approach facilitates efficient key computation technique such that only authorized group members will be able to computer and retrieve the secret key and unauthorized members cannot retrieve the key. The proposed algorithm is more efficient and relies on NP class. In addition, the distribution of key is also safe and secure. Moreover, the pair generated for the computation of key is also very strong since the cryptographic techniques are used which provides efficient computation.
Analysis of virtual machine creation characteristics on virtualized computing environment

Virtualization technology is one of the key technology by explosive growth in several computing paradigm including cloud computing. Specially, some virtualization software stacks widely support virtualization mechanism on various system types including a system which doesn't support hardware level virtual-ization. Thus the virtualization technique allows various systems to become a computing platform in virtualized computing environment. However, some virtualized systems create critical utilization by computation power during creation of virtual machine. In our research, we analyze creation characteristics of virtual machine on various dimensions like virtual machine, virtualized system, and storage server. To perform our study, we implement an automated measurement method and test bed which employs Xen as hypervisor on top of the cluster systems with storage server for distributing virtual disk images. Our experiments indicate that computational power of virtualized system affects the boot time of virtual machine in our test bed even the heterogeneity is relatively row. Also, the creation of virtual machine creates significant overhead (100%) at initial phase on virtualized system in worst case. Additionally, we provide overhead analysis on storage server through generating workloads by scaling virtual machines.
A research paper recommender system based on spreading activation model

With the progress of digital technology and the development of the web, digital library need to provide on-line services to meet the diversified demands of individual patrons and to offer more personalized services. A personalized research paper recommender system is proposed in this paper. It constructs user profile based on concept tree to overcome the drawbacks of the traditional vector space model. A spreading activation model is employed to search for user community with similar interests. Finally, its accuracy and availability is verified based on data extracted from the database of National Science and Technology Library.
TrkA‐immunoreactive profiles in the central nervous system: Colocalization with neurons containing p75 nerve growth factor receptor, choline acetyltransferase, and serotonin

The present investigation used an antibody directed against the extracellular domain of the signal transducing nerve growth factor recepto, trkA, to reveal immunoreactive perikarya or fibers within the olfactory bulb and tubercle, cingulate cortex, nucleus accumbens, striatum, endopiriform nucleus, septal/diagonal band complex, nucleus basalis, hippocampal complex, thalamic paraventricular and reuniens nuclei, periventricular hypothalamus, interpeduncular nucleus, mesencephalic nucleus of the fifth nerve, dorsal nucleus of the lateral lemniscus, prepositus hypoglossal nucleus, ventral cochlear nucleus, ventral lateral tegmentum, medial vestibular nucleus, spinal trigeminal nucleus oralis, nucleus of the solitary tract, raphe nuclei, and spinal cord. Colocalization experiments revealed that virtually all striatal trk‐Aimmunoreactive neurons (> 99%) coexpressed choline acetyltransferase (ChAT) but not p75 nerve growth factor receptor (NGFR). Within the septal/diagonal band complex virtually all trkA neurons (> 95%) coexpressed both ChAT and p75 NGFR. More caudally, dual stained sections revealed numerous trkA/ChAT (> 80%) and trkA/p75 NGFR (> 95%) immunoreactive neurons within the nucleus basalis. In the brainstem, raphe serotonergic neurons (45%) coexpressed trkA. Sections stained with a pan‐trk antibody that recognizes primarily trkA, as well as trkB and trkC, labeled neurons within all of these regions as well as within the hypothalamic arcuate, supramammilary, and supraoptic nuclei, hippocampus, inferior and superior colliculus, substantia nigra, ventral tegmental area of T'sai, and cerebellar Purkinje cells. Virtually all of these other regions with the exception of the cerebellum also expressed pan‐trk immunoreactivity in the monkey. The widespread expression of trkA throughout the central neural axis suggests that this receptor may play a role in signal transduction mechanisms linked to NGF‐related substances in cholinergic basal forebrain and noncholinergic systems. These findings suggest that pharmacological use of ligands for trkA could have beneficial effects on the multiple neuronal systems that are affected in such disorders as Alzheimer's disease. © 1994 Wiley‐Liss, Inc.