Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Tier Fog Architecture: A New Delay-Tolerant Network for IoT Data Processing

In order to gather the full profits of Internet of Things (IoT) technologies, it will be necessary to provide efficient networking and computing infrastructure to support low latency and fast response for IoT applications. In this paper, we introduce a new fog architecture for IoT applications. We propose to deploy servers at the network fog level as a tree hierarchy, to efficiently use the cloud resources to serve the peak loads from devices. To schedule different devices demands, we develop an optimal workload placement method by solving a mixed nonlinear integer programming (MNIP). Then, the optimal solution is aggregated over different tiers using the Simulated Annealing Algorithm (SAA) to find out the optimal allocation using numerical iterations. The advantage of the proposed architecture is proved over different performance metrics and trough a probabilistic model and an analytic comparison.
Non-Orthogonal Pilot Designs with Collision Detection Capability for Grant-Free Access

In this paper we consider a densely deployed phantom cell system providing a low latency network for massive connectivity. Non-orthogonal pilot designs serve as a promising solution to support a large number of users but fast collision detection at the receiver is needed for low latency networks. Recently, a nice solution based on an on-off type non-orthogonal pilot design with collision detection capability has been proposed. It can serve more users than the orthogonal pilot design but at the cost of degraded channel estimation performance compared to the orthogonal optimum pilot design. We propose a new non-orthogonal pilot design with collision detection capability and improved channel estimation performance. An optimum threshold based detection criteria is developed. We further show that a dynamically calculated optimum threshold outperforms a fixed threshold based detection. Next, we investigated non-orthogonal pilot designs for fractional bandwidth allocation. We propose two new non-orthogonal pilot designs for physical resource block (PRB) based resource allocation. Both of the new designs support fast collision detection at the receiver. Performance evaluation results show that the proposed schemes provide equivalent or better channel estimation performance and support much more users than the orthogonal pilots defined in the current 4G standards.
In search of a longitudinal animal model of evoked swallow function

A malfunction or impairment of swallow function can potentiate aspiration events and interfere with both quality of life and survival. Establishing an animal model for swallow research would provide a better understanding of its pathophysiology and would also allow for the development and validation of physiologically based clinical interventions to improve swallow function. Two requirements define the ideal model for longitudinal exploration: 1) identification of species similar to human in form and function; and 2) provision for reliable and reproducible evoked swallow under general anesthesia and one that would also support a longitudinal study design.
Quantitative Analysis of Seven New Prostate Cancer Biomarkers and the Potential Future of the ‘Biomarker Laboratory’

Prostate cancer is the third highest cause of male mortality in the developed world, with the burden of the disease increasing dramatically with demographic change. There are significant limitations to the current diagnostic regimens and no established effective screening modality. To this end, research has discovered hundreds of potential ‘biomarkers’ that may one day be of use in screening, diagnosis or prognostication. However, the barriers to bringing biomarkers to clinical evaluation and eventually into clinical usage have yet to be realised. This is an operational challenge that requires some new thinking and development of paradigms to increase the efficiency of the laboratory process and add ‘value’ to the clinician. Value comes in various forms, whether it be a process that is seamlessly integrated into the hospital laboratory environment or one that can provide additional ‘information’ for the clinical pathologist in terms of risk profiling. We describe, herein, an efficient and tissue-conserving pipeline that uses Tissue Microarrays in a semi-automated process that could, one day, be integrated into the hospital laboratory domain, using seven putative prostate cancer biomarkers for illustration.
Model selection for discrete Markov random fields on graphs

FRONDANA, I. M. Model selection for discrete Markov random fields on graphs. PhD Thesis Institute of Mathematics and Statistics, University of São Paulo, São Paulo, 2016. In this thesis we propose to use a penalized maximum conditional likelihood criterion to estimate the graph of a general discrete Markov random field. We prove the almost sure convergence of the estimator of the graph in the case of a finite or countable infinite set of variables. Our method requires minimal assumptions on the probability distribution and contrary to other approaches in the literature, the usual positivity condition is not needed. We present several examples with a finite set of vertices and study the performance of the estimator on simulated data from theses examples. We also introduce an empirical procedure based on k−fold cross validation to select the best value of the constant in the estimator’s definition and show the application of this method in two real datasets.
Impact of Resource Blocks Allocation Strategies on Downlink Interference and SIR Distributions in LTE Networks: A Stochastic Geometry Approach

We propose a model based on stochastic geometry to assess downlink interference and signal over interference ratio (SIR) in LTE networks. The originality of this work lies in the proposition and combination of resource blocks assignment strategies, transmission power control, and realistic traffic patterns into a stochastic geometry model. For this model, we compute the first two moments of interference. They are used to parameterize its distribution from which we deduce the SIR distribution. Outage and transmission rates (modulation and coding rate) are then derived to evaluate the system performance. Simulations that cover a large set of scenarios show the accuracy of our proposal and allow us to compare these strategies with more complex ones that aim to minimize global interference. Numerical evaluations highlight the behavior of the LTE network for different traffic patterns/load, eNodeB density, and amount of resource blocks and offer insights about possible parameterization of LTE networks.
Testosterone Levels in Third Trimester in Polycystic Ovary Syndrome: Odense Child Cohort

Background Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenism. In pregnancy, testosterone levels may be higher in women with PCOS compared with controls. Aims To compare total testosterone (TT), free testosterone (FT), and sex hormone-binding globulin (SHBG) levels in third-trimester pregnant women with PCOS and controls and to establish reference ranges for TT, FT, and SHBG in PCOS and controls. Methods The study was part of the prospective study, Odense Child Cohort. PCOS was diagnosed by questionnaires and/or patient records. Fasting blood samples were collected at gestational week 28 and plasma TT was measured by liquid chromatography-tandem mass spectrometry in women with PCOS (n = 145) and in women without PCOS (controls, n = 1341). Results Levels of TT (mean, 2.4 vs 2.0 nmol/L) and FT (mean, 0.005 vs 0.004 nmol/L) were higher, whereas SHBG levels (mean, 447 vs 477 nmol/L) were lower in women with PCOS vs controls (all P < 0.001). Reference intervals for TT, FT, and SHBG in women with PCOS and controls were overlapping, and partitioning of reference intervals was an ambiguous decision. In multiple regression analyses, TT and FT levels were positively associated with PCOS status and BMI and inversely associated with age and parity. Offspring sex did not predict maternal TT and FT. Conclusions TT and FT levels were higher in third-trimester pregnant women with PCOS compared with controls. Separate reference interval for FT in women with PCOS should be considered.
Roles for ClpXP in regulating the circadian clock in Synechococcus elongatus

Significance Protein degradation is critical for modulating cellular responses to the environment. Here, we show that proteome remodeling by ClpX, ClpP1, and ClpP2 controls circadian clock function in Synechococcus elongatus PCC 7942. The results suggest that protein degradation by the ClpXP1P2 protease is required to maintain normal periodicity of the circadian clock and to limit the temporal range over which the phase can be reset in response to darkness. Moreover, ClpX-mediated protein unfolding, independent of its functions with the ClpP protease subunits, is required to relieve a clock-induced cell division checkpoint. Together, the results suggest that the circadian clock and ClpX act together to time the window in every 24-h day in which cell division is inhibited. In cyanobacteria, the KaiABC posttranslational oscillator drives circadian rhythms of gene expression and controls the timing of cell division. The Kai-based oscillator can be reconstituted in vitro, demonstrating that the clock can run without protein synthesis and degradation; however, protein degradation is known to be important for clock function in vivo. Here, we report that strains deficient in the ClpXP1P2 protease have, in addition to known long-period circadian rhythms, an exaggerated ability to synchronize with the external environment (reduced “jetlag”) compared with WT strains. Deletion of the ClpX chaperone, but not the protease subunits ClpP1 or ClpP2, results in cell division defects in a manner that is dependent on the expression of a dusk-peaking factor. We propose that chaperone activities of ClpX are required to coordinate clock control of cell division whereas the protease activities of the ClpXP1P2 complex are required to maintain appropriate periodicity of the clock and its synchronization with the external environment.
A Comprehensive Review of Technologies Used for Screening, Assessment, and Rehabilitation of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is an umbrella term for a wide range of developmental disorders. For the past two decades, researchers proposed the use of various technologies in order to tackle specific symptoms of the disorder. Although there exist many literature reviews about screening, assessment, and rehabilitation of ASD, no comprehensive survey of types of technologies in all defined symptoms of ASD has been presented. Therefore, in this paper a comprehensive survey of previous studies has been presented in which the studies are classified into three main categories, and several sub-categories, and three main technologies. An analysis of the number of studies in each category and sub-category is given to help researchers decide on areas which need further investigation. The analysis show that the majority of studies fall into the software-based systems technology category. Finally, a brief review of studies in each category of ASD is presented for each type of technology. As a result, this paper also helps researchers to obtain an overview of the typical methods of using a specific technology in ASD screening, assessment, and rehabilitation.
Service-Oriented Robot Software Development Framework for Distributed Heterogeneous Platforms

A key technical challenge to make multiple robots work together is software development how to specify the mission at the user level and how to program each robot separately. In this paper, we propose a novel software development framework in which a mission is specified with a novel scripting language and the individual robot behavior with an extended dataflow model at the task level. How to relate these two specifications and how to generate the robot code automatically are also addressed in the proposed framework. The viability of the proposed methodology is validated with a preliminary experiment.
Cellular Studies of an Aminoglycoside Potentiator Reveal a New Inhibitor of Aminoglycoside Resistance

Aminoglycosides are a group of broad‐spectrum antibiotics that have been used in the clinic for almost a century. The rapid spread of bacterial genes coding for aminoglycoside‐modifying enzymes has, however, dramatically decreased the utility of aminoglycosides. We have previously reported several aminoglycoside potentiators that work by inhibiting aminoglycoside N‐6′‐acetyltransferase, one of the most common determinants of aminoglycoside resistance. Among these, prodrugs that combine the structure of an aminoglycoside with that of pantothenate into one molecule are especially promising. We report here a series of cellular studies to investigate the activity and mechanism of action of these prodrugs further. Our results reveal a new aminoglycoside resistance inhibitor, as well as the possibility that these prodrugs are transformed into more than one inhibitor in bacteria. We also report that the onset of the potentiators is rapid. Their low cell cytotoxicity, good stability, and potentiation of various aminoglycosides, against both Gram‐positive and Gram‐negative bacteria, make them interesting compounds for the development of new drugs.
A Model for Infrastructure Sharing in mmWave Cellular Networks

Competing cellular operators aggressively share infrastructure in many major US markets. If operators also share spectrum licenses, intra- cellular interference will become correlated with inter-cellular interference. We propose a mathematical framework to model a two-operator millimeter-wave (mmWave) cellular network with co- located base-stations (BSs). We then characterize the SINR distribution for an arbitrary network to understand the impact of varying the spatial correlation between the operators' networks. An interesting observation is that sharing spectrum and infrastructure yields a higher rate coverage probability for higher rate thresholds, but has a lower coverage for lower thresholds. This suggests that networks catering for low-rate, limited-QoS devices, are at a disadvantage when spectrum and infrastructure are shared.
Learning Cooperative Policy among Self-Driving Vehicles to Relieve Traffic Jams

We propose a novel driving policy which is a velocity control for self-driving vehicles to relieve trafﬁc jams. Although the driving policy in previous research was empirically designed for a given trafﬁc situation, which meant that the driving policy required to be reconﬁgured for every trafﬁc situation and every change in trafﬁc, we propose a driving policy that is learned by a learning agent via reinforcement learning using the data collected from the self-driving vehicles during simulation. The driving policy is relayed to the smart vehicles, which, in turn, are guided by the driving policy. To test and evaluate our proposed driving policy, we conducted trafﬁc ﬂow simulations with manually driven and self-driving vehicles in several scenar-ios wherein the two key parameters, vehicle density and self-driving vehicle penetration rate, are assigned different values. Our ﬁndings show that a driving policy for self-driving vehicles does relieve trafﬁc jams in conditions such as (1) when the vehicle density is 42 vehicles/km and the penetration of the self-driving vehicle is 10% of the total trafﬁc, and (2) when the vehicle density is 50 vehicles/km and the penetration of the self-driving vehicle is 70% of the total trafﬁc (at which point trafﬁc ﬂow is nearly optimized). In addition, we found that inter-vehicle communication among self-driving vehicles provides real-time trafﬁc information that re-lieves trafﬁc jam even more effectively.
Optimal Auditing on Smart-Grid Networks

As the existing power grid becomes increasingly complex, the deployment of Smart Grids-which can significantly improve the stability and efficiency of power infrastructure- has seen increasing interest. However, with new technology comes new security concerns. Recent work has shown that fabricating valid but malicious messages on a Smart Grid's SCADA network can cause widespread power outages. Moreover, the large scale, complexity, and tight constraints of these networks makes deploying in-line detection systems insufficient. A common approach is to instead conduct whole-network audits by temporarily duplicating & forwarding all network traffic to a server dedicated to detecting malicious content. This is usually done by taking advantage of port- mirroring to duplicate the packets received with minimal overhead. However, the operation of these audits sees a number of challenges. For instance, each router used to collect traffic demands physical set-up - thus there is a real cost to needlessly high coverage. In this work, we consider the problem of efficiently finding the minimal set of routers in the SCADA network to use for auditing traffic. This efficiency is critical for enabling timely auditing. Similar versions of this problem have seen study. However, they suffer either from a severe mismatch w.r. t. the problem domain, or from serious scalability concerns. This motivates us to devise a novel (2+\theta)(ln|V|+1) approximation algorithm for this problem with a 2- approximation for the special case of tree networks. We experimentally evaluate our solution and compare it to an optimal IP formulation, finding that it performs near-optimally on small networks and significantly outperforms heuristics in all cases.
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection

This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL). We describe the architecture of the model and its performance in the shared task of the ACL 2018 Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA). This hybrid approach yields comparably high false positive rate (FPR = 0.1274) and precision (Pd= 0.7519; Pi= 0.6311), but low recall (Rd = 0.3035; Ri = 0.1696 ) in grammatical error detection and identification tasks. Additional statistical information and linguistic rules can be added to enhance the model performance in the future.
University students in the educational field and Wikipedia vandalism

Wikipedia is an online, open and free encyclopaedia edited collaboratively. Today it is the fifth most visited website and the most used online encyclopaedia. Volunteer editors from around the world can edit its content, allowing users to intentionally provide incorrect information. This research aims to find the extent to which a particular group of university students vandalize Wikipedia, while also exploring their perceptions of vandalism. Data is obtained from a questionnaire sent to university students in educational psychology, early and primary childhood education, and related master's programs, as well as a focus group involving a sample of these students and interviews with editors in charge of maintaining Wikipedia. Results show that only a small percentage of students do in fact vandalize. In line with the implicit theories approach, it seems that students and editors have some preconceived ideas (boredom, amusement, or ideological motivations) about what pushes individuals to vandalize.
Parallel self-verified solver for dense linear systems

This thesis presents a free, fast, reliable and accurate solver for point and interval dense linear systems. The idea was to implement a solver for dense linear systems using a verified method, interval arithmetic and directed roundings based on MPI communication primitives associated to optimized libraries, aiming to provide both self-verification and speed-up at the same time. A first parallel implementation was developed using the C-XSC library. However, the CXSC parallel method did not achieve the expected overall performance since the solver was not 100% parallelized due to its implementation properties (special variables and optimal scalar product). C-XSC did not seem to be the most efficient tool for time critical applications, consequently we proposed and implemented a new sequential verified solver for dense linear systems for point and interval input data using both infimum-supremum and midpoint-radius arithmetic based on highly optimized libraries (BLAS/ LAPACK). Performance tests showed that the midpointradius algorithm needs approximately the same time to solve a linear system with point or interval input data, while the infimum-supremum algorithm needs much more time for interval data. Considering that, midpoint-radius arithmetic was the natural choice for the next step of this work: the parallel implementation. We then developed a new parallel verified solver for point and interval dense linear systems using midpoint-radius arithmetic, directed roundings and optimized libraries (PBLAS/ ScaLAPACK). The performance results showed that it was possible to achieve very good speed-ups in a wide range of processor numbers for large matrix dimensions for both point and interval input data. In order to overcome the memory limitation imposed by the generation of the whole matrix in one processor, we decided to generate sub-matrices of the input matrix individually on each available node, allowing a better use of the global memory. These modifications made it possible to solve dense systems with up to 100 000 dimension. In addition to that, in order to investigate the portability of the proposed solution, during this thesis, tests were performed using 3 different clusters in Germany (ALiCEnext, XC1 and IC1) with distinct configurations presenting significant results, indicating that the parallel solver scales well even for very large dense systems over many processors. Further investigations were done in two directions: study of the use of dedicated threads to speed up the solver of dense linear systems on shared memory, specially dual-core processors and the use of the ideas presented in this thesis to speed-up the C-XSC library.
Posttraumatic Stress in U.S. Marines: The Role of Unit Cohesion and Combat Exposure.

Combat exposure is a consistent predictor of posttraumatic stress (PTS). Understanding factors that might buffer the effects of combat exposure is crucial for helping service members weather the stress of war. In a study of U.S. Marines returning from Iraq, hierarchical multiple regression analyses revealed that unit cohesion and combat exposure predicted PTS, depression, and anger. Furthermore, results indicated that unit cohesion may be an important buffer, possibly limiting the development of PTS and depression following combat exposure.
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets

While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, parts-of-speech (POS) taggers, chunkers have been developed. Named Entity Recognition (NER) is an important text analysis task which is not only informative by itself, but is also needed for downstream NLP tasks such as semantic role labeling. In this work, we present an exploration of automatic NER of code-mixed data. We compare our method with existing off-the-shelf NER tools for social media content,and find that our systems outperforms the best baseline by 33.18 % (F1 score).