Miracle-dz/UniFAR-FTUs huggingface dataset:
Blurred Lines: Pathogens, Commensals, and the Healthy Gut

Detailed studies of the chickenmicrobiome have emerged in recent years, largely due to the impact of next-generation sequencing (NGS). We increasingly understand how the microbiome is important in health, in development of the gut and the immune system, and in maintenance of homeostasis. Manipulation of the microbiota directly through probiotics or antimicrobials or indirectly through feed and feed additives has long been used by the poultry industry to increase growth rates and feed conversion, to improve gut health, and to reduce the burden of pathogens and, in particular, to reduce the load of foodborne zoonotic pathogens such as Salmonella and Campylobacter. We can now begin to mechanistically determine how these treatments affect the microbiota and the wider host, and this understanding will allow us to use more targeted approaches in the future. In terms of food security, increasing yield is clearly a good thing. However, it is far from clear what represents a “healthy” microbiome, and the lines between what is a “harmless” commensal and what is a pathogen are often blurred. As such an understanding of themicrobial ecology of the gut and how this is affected by manipulation of the microbiome or indeed treatment of “pathogens” is essential in ensuring that treatments intended to improve health and productivity do not in fact cause more problems.
Clientelism and Voting Behavior: Evidence from a Field Experiment in Benin

The author conducted a field experiment in Benin to investigate the impact of clientelism on voting behavior. In collaboration with four political parties involved in the 2001 presidential elections, clientelist and broad public policy platforms were designed and run in twenty randomly selected villages of an average of 756 registered voters. Using the survey data collected after the elections, the author estimated the effect of each type of message by comparing voting behavior in the villages exposed to clientelism or public policy messages (treatment groups) with voting behavior in the other villages (control groups). The author found that clientelist messages have positive and significant effect in all regions and for all types of candidates. The author also found that public policy messages have a positive and significant effect in the South but a negative and significant effect in the North. In addition, public policy messages seem to hurt incumbents as well as regional candidates. Finally, the evidence indicates that female voters tend to have stronger preference for public policy platforms than male voters.
Background Paper on Measuring WASH and Food Hygiene Practices – Definition of Goals to be Tackled Post 2015 by the Joint Monitoring Programme

Prepared by a team from the Hygiene Centre at the London School of Hygiene and Tropical Medicine, The IRC International Water and Sanitation Centre and the International Centre for Diarrhoeal Disease Research, Bangladesh under contract to USAID contract No 3276 April 2012. The purpose of this background paper was to inform and stimulate discussion. It does not represent a consensus and the authors are not suggesting that any of the definitions, goals, targets or indicators proposed in the document are final or should necessarily be adopted.
Building a Better Netflow: Technical Report

Network operators need to determine the composition of the traffic mix on links when looking for dominant applications, users, or estimating traffic matrices. Cisco's NetFlow has evolved into a solution that satisfies this need by reporting flow records that summarize a sample of the traffic traversing the link. But sampled NetFlow has shortcomings that hinder the collection and analysis of traffic data. First, during flooding attacks router memory and network bandwidth consumed by flow records can increase beyond what is available ; second, selecting the right static sampling rate is difficult because no single rate gives the right tradeoff of memory use versus accuracy for all traffic mixes; third, the heuris-tics routers use to decide when a flow is reported are a poor match to most applications that work with time bins; finally , it is impossible to estimate without bias the number of active flows for aggregates with non-TCP traffic. In this paper we propose Adaptive NetFlow, deployable through an update to router software, which addresses many shortcomings of NetFlow by dynamically adapting the sampling rate to achieve robustness without sacrificing accuracy. To enable counting of non-TCP flows, we propose an optional Flow Counting Extension that requires augmenting existing hardware at routers. Both our proposed solutions readily provide descriptions of the traffic of progressively smaller sizes. Transmitting these at progressively higher levels of reliability allows graceful degradation of the accuracy of traffic reports in response to network congestion on the reporting path.
A Generalized Cauchy Distribution Framework for Problems Requiring Robust Behavior

Statistical modeling is at the heart of many engineering problems. The importance of statistical modeling emanates not only from the desire to accurately characterize stochastic events, but also from the fact that distributions are the central models utilized to derive sample processing theories and methods. The generalized Cauchy distribution (GCD) family has a closed-form pdf expression across the whole family as well as algebraic tails, which makes it suitable for modeling many real-life impulsive processes. This paper develops a GCD theory-based approach that allows challenging problems to be formulated in a robust fashion. Notably, the proposed framework subsumes generalized Gaussian distribution (GGD) family-based developments, thereby guaranteeing performance improvements over traditional GCD-based problem formulation techniques. This robust framework can be adapted to a variety of applications in signal processing. As examples, we formulate four practical applications under this framework: (1) filtering for power line communications, (2) estimation in sensor networks with noisy channels, (3) reconstruction methods for compressed sensing, and (4) fuzzy clustering.
Towards an automatic bag-of-features model for the classification of dermoscopy images: The influence of segmentation

The classification of skin lesions in dermoscopy images depends on three critical steps: i) lesion segmentation, ii) feature extraction and iii) feature classification. Lesion segmentation plays an important role since segmentation errors may jeopardize the other two steps, leading to erroneous decisions. This paper studies the robustness of a skin lesion classifier based on a Bag-of-features approach in the presence of segmentation errors. We compare the performance achieved by the system using an automatic segmentation algorithm with the performance obtained using manual segmentation provided by a specialist. We observe a degradation of the system accuracy by 8% when automatic segmentation is used. We also show that these results can be improved if manually segmented images are used in training phase, keeping a fully automatic solution during the testing phase.
A feature selection approach implemented with the Binary Bat Algorithm applied for intrusion detection

The large number and various technological solutions adopted by many enterprises, overwhelms security systems, which must acquire informations from all these diverse sources and interpret them. Furthermore, the proliferation of more complex cyber threats imposes a difficult task for information security assurance. Therefore, it is clear that new solutions are required. In this paper we propose a wrapper feature selection approach that combines an improved version of the Binary Bat Algorithm with two classifiers (C4.5 and SVM). We test our proposed model on the NSL-KDD dataset and empirically prove that our method can boost the performance of the classifiers and outperforms BBA and BPSO in terms of attack detection rate and false alarm rate, obtained after a fewer number of iterations. Furthermore, we reduced the number of features with almost 64% and improved the performances of the classifier, even for unknown intrusions.
Board-level authentication through I/O fingerprinter

Providing resistance against hardware attacks is important to ensure computing security. A board-level authentication mechanism can be used to detect tampers to the components of the circuit board. The proposed board-level authentication approach is to utilize the I/O timing characters to obtain a specified fingerprint for each specified component on the board, and to check the validity of the component by matching the fingerprint for later usage. Such fingerprint is extracted from the sampling results between the sampling pulses and the I/O pins data. The proposed approach can be easily built into common chips, because it doesn't need complicated cryptographical logics and it also doesn't need high speed sampling clock circuit. A FPGA based evaluation system is constructed and real 8051-MCU chips are tested. The tested result shows that it can effectively identifies the valid chip from the faked ones. Hence, the proposed approach can be deployed into the circuit board to protect the components equipped on the board against hardware attacks.
City elements propelling city brand meaning-making processes

City branding literature discounts the nonmarketer-controlled aspects of city brand meaning co-creation as part of everyday, sociocultural meaning-making. Engaging a fundamental gap within this theoretical oversight, the present article explores how socially constructed and, thus, inherently uncontrollable elements of the city communicate symbolic messages about the city during the course of daily urban life. Specifically, the article contributes to marketing theory by conceptualizing how (1) urban reminders, (2) the arts, and (3) residential behavior emit symbolic messages in highly interrelated ways that see these elements propel the interconnected meaning-making processes enveloping city brands. Subsequent critical discussion further demonstrates how the more holistic view of city brand meaning-making processes that we develop opens up a fresh lens to grasp more of city brand meaning co-creation, advance intellectual debates around city branding, and, ultimately, expand the frontiers of marketing theory.
Dust in the Photospheric Environment. II. Effect on the Near-Infrared Spectra of L and T Dwarfs

We report an attempt to interpret the spectra of L and T dwarfs with the use of the unified cloudy model (UCM). For this purpose, we extend the grid of the UCMs to cases of log g = 4.5 and 5.5. The dust column density relative to the gas column density in the observable photosphere is larger at higher gravities, and molecular line intensity is generally smaller at higher gravities. The overall spectral energy distributions (SEDs) are fJ < fH < fK in middle and late L dwarfs, fJ < fH > fK in early T dwarfs (L/T transition objects), and finally fJ > fH > fK in middle and late T dwarfs, where fJ,fH, and fK are the peak fluxes at J, H, and K bands, respectively, in fν units. This tendency is the opposite of what is expected for the temperature effect, but it can be accounted for as the effect of thin dust clouds formed deep in the photosphere together with the effect of the gaseous opacities, including H2 (collision-induced absorption), H2O, CH4, and K I. Although the UCMs are semiempirical models based on a simple assumption that thin dust clouds form in the region of Tcr ≲ T ≲ Tcond (Tcr ≈ 1800 K is only an empirical parameter, while Tcond ≈ 2000 K is fixed by the thermodynamical data), the major observations, including the overall SEDs and the strengths of the major spectral features, are consistently accounted for throughout L and T dwarfs. In view of the formidable complexities of the cloud formation, we hope that our UCM can be of some use as a guide for future modeling of ultracool dwarfs and for interpretation of observed data of L and T dwarfs.
Power Allocation for SLNR Precoding based on MMSE Criterion in Multiuser MIMO System

Abstract In order to fulfill the rapid increasing demands of the broadband multimedia and wireless communication, the Multi-input multi-output (MIMO) technique serving as one of the key technologies of the physical layer, have drawn a lot of attention in the past few years. Multi-user multiple-input multiple-output (MU-MIMO) wireless communication system has rapidly gained in popularity over the past decade due to its promising improvement in terms of system capacity and transmission quality without incurring extra bandwidth or transmission power. Multi-user interference (MUI) is one of the major impairments on the performance of multi-user multiple input multiple-output (MU-MIMO) wireless communication system. Signal-to-leakage-and-noise ratio (SLNR) precoding suppresses MUI and provides a closed form solution to the coupling problem encountered in the conventional designs based on maximizing signal-to-interference-and-noise ratio (SINR). In this paper, to enhance the transmission quality for SLNR precoding in the MU-MIMO downlink communication, an iterative power allocation scheme is proposed. Based on minimum total mean square error criterion (MMSE), subject to a total transmit power constraint, power allocation factors, precoding and decoding matrix are regenerated and updated in turn iteratively. A local optimal solution could be solved in this alternating optimization. Simulation results demonstrate the improved bit error rate (BER) performance and the convergence of MSE of the proposed scheme.
Understanding and formulation of various kernel techniques for suport vector machines

Support Vector Machines (SVM's) are supervised learning algorithms which can be used for analyzing patterns and classifying data. This supervised algorithm is applicable for binary class as well as multiclass classification. The core idea is to build a hyperplane which can easily separate the training examples. For binary class, SVM constructs a hyper-plane which can easily separate d-dimensional training examples perfectly into 2-classes. but sometimes, the training examples are not linearly separable. Thus, for non-linear training examples, SVM introduced Kernel functions which transforms the data into high dimensional space where the data can be separated linearly. For minimizing the test error and for improving classification accuracy, kernels functions are used. This paper explains applications of kernels in support vector machine and provide information about the properties of these kernels and situations in which they can be used.
Aircraft conflict detection in an associative processor

A major problem in Air Traffic Control (ATC) is detecting when two aircraft are on a potential collision course soon enough to take some corrective action. Many algorithms are being developed which may lead to automating the process of conflict detection. However, these algorithms typically require large amounts of computing resource if they are to be performed in real-time. This paper describes some techniques which may be used by an associative processor to perform the conflict detection operation.
Identification and Prediction of Internet Traffic Using Artificial Neural Networks

This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing internet traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an internet traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing internet traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the internet traffic at different times.
Research on Key Technologies of Video Intelligent Identification and Security Management and Control System in Substation

With the development of substation automation technology, unmanned substation operation mode has become a trend, at present, many substations to achieve the "remote viewing" function, will be the scene of the substation, the equipment state to video image transmission to the remote control center (set control station). And scheduling center (set control station) on live video equipment to control and adjust, however traditional remote vision system can improve the automation level of substation, but due to technical, management and other aspects of the reasons, it is difficult to achieve substation real unattended, not completely reached synergism of depletion of numbers and intrinsically safe. Substation operation, management personnel not only need to see every corner of the substation in the remote monitoring center, but also the abnormal phenomenon of the environment in time, and remote control to dispose of. With the rapid development of science and technology and the revolutionary breakthrough of video processing technology, this paper introduces the key technology of "intelligent video image analysis technology", and the new generation of "large video management and control software platform", which is based on the new design concept. The system can provide remote vision system in the field of substation, the running state of the equipment, the image switching operation process state image, state instrument image, and on-site maintenance work on-site real-time image at the same time; in contact with the original substation communication equipment and electrical equipment case, digital remote video image acquisition equipment, and carries on the analysis the automatic processing and recognition, equipment and staff positions, knife gate opening and closing state change of power system, the instrument display read the contents of monitoring, and issued a warning signal according to the set requirements, provide a new method for automatic detection of fault diagnosis, unattended substation monitoring and control, through accurate fault, timely early warning information accident analysis, auxiliary diagnosis, to improve the reliability of grid, reducing safety accidents, is of great significance to reduce monitoring cost of operation and maintenance. This paper presents the results of project development for power dispatch, high voltage operation (interval protection), high pressure area equipment maintenance, provide on-site abnormal image automatic alarm display, and the scene of automatic alarm and alarm, so as to achieve the "safe operation of the substation" visual, from the technical means to further the substation safe production, in particular, to effectively prevent personal injury, provide a strong guarantee. Key words-Substation; intelligent; video recognition; safety control
Projective Splitting Methods for Pairs of Monotone Operators

By embedding the notion of splitting within a general separator projection algorithmic framework, we develop a new class of splitting algorithms for the sum of two general maximal monotone operators in Hilbert space. Our algorithms are essentially standard projection methods, using splitting decomposition to construct separators. These projective algorithms converge under more general conditions than prior splitting methods, allowing the proximal parameter to vary from iteration to iteration, and even from operator to operator, while retaining convergence for essentially arbitrary pairs of operators. The new projective splitting class also contains noteworthy preexisting methods either as conventional special cases or excluded boundary cases.
Mitigating emissions and energy consumption for urban transportation networks : simulation-based signal control strategies

Microscopic urban traffic simulators embed the most detailed traveler behavior and network supply models. They represent individual vehicles and can therefore account for vehicle-specific technologies. These simulators can be coupled with instantaneous energy consumption and emissions models to yield detailed network-wide estimates of energy consumption and pollutant emissions. Nonetheless, there is currently a lack of computationally efficient optimization techniques that enable the use of these complex integrated models to design sustainable transportation strategies. This thesis proposes a methodology that combines a stochastic microscopic traffic simulation model with an instantaneous vehicular fuel consumption model and consecutively, with an instantaneous vehicular emissions model. The combined models are embedded within a simulation-based optimization (SO) algorithm and used to address a signal control problem. First, a framework that combines travel time and fuel consumption in the objective is formulated followed by one combining travel time and various pollutant emissions. The proposed technique couples detailed, stochastic and computationally inefficient models, yet is an efficient optimization technique. Efficiency is achieved by combining simulated observations with analytical approximations of the objective functions. This methodology is applied to a network within the Swiss city of Lausanne. The proposed method identifies signal plans with improved travel time, fuel consumption and emissions metrics, and does so within a tight computational budget. It systematically outperforms traditional techniques, particularly when performance metrics with high variance, such as fuel consumption and emissions, are used. This method enables the use of disaggregate instantaneous vehicle-specific information to inform and improve traffic operations at the network-scale. Thesis Supervisor: Carolina Osorio Title: Assistant Professor of Civil and Environmental Engineering
Non-deterministic fault localiza-tion in communication systems using belief networks

This paper investigates non-deterministic fault diagnosis in communication systems. We introduce a nondeterministic system model for multi-layer fault diagnosis, which incorporates both availability and performance problems, and allows arbitrary relationships among system components to be represented. We propose a mapping of the layered model into a belief network and investigate an application of Bayesian reasoning techniques to performing fault localization using a belief network as a fault propagation model. Although it allows very accurate root cause determination, exact Bayesian reasoning is infeasible in real-life systems due to its exponential complexity. We introduce adaptations of two Bayesian reasoning techniques for polytrees, iterative belief updating and iterative most probable explanation calculation, as approximate fault localization schemes in belief networks of arbitrary shape. Using end-to-end service failure diagnosis as a case study, we show through simulation that the approximate schemes allow almost optimally accurate fault localization to be performed in polynomial time. The approximate techniques are able to identify multiple simultaneous faults, incorporate both positive and negative information into the reasoning process, and are resilient to noise in the observed symptoms. Both techniques are event-driven. They may be effectively used in systems with various types of dependencies among their components. We show that fault localization through iterative belief updating offers very high accuracy even if the fault propagation model contains inaccurate values of the probability distribution. We conclude that Bayesian reasoning is a promising tool in the area of fault localization.