Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase Transitions in Group Testing

The group testing problem consists of determining a sparse subset of a set of items that are "defective" based on a set of possibly noisy tests, and arises in areas such as medical testing, fault detection, communication protocols, pattern matching, and database systems. We study the fundamental limits of any group testing procedure regardless of its computational complexity. In the noiseless case with the number of defective items k scaling with the total number of items p as O(pθ) (θ ∈ (0, 1)), we show that the probability of reconstruction error tends to one when n ≤ k log2 p/k (1 + o(1)), but vanishes when n ≥ c(θ)k log2 p/k(1 + o(1)), for some explicit constant c(θ). For θ ≤ 1/3, we show that c(θ) = 1, thus providing an exact threshold on the required number measurements, i.e. a phase transition, which was previously known only in the limit as θ → 0. Analogous necessary and sufficient conditions are derived for the noisy setting, and also for a relaxed partial recovery criterion. This work was supported in part by the European Commission under Grant ERC Future Proof, SNF 200021-146750 and SNF CRSII2-147633. The first author acknowledges support from the 'EPFL Fellows' fellowship programme co-funded by Marie Sklodowska-Curie, Horizon2020 Grant agreement no. 665667.
Perceptual and Neural Olfactory Similarity in Honeybees

The question of whether or not neural activity patterns recorded in the olfactory centres of the brain correspond to olfactory perceptual measures remains unanswered. To address this question, we studied olfaction in honeybees Apis mellifera using the olfactory conditioning of the proboscis extension response. We conditioned bees to odours and tested generalisation responses to different odours. Sixteen odours were used, which varied both in their functional group (primary and secondary alcohols, aldehydes and ketones) and in their carbon-chain length (from six to nine carbons). The results obtained by presentation of a total of 16 × 16 odour pairs show that (i) all odorants presented could be learned, although acquisition was lower for short-chain ketones; (ii) generalisation varied depending both on the functional group and the carbon-chain length of odours trained; higher generalisation was found between long-chain than between short-chain molecules and between groups such as primary and secondary alcohols; (iii) for some odour pairs, cross-generalisation between odorants was asymmetric; (iv) a putative olfactory space could be defined for the honeybee with functional group and carbon-chain length as inner dimensions; (v) perceptual distances in such a space correlate well with physiological distances determined from optophysiological recordings of antennal lobe activity. We conclude that functional group and carbon-chain length are inner dimensions of the honeybee olfactory space and that neural activity in the antennal lobe reflects the perceptual quality of odours.
Vitamins C, E, and NADH on in Vitro Lymphocyte Proliferation and Redox Status among Obese Patients

Background: The prevalence of overweight and obesity associated with oxidative stress and immune abnormalities is continuously increasing. Antioxidant supplementations might counteract potential damage caused by ROS to cellular tissues. Objective: To determine the role of vitamins on immune improvement during obesity, we investigated in vitro effects of vitamins C, E, and NADH on mitogen-stimulated proliferation, Th1- and Th2-type cytokine production, and oxidant/antioxidant status of lymphocytes isolated from obese patients. Methods: Peripheral blood lymphocytes were isolated using a density gradient of Histopaque. They were in vitro cultured and stimulated by Con A in the presence or absence of vitamins. Cell proliferation was determined by MTT assay and interleukin-2, interleukin-4 and interferon-γ (INFγ) secretions. Cell oxidant/antioxidant balance was studied by assaying glutathione (GSH), malondialdehyde (MDA), carbonyl protein levels, catalase activity and micronucli frequency. Results: Obesity is associated with enhanced oxidative stress response. Indeed, vitamin C, E and NADH improved significantly lymphocyte proliferation and diminished cellular oxidative stress. Conclusion: Treatments of lymphocytes with vitamins had beneficial effects on lymphocyte proliferation, cytokines secretions and redox status, generating an anti-inflammatory profile and should be considered in therapeutic approaches for normalizing immune cell function in obesity.
Robust Matrix Decomposition with Outliers

Suppose a given observation matrix can be decomposed as the sum of a low-rank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the observed sum. Such additive decompositions have applications in a variety of numerical problems including system identification, latent variable graphical modeling, and principal components analysis. We study conditions under which recovering such a decomposition is possible via a combination of $\ell_1$ norm and trace norm minimization. We are specifically interested in the question of how many outliers are allowed so that convex programming can still achieve accurate recovery, and we obtain stronger recovery guarantees than previous studies. Moreover, we do not assume that the spatial pattern of outliers is random, which stands in contrast to related analyses under such assumptions via matrix completion.
Weaving Models with the Eclipse AMW plugin

The basic assumption in model engineering (MDE) is to consider models as first class entities. One of the most important kinds of models in MDE approaches are transformation models. Transformation models define operations between different models. However, there are many operations that are not efficiently handled by generic model transformations. For example, models transformations are not adapted to define and to capture relationships between models elements. Relationships between model elements are present in many different application scenarios, such as specification of transformations, traceability, or model alignment. We propose the use of weaving models to capture relationships between model elements. Weaving models can be used in different application scenarios, because they conform to different extensions of a core weaving metamodel. In this paper, we explain in detail what a model weaving is. We present a set of application scenarios, and we extract a set of basic requirements for creating relationships between model elements. Based on that, we define a core weaving metamodel, metamodel extensions, and describe a set of methods to create weaving models. We implement an Eclipse plugin named AMW (ATLAS Model Weaver) to support the creation of these weaving metamodels and models.
Face distance estimation from a monocular camera

A new era of mobile devices (phones and tablets) has emerged with some of them possessing a front camera facing the person or user and stereo cameras in the rear side. This paper emphasise on a novel method to use such devices with monocular camera to determine the depth between the user and the front camera using a back propagation neural network (BPNN). This depth is successively used to calculate the zooming factor for a legible view, to read a document on the display of the mobile device. In the absence of camera parameters and also considering the fact that the camera and user's face are in constant motion; measuring the distance of the face from the camera becomes a hard problem. We propose the use of frontal facial features acquired from the monocular camera to find the depth information with the use of supervised learning algorithm. Training the BPNN for the facial features for a standard face is one time factory programmed during camera manufacturing. One time new user's face registration for the gadget using rear cameras followed by the simulation of depth from the front camera using the trained BPNN is proposed in this work. The results of distance estimation are likened with stereo camera setup and the approach is validated.
Simulation-based average case analysis for parallel job scheduling

This paper analyses the resource allocation problem in parallel job scheduling, with emphasis given to gang service algorithms. Gang service has been widely used as a practical solution to the dynamic parallel job scheduling problem. To provide a sound analysis of gang service performance, a novel methodology based on the traditional concept of competitive ratio is introduced. Dubbed dynamic competitive ratio, the new method is used to do an average case analysis based on simulation of resource allocation algorithms. These resource allocation algorithms apply to the gang service scheduling of a workload generated by a statistical model. Moreover, dynamic competitive ratio is the figure of merit used to evaluate and compare packing strategies for job scheduling under multiple constraints. It is shown that for the unidimensional case there is a small difference between the performance of best fit and first fit; first fit can hence be used without significant system degradation. For the multidimensional case, when memory is also considered, we conclude that the resource allocation algorithm must try to balance the resource utilization in all dimensions simultaneously, instead of given priority to only one dimension of the problem.
Software Defined Networks: A case for QoS implementation at the Greek School Network

The Software Defined Networking is a different approach to the way network equipment operation. Until recently, network devices embody both the physical layer (signaling) and the logic on how to exchange packets. This was intended to maximize the upload speed, and a predetermined path are not imposed delays. Similar solutions have been given to other resources in the field of Information Technology. Processing power, magnetic storage media, as well as random access memory, a factor which may be available depending on the needs of users. This was the main idea of the area of mainframes up to Virtualization and Cloud Computing. SDN is an emerging architecture that is dynamic, manageable, cost-effective, and adaptable, making it ideal for the high-bandwidth, dynamic nature of today's applications. This architecture decouples the network control and forwarding functions enabling the network control to become directly programmable and the underlying infrastructure to be abstracted for applications and network services. The OpenFlow protocol is a foundational element for building SDN solutions. In this paper an theoretical study of SDN technology will be given, as also as an implementation of a QoS mechanism based on SDN technology will be presented for the Greek School Network.
A Case Study in Formal System Engineering with SysML

In the development of complex critical systems, an important source of errors is the misinterpretation of system requirements allocated to the software, due to inadequate communication between system engineering teams and software teams. In response, organizations that develop such systems are searching for solutions allowing formal system engineering and system to software bridging, based on standard languages like SysML. As part of this effort, we have defined a formal profile for SysML (OMEGA SysML) and we have built a simulation and verification toolbox for this profile (IFx). This paper reports on the experience of modelling and validating an industry-grade system, the Solar Generation System (SGS) of the Automated Transfer Vehicle (ATV) built by Astrium, using IFx-OMEGA. The experience reveals what can currently be expected from such an approach and what are the weak points that should be addressed by future research and development.
SHELLOS: Enabling Fast Detection and Forensic Analysis of Code Injection Attacks

The availability of off-the-shelf exploitation toolkits for compromising hosts, coupled with the rapid rate of exploit discovery and disclosure, has made exploit or vulnerability-based detection far less effective than it once was. For instance, the increasing use of metamorphic and polymorphic techniques to deploy code injection attacks continues to confound signature-based detection techniques. The key to detecting these attacks lies in the ability to discover the presence of the injected code (or, shellcode). One promising technique for doing so is to examine data (be that from network streams or buffers of a process) and efficiently execute its content to find what lurks within. Unfortunately, current approaches for achieving this goal are not robust to evasion or scalable, primarily because of their reliance on software-based CPU emulators. In this paper, we argue that the use of software-based emulation techniques are not necessary, and instead propose a new framework that leverages hardware virtualization to better enable the detection of code injection attacks. We also report on our experience using this framework to analyze a corpus of malicious Portable Document Format (PDF) files and network-based attacks.
Bayesian Inference of Sampled Ancestor Trees for Epidemiology and Fossil Calibration

Phylogenetic analyses which include fossils or molecular sequences that are sampled through time require models that allow one sample to be a direct ancestor of another sample. As previously available phylogenetic inference tools assume that all samples are tips, they do not allow for this possibility. We have developed and implemented a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to infer what we call sampled ancestor trees, that is, trees in which sampled individuals can be direct ancestors of other sampled individuals. We use a family of birth-death models where individuals may remain in the tree process after sampling, in particular we extend the birth-death skyline model [Stadler et al., 2013] to sampled ancestor trees. This method allows the detection of sampled ancestors as well as estimation of the probability that an individual will be removed from the process when it is sampled. We show that even if sampled ancestors are not of specific interest in an analysis, failing to account for them leads to significant bias in parameter estimates. We also show that sampled ancestor birth-death models where every sample comes from a different time point are non-identifiable and thus require one parameter to be known in order to infer other parameters. We apply our phylogenetic inference accounting for sampled ancestors to epidemiological data, where the possibility of sampled ancestors enables us to identify individuals that infected other individuals after being sampled and to infer fundamental epidemiological parameters. We also apply the method to infer divergence times and diversification rates when fossils are included along with extant species samples, so that fossilisation events are modelled as a part of the tree branching process. Such modelling has many advantages as argued in the literature. The sampler is available as an open-source BEAST2 package (https://github.com/CompEvol/sampled-ancestors).
Rational Opacity In Private Equity Markets ∗

Private equity investments are highly illiquid compared to publicly traded alternatives. We argue that, given this distinguishing characteristic, private equity markets should optimally feature restricted information ows between operators and investors precisely as they do in practice. When secondary market opportunities are likely to be shallow more information can reduce the expected liquidation value of projects. Even given direct control over information design, it is optimal for investors to incentivize fund managers to withhold interim information from them. To make these points, we describe a multi-period investment model where the optimal information design can be fully characterized.
Power systems stability control: reinforcement learning framework

In this paper, we explore how a computational approach to learning from interactions, called reinforcement learning (RL), can be applied to control power systems. We describe some challenges in power system control and discuss how some of those challenges could be met by using these RL methods. The difficulties associated with their application to control power systems are described and discussed as well as strategies that can be adopted to overcome them. Two reinforcement learning modes are considered: the online mode in which the interaction occurs with the real power system and the offline mode in which the interaction occurs with a simulation model of the real power system. We present two case studies made on a four-machine power system model. The first one concerns the design by means of RL algorithms used in offline mode of a dynamic brake controller. The second concerns RL methods used in online mode when applied to control a thyristor controlled series capacitor (TCSC) aimed to damp power system oscillations.
Information Combining for OFDM-based Relay Networks

In this paper, we present a method for calculating lower and upper bounds on the mutual information of a broadband relay network. The analysis is carried out for a simple network consisting of a source, one relay and the destination. However, it can be easily extended to larger networks. The relay operates in a half-duplex mode and performs decode-and-forward. In order to overcome the effect of multipath fading, orthogonal frequency division multiplexing (OFDM) is used on all physical links. The capacity of this network ca be analyzed by the max-flow min-cut theorem delivering an upper bound for the general case. For specific networks like the degraded relay channel, the achievable rate can be exactly determined. In order to investigate the tightness of these theoretical results, a capacity approaching repeat accumulate code is considered. By using the information combining technique that has been developed for the analysis of iterative decoding strategies, tight bounds on the achievable mutual information can be numerically computed. It is demonstrated that the upper bound of the max-flow min-cut theorem is nearly reached by this code and that the result for the degraded relay channel can serve only as a lose lower bound. In particular, the relay is also useful even in a scenario where it is not able to fully decode the received message.
The role of the self in responses to health communications: A cultural perspective

To the extent that cultures vary in how they shape individuals' self-construal, it is important to consider a cultural perspective to understand the role of the self in health persuasion. We review recent research that has adopted a cultural perspective on how to frame health communications to be congruent with important, culturally variant, aspects of the self. Matching features of a health message to approach vs. avoidance orientation and independent vs. interdependent self-construal can lead to greater message acceptance and health behavior change. Discussion centers on the theoretical and applied value of the self as an organizing framework for constructing persuasive health communications.
Packet classification with filter categorization

In next generation networks, routers use the technique of packet classification to classify incoming packets into multiple forwarding classes. The classification is based on predefined filters which indicate the requirements of quality of service or security handling. To achieve scalable packet classification, numerous algorithms based on hash tables have been proposed. These algorithms focus on reducing the number of accessed hash tables to improve the search performance; however, their performance improvement is mainly affected by filter characteristics. We analyze the search procedures of hash-based algorithms and propose a scheme of adaptive filter categorization to improve the search performance. Our scheme stores the filters in different data structures according to their characteristics. Therefore, the search performance could be improved by eliminating the worst cases in previous algorithms. Our scheme is evaluated with filter databases of varying sizes and characteristics, and the experimental results demonstrate that our scheme is feasible and scalable.
Predicting arterial stiffness with the aid of ensemble empirical mode decomposition(EEMD) algorithm

In this study, we propose an easy-to-use non-invasive arterial stiffness assessment instrument that can be used to record the radial arterial pressure signals from the wrist. The system combines the ensemble empirical mode decomposition (EEMD) algorithm with the signals to derive a modified reflection index (MRI). The performance of MRI was verified based on 42 subjects (33 men and 9 women, 20 to 27 years of age). Early self-monitoring of cardiovascular dysfunction and arterial stiffness can be easily and effectively achieved by MRI because only few minutes are needed for conducting at home.
Prototype-Based Approximate Nearest Neighbor Search

The principles of the prototype model for speaker recognition have been applied in a new version of the Approximate Nearest Neighbor schema. An analytical expression for the space complexity, using the prototype model, has been developed. It has also been empirically proven that increasing the error bounds when using the Approximate Nearest Neighbor search speeds up the prototype-based search, with minimal reduction of search accuracy. Key-Words: Approximate Nearest Neighbors; Prototype-Based Search; Space and Time Complexities; Speaker Recognition.
Job Satisfaction: The Influence Of Person-Job Fit And Leader-Member Exchange

The effects of person-job fit and leader-member exchange on job satisfaction have been repeatedly found in literature, yet no study has compared these effects between public and private corporations. The purpose of this research is to study the influence of person-job fit and leader-member exchange to job satisfaction of employees in Vietnam and examine if these effects are moderated by sector type. 547 employees in public and private sector in Vietnam reported to 3 measures: person-job fit scale (Chuang, Shen & Judge, 2015), leader-member exchange scale (Lyden & Maslyn, 1998), and job satisfaction scale (Seashore et al, 1982). Results showed that there were positive correlations between person-job fit and job satisfaction, and between leader-member exchange and job satisfaction. There were also significant moderation effects of sector type. Person-job fit has a weaker effect on job satisfaction in the public sector than in the private sector; while leader-member exchange has a stronger effect on job satisfaction in the public sector than in the private sector. Implications of the findings were discussed; in particular, leaders in the public sector need to focus on creating a smooth supervisor-subordinate relationship; while those in the private sector should select people that fit the job. © 2018 Published by Future Academy www.FutureAcademy. org.UK