Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding protein-protein interaction networks (עם תקציר בעברית ושער נוסף: הבנת רשתות אינטראקציה בין חלבונים.)

All living organisms consist of living cells and share basic cellular mechanisms. Amazingly, all those cells, whether from a bacterium or a human being, although different in their structure and complexity, comprise the same building blocks of macro molecules: DNA, RNA, and proteins. Proteins play major roles in all cellular processes: they create signaling cascades, regulate almost every process in the cell, act as selective porters on the cell membrane, accelerate chemical reactions, and many more. In most of these tasks, proteins work in concert, by creating complexes of varying sizes, modifying one another and transporting other proteins. These interactions vary in many aspects: they might take place under specific conditions, have different biophysical properties, different functional roles, etc. Identifying and characterizing the full repertoire of interacting protein pairs are of crucial importance for understanding the functionality of a living cell. In the last decade, development of new technologies allowed large-scale measurements of interaction networks. In turn, many studies used the results of such assays to gain functional insights into specific proteins and specific pathways as well as learn about the more global characteristics of the interaction network. Unfortunately, the experimental noise in the large-scale assays makes such analyses hard, challenging the development of advanced computational approaches towards this goal. In the first part of my PhD work I used the language of relational graphical models to suggest a novel statistical framework for representing interaction networks. This framework enables taking into account uncertainty about the observed large-scale measurements, while investigating the interaction network properties. Specifically, it allows simultaneous prediction of all interactions given the results of large-scale experimental assays. I applied this model to noisy observations of protein-protein interactions and showed how such simultaneous predictions enable intricate information flow between the interactions, allowing for better prediction of missing information. However, application of this model to the entire interaction network would require creation of a huge model over millions
Dual-Tree Tight Frame Wavelets with Symmetric Envelope

Wavelets based on Hilbert pairs have appealing properties when applied to image denoising and feature detection due to their directional sensitivity. In this paper we propose dual-tree 4-channel tight frame wavelets and scaling functions {phih, psih1, psih2, psih3} and {phig, psig1, psig2, psig3} based on FIR filterbanks of four filters, and downsampling by 2. Such wavelets closely approximate shift invariance, and the resulting complex wavelets lead to exactly symmetric envelope. The filters enjoy vanishing moments property.
On monotone linear operators and the spectral radius of their representing matrices

In this paper, linear operators on the space of $p \times p$ matrices are considered. Such linear operators can be represented by $p^2 \times p^2 $ matrices. In particular, sums of Kronecker products occur as representing matrices. Let the linear operators $\mathcal{L}_S $ and $\mathcal{L}_U $ be represented by the matrices S and U, where U is of the form $U = \sum {A_k } \otimes \bar A_k $. It is shown that, in order that $\mathcal{L}_U ( X )\leqq \mathcal{L}_S ( X )$ for all positive-semidefinite X, it is necessary that the spectral radii of U and S satisfy the inequality $\rho ( U )\leqq \rho ( S )$.
Modelling and validation of social influences in human security behaviour

A major challenge for organisations is effectively implementing security policies where employees have a choice to comply. For instance, an organisational requirement such as no unauthorised personnel in restricted areas may have a policy stating authorised persons must wear identification badges and, therefore, places the onus on employees to make a choice and have the responsibility of wearing their badge. Existing literature in psychology and human factors in security tells us that a person’s compliance behaviour for a security policy depends on their compliance attitude. For the organisation, there exists uncertainty for quantifying compliance attitudes of employees towards security policies where those employees have a choice to comply. Quantifying the compliance attitudes would allow an organisation to further establish its current risk environment. In the case of not wearing identification badges due to poor compliance attitudes, it would be challenging to identify unauthorised personnel and the organisational requirement would not be met. A person’s compliance behaviour depends on their compliance attitude which itself depends on the compliance behaviour of themselves and of others. For example, the compliance behaviour from top-level management can influence others to be non compliant. This thesis poses the question how could one quantify compliance attitudes for security policies in an organisation where people can observe other people’s compliance behaviour. This thesis contributes the following: 1) modelling of social influences in Coloured Petri Nets 2) a rule based model to represent agents that observe the actions of other agents 3) the application of machine learning to identify hidden compliance attitudes 4) a user study with behavioural interventions towards security policy compliance 5) a simulation tool for assessing how compliance attitudes evolve amongst agents 6) validation of the simulation tool by comparison to the empirical data from the user study. Overall, we believe that this thesis provides a holistic approach towards social influences over compliance attitudes and the simulation tool paves the way towards accurately assessing compliance attitudes for security policies.
Synthesis of Privacy-Preserving Systems

Synthesis is the automated construction of a system from its specification. In many cases, we want to maintain the privacy of the system and the environment, thus limit the information that they share with each other or with an observer of the interaction. We introduce a framework for synthesis that addresses privacy in a simple yet powerful way. Our method is based on specification formalisms that include an unknown truth value. When the system and the environment interact, they may keep the truth values of some input and output signals private, which may cause the satisfaction value of specifications to become unknown. The input to the synthesis problem contains, in addition to the specification φ , also secrets ψ 1 , . , ψ k . During the interaction, the system directs the environment which input signals should stay private. The system then realizes the specification if in all interactions, the satisfaction value of the specification φ is true, whereas the satisfaction value of the secrets ψ 1 , . , ψ k is unknown. Thus, the specification is satisfied without the secrets being revealed. We describe our framework for specifications and secrets in LTL, and extend the framework also to the multi-valued specification formalism LTL[ F ], which enables the specification of the quality of computations. When both the specification and secrets are in LTL[ F ], one can trade-off the satisfaction value of the specification with the extent to which the satisfaction values of the secrets are revealed. We show that the complexity of the problem in all settings is 2EXPTIME-complete, thus it is not harder than synthesis with no privacy requirements.
The Effect of Along-front Temperature Variation in a Two-Dimensional Primitive Equation Model of Surface Frontogenesis

Abstract The development of frontal zones is examined in a two-dimensional primitive equation model of frontogenesis formulated for dry, nearly adiabatic and inviscid conditions. The model results are interpreted in the context of the general problem of determining the dynamical properties of cold and warm fronts. The central hypothesis (attributable to Eliassen) is that cold and warm fronts may be distinguished by the orientation of the cross-front thermal wind component and the sense of the associated along-front temperature variation. Three simulations comprising confluent forcing (geostrophic contraction in the cross-front direction) and differing initial specifications of the along-front potential temperature gradient are examined in detail. In the first simulation, referred to as the pure confluence case, the along-front potential temperature gradient is set to zero, establishing a control for specifying along-front potential temperature variations respectively characteristic of cold and warm fronts.
A New and Comprehensive Determination of the Distance to Member Stars of the Hyades

This paper critiques the methods used in the past for estimating the distances to the stars that make up the Hyades moving cluster, and develops one which is new and comprehensive. We develop a formalism for the application of stochastic restrictions to the adjustment parameters (velocity components and distances) and test it on constructed cluster models. We perform model calculations with fictitious model star clusters in which the 'true' values of the input parameters (i.e. positions, distances and velocity components) are therefore known. The comparison of the distances recovered by different methods with the true input distances shows our approach to be very superior to the traditional ones that estimate distances by the method of stream parallaxes without restricting the adjustment parameters. Applying our algorithm to the Hyades, we assume a dispersion of 0.25 Ian s -1 in each coordinate about a common value for the velocity components of the cluster members and a dispersion of 5 pc about the distance to the cluster's centre for the distances to the stars in the cluster. Under these stochastic restrictions, we analyse the known estimates (i.e. measurements) of trigonometric parallaxes, sets of high-precision proper motion pairs and high-precision radial velocities subject to the above-stated stochastic constraints, and as a result estimate the cluster's centre to be at a distance of 45.8 ± 1.25 (standard error) pc.
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IL-17A was higher in axial SpA patients compared with controls. We previously evaluated a series of patients with radiographic axial SpA and found that patients with axial SpA had a higher proportion of IL-17A/interferon-γ–producing MAIT cells and IL-22– producing MAIT cells (2). As reported in an SKG mouse model that develops an SpA-like disease, the synergistic effects of IL-22 and IL-17A are required to promote enthesitis (3). In a mouse model of arthritis, overexpression of IL-23 induced the production of IL-22 by entheseal IL-23R+CD4−CD8−CD3+ T cells, with the involvement of IL-22 in the induction of genes implicated in bone proliferation (4). Although Rosine et al highlighted the presence of MAIT cells in entheseal structures of healthy subjects and reported that these cells expressed the vascular endothelial growth factor gene involved in angiogenesis (VEGFA) at high levels, we currently do not know the specific expression of MAIT cells within the sites of inflammation in axial SpA patients. Moreover, IL-22 production by resident entheseal MAIT cells was not examined by Rosine et al. Finally, MAIT cells are primarily expressed at mucosal surfaces and in the blood, but they also express chemokine receptors and can migrate to specific tissues, such as the gut. With the consideration of the involvement of the gut and joint in axial SpA, attention should also be given to the specific expression of chemokine receptors (e.g., CCR9) associated with α4β1 and α4β7 integrins by MAIT cells. Author disclosures are available at https://onlinelibrary.wiley. com/ action/downloadSupplement? doi=10.1002%2Fart.42278&file=art42278sup-0001-Disclosureform.pdf.
INTERLEUKIN-22 REGULATES ANTI-TUMOR IMMUNITY IN MOUSE MODELS OF LUNG AND BREAST

the homozygous cohort. Our findings demonstrate a reduced amount of potentially immunogenic peptides in EGA patients with HLA-homozygosity for at least one locus, which may result in impaired cancer immunosurveillance. In line with this observation, we also found increased levels of CTA expression in homozygous compared to heterozygous patients. After artificial modification of the genotype of homozygous patients to a heterozygous genotype, the set of predicted good-binding peptides was comparable to the heterozygous cohort. Conclusion Our results highlight the effect of HLA-I homozygosity on the immunopeptidome as important prerequisite of anti-tumor immunity. The high frequency of genomic HLA-I homozygosity observed in the EGA cohort may reflect an increased cancer risk for these patients. Together with previous reports demonstrating reduced survival after checkpoint therapy, our study suggests consideration of germ-line HLA-homozygosity for the design and interpretation of immunotherapeutic trials.
A localized-basis scheme for molecular dynamics

The combination of molecular dynamics and density-functional theory introduced by Car and Parrinello (CP) has broadened very markedly the range of systems and properties that can be treated on a first-principles basis. The CP formulation involves plane-wave expansions for wavefunctions and potentials and is best suited when the atoms can be represented by weak pseudopotentials. An alternative approach employs expansions in localized orbitals and may prove advantageous where strong potentials are encountered. The approach derives forces from a density functional closely related to the Kohn-Sham functional but defined rigorously on function space, in particular for a sum of site densities. This simplifies force calculation substantially. The site densities are represented via a density basis and the orbitals by an orbital basis of exponentially localized Lambda -functions whose exponents are optimized dynamically. The potential of the approach is demonstrated via explicit calculations, including molecular-dynamics simulations for hydrogen clusters.
State of the Environment and Its Impacts on the Urban Agriculture of Edible Plants in the City of Lubumbashi

This research looked at the state of the environment in the areas where amaranth urban agriculture is practised in the municipality of Katuba, City of Lubumbashi, the Democratic Republic of Congo. Samples of water used for watering, the soil where urban agriculture is practised, and amaranths have been subjected to characterization analyses to look for chemical and microbiological pollutants. These analyses revealed that water and amaranths are polluted from the mineral and microbial standpoint, unlike soil samples. Indeed, water samples contained Paramecia, Entamoeba histolytica's eggs and colonies of faecal contamination germs, mesophilic flora, and other pathogen germs such as Aeromonas hydrophila and Pseudomonas aeruginosa. As for amaranth samples, their concentrations of cobalt were greater than the value set by the quality standards. The Paramecia, Entamoeba histolytica’s eggs, Escherichia coli, and Klesiella planticola contaminate them. For the above, consuming these amaranths poses health risks to the population. Public authorities must take the necessary measures to organize the practice of urban agriculture of edible plants to bring it up to standard and protect the consumers’ health.
Study of Two Design Variations of an Antipodal Vivaldi Antenna Working at 28 GHz Millimeter Wave Frequency for 5G Applications

Antipodal Vivaldi Antenna (AVA) that overcomes the limitation of low impedance bandwidth in conventional Vivaldi antenna is used in a range of applications in different frequency bands including ultra-wideband, X-band, millimeter wave (mmW) bands etc. It also provides stable radiation patterns as well as high gain. In the present work, two variations of a planar AVA topology have been simulated for 5G application at 28 GHz mmW frequency. The purpose of implementing two design variations is to showcase that the minor changes in the antenna design result in major improvements in its radiation parameters. The designed antenna has a radiation efficiency of 90%, gain of 10.7 dBi, side lobe level of -12.1 dB, cross polarization level below -20.17 dB, half power beam width of 55.7° and 32.3° in E and H planes, respectively.
The Implementation of Artificial Neural Network (ANN) in the Prediction of Tides Level Data in Indonesia

Indonesia is currently focusing on its big goal to become The World’s Maritime Axis. For this reason, several sectors such as the infrastructure of the port, the development of the fishing, and tourism industry should be improved. The use of accurate tides level data is indispensable to support these developments. However, the number of instruments to observe tides data is limited compared to the covered area since Indonesia has the third longest coastline in the world. Recently, the frequent use of Artificial Intelligence (AI) has also offered an alternative solution to provide prediction data, including tides level data. Thereby, Artificial Neural Networks (ANN) as the subfield of AI is then chosen to make a prediction of tides level data. The type of ANN used in this study is two-layer Feed Forward Neural Network (FFNN). The previous observed tides data using atmospheric data (temperature and pressure) and moon position as the features are used to train the network. In order to evaluate the performance of ANN model, the result of the prediction is then compared to the observed tides level data using Automatic Weather Station (AWS). The result shows that the predicted tide level data has a strong correlation with the observed data with coefficient correlation of 0.9238. Furthermore, Root Mean Square Error (RMSE) as the statistics parameters to evaluate the performance of ANN model is found to be low around 0.077 meters. This preliminary result suggests that the FFNN has a good performance in predicting tides level data and therefore can be applied to provide tides level data on a larger scale in Indonesia.
Machine Learningcloud-Based Approach to Identify and Classify Disease

The term "Internet of Things" (IoT) describes the process of creating and modeling web-related physical objects across computing systems. IoT-based healthcare applications have offered multiple real-time products and benefits in recent years. For millions of people, these programmers provide hospitalization can get regular medical records and healthy lives. The introduction of IoT devices in the health sector has several technological developments. This study uses the IoT to construct a disease diagnostic system. Wearable sensors in this system initially monitor the patient's sympathy impulses. The impulses are then sent by a network environment to a server. In addition, a new hybrid approach to evaluation decision-making was presented as part of this research. This technique starts with the development of a set of features of the patient's pulses. Based on a learning approach qualifications are neglected. A fuzzy neural model was used as a diagnostic tool. A specific diagnosis of a particular ailment, such as the diagnosis of a patient's normal and abnormal pulse or the assessment of insulin issues, would be modeled to assess this technology.
Automated Evaluation of Child Emotion Expression and Recognition Abilities

Advancement of human computer interactions have led to the development of automated systems for the study of facial emotions. Currently, human intervention is required to identify the emotional state of an individual, thus our study proposes an automated process using a novel mobile application named Chezer which aims to identify the emotion expression and recognition abilities of children. The main objective of this study is to provide an affordable application to low income families by eliminating the need to visit clinics for child emotion related concerns. Chezer uses a multi-sensory gaming approach in developing games to identify the emotion expression and recognition ability of a child, where a level and a scenario based gaming methods are implemented respectively. The games include various methods such as visual and audio stimulation and quiz based assessments to aid a parent in analyzing the ability of their children. To evaluate the games, an emotion prediction model based on Convolution Neural Network which yields an accuracy of 90% is incorporated along with other models to detect valence, arousal and emotion levels which makes the study unique in the context of children. Valence and arousal detection models yield Root Mean Square Error scores of 0.23 and 0.05 respectively. LIRIS and EmoReact children video datasets were used for the model training and testing purposes. Further, the application was tested among the children at Sri Lanka which yielded promising results. Overall, combination of all these features in a single application makes the study novel.
Effect of heat sink layer on ultrafast magnetization recovery of FeCo films

For FeCo alloy thin films with Ag, Cu, Pt, Ta and Cr as heat sink layers, ultrafast demagnetization and recovery processes of transient magnetization have been studied by the time-resolved magneto-optical Kerr effect. For all samples, the ultrafast demagnetization process is accomplished within almost the same time interval of 500 fs, which is independent of the heat sink layer material and the pump fluence. The recovery rate of the FeCo film grown on the Si(1 0 0) substrate is enhanced with a heat sink layer. In addition, the recovery rate is found to be independent of the heat sink layer thickness; it decreases with increasing pump fluence. Among all heat sink layers, the sample with the Cr layer achieves the highest recovery rate because it has the same bcc structure as that of the FeCo layer and the small lattice mismatch. The sample with the Ta layer, has the largest damage threshold of pump fluence because of the highest melting point.
An application of conversational systems to promote healthy lifestyle habits

Recent reports indicate the multiple benefits of adopting conversational systems for healthcare, providing automation of management tasks in overburdened healthcare systems, improved patient experiences, and better efficiency and productivity rates. In this paper, we describe a conversational system aimed at promoting healthy lifestyle habits related to nutrition and physical exercise. The system has been developed using Google’s Dialogflow platform and the combination of different APIs and data repositories in the cloud. It has been integrated in the Facebook Messenger instant messaging platform. The results of the preliminary assessment of the system through a subjective questionnaire show the high degree of satisfaction of the users with the functionalities provided and the help offered to allow fulfilling the predefined objectives.