Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Reference Adaptive and Gain Scheduling Control for variable payload UAV Quadcopters

This work addresses the flight control of a UAV subject to an important and sudden modification of its dynamics during the flight. Namely a UAV Quadcopter with variable payload during a mass drop mission is considered; at least 30% of the nominal weight of the drone is dropped. The control objective is to guarantee the same level of performance whatever the configuration of the UAV: loaded or unloaded. Two adaptive strategies are considered: Direct Model Reference Adaptive Control and Gain Scheduling Control. A feasibility study between both strategies is carried out, alongside a detailed comparison on the relative efficacy and ease of implementation of each. Finally, both controllers are integrated on ISAE-SUPAERO simulation facilities and tested in real flight. The superiority of Model Reference Adaptive Control is proven, not only in terms of dynamic behaviour, but also for its simplicity and robustness. The properties of autotuning and adaptation towards an unknown and disturbed flight make it a valuable solution for the flight control of UAVs.
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas

It is valuable yet remains challenging to apply neural networks in logical reasoning tasks. Despite some successes witnessed in learning SAT (Boolean Satisfiability) solvers for propositional logic via Graph Neural Networks (GNN), there haven’t been any successes in learning solvers for more complex predicate logic. In this paper, we target the QBF (Quantified Boolean Formula) satisfiability problem, the complexity of which is in-between propositional logic and predicate logic, and investigate the feasibility of learning GNN-based solvers and GNN-based heuristics for the cases with a universal-existential quantifier alternation (so-called 2QBF problems). We conjecture, with empirical support, that GNNs have certain limitations in learning 2QBF solvers, primarily due to the inability to reason about a set of assignments. Then we show the potential of GNN-based heuristics in CEGAR-based solvers, and explore the interesting challenges to generalize them to larger problem instances. In summary, this paper provides a comprehensive surveying view of applying GNN-based embeddings to 2QBF problems, and aims to offer insights in applying machine learning tools to more complicated symbolic reasoning problems.
The effect of DEBIO 1143 usage alone or in combination with Tamoxifen on estrogen receptor positive breast cancer cell lines.

Amac: Tamoxifen (TAM) ve DEBIO 1143 (AT-406)’un artan konsantrasyonlarinin tek basina veya birlikte uygulanmasinin, MCF-7 ve BT-474 ostrojen reseptoru pozitif (ER+) meme kanseri hucre hatlarindaki etkisini arastirmak. Gerec ve Yontem: XTT testi, Tamoxifen ve DEBIO 1143’un tek basina veya birlikte kullanimin 72 saatlik MCF-7 ve BT-474 hucre hatlarindaki hucre canliligi uzerine olan etkisini belirlemek icin kullanildi. TAM ve DEBIO 1143'un tek basina / kombinasyonunun her iki hucre hatti uzerindeki etkisini gostermek icin cok parametreli apoptoz analiz kiti kullanildi ve floresan mikroskobik goruntuleme yapildi. Bulgular: TAM'in IC 50 degeri MCF-7 ve BT-474 hucre hatlarinda sirasiyla 3,8±0,6 mikromolar (µM) ve 18,9±6,7 µM idi. DEBIO 1143'un IC 50 degeri, MCF-7 hucre hattinda 15±0,5 uM idi. Ilac kombinasyonuna bagli sonuclar her iki hucre hatti icin istatistiksel olarak anlamliydi ( p< 0,001). Hucre canliliginin azalmasi apoptozis ile iliskili bulunmadi. Sonuc: ER+ meme kanseri hucre hatlarinda, kombinasyon halinde uygulanan TAM ve DEBIO 1143 dozlarinin, tek basina uygulanmalarina gore hucre canliligini daha fazla azalttigi belirlenmistir. Her iki hucre hattinda kombinasyon dozlarinin uygulanmasi sinerjik bir etki gostermistir. Kombine uygulamanin sebep oldugu hucre canliligindaki azalmanin, apoptoz disindaki hangi hucre olum tipi ile oldugunu belirlemek icin ileri arastirmalara ihtiyac vardir.
Supplementary Material of “Robust Multi-view Subspace Learning with Non-identically and Non-independently Distributed Complex Noises”

In this supplementary material, we provide more details on the computations involved in the proposed variational inference algorithm and more experiment results. I. NIID-MSL MODEL A. Model Formulation Basically, we decompose the observed data into: X = S +E, (1) where E = {eij}d×n denotes the residual term (i.e., noise component) and S ∈ Rd×n is the expected data located on the latent subspace, d and n represent the dimensionality and the number of samples in each view, respectively. Firstly, we model the noise term E as follows: ξk ∼ Gam(e0, f0), eij ∼ N (0, (ξczv ij ) −1 ), (2a) ct ∼ Multi(β), z ij ∼ Multi(π), (2b)
PII: S0197-4580(02)00013-1

We studied the hippocampal angle and spatial relationships of medial temporal lobe (MTL) structures, using midbrain colliculi and inter-collicular sulcus (ICS) as landmarks, and measured MTL width on axial 3D-T1-weighted MRI at ICS level in 41 normal, aged participants. Mean hippocampal angle was 29 ◦ (range 17–42 ◦) caudal to the anterior–posterior commissure (AC–PC) line. The slice at the ICS, parallel to the long axis of the hippocampus, best revealed a longitudinal view of hippocampus and parahippocampal gyrus in 76% of participants, compared to only 7% when slices were 20 ◦ caudal to orbitomeatal line (OML), an accepted technique used to examine MTL width in previous CT studies. The MTL width measured midway and at its thinnest between the anterior–posterior borders of the midbrain was highly reproducible (intraclass correlation coefficients >0.98) using these new methods. These simple decision rules, individualized orientation along the hippocampus and using a standardized landmark like the ICS, make these measures more comparable across subjects, and hence more useful in detecting and monitoring MTL atrophy in dementia. © 2002 Elsevier Science Inc. All rights reserved.
FORMULATION OF PROBABILISTIC DIFFERENTIAL EQUATIONS USING IMAGE TECHNOLOGY IN NO DATA PROBLEM

In Bayes decision making rule, we consider two distributions of subjectivity distribution which expresses human subjectivity and membership function which converts and maps state of nature into fuzzy event by membership function. The membership function is a kind of filter function that is set by each decision maker and transforms and maps the natural state into fuzzy events with membership functions. As this filter function is set by the subjectivity of each decision maker, consequently giving a certain degree of freedom to fuzzy events. With Zadeh, a fuzzy set was proposed, and Tanaka et al extended it to a decision-making problem. This extension is called fuzzy Bayes decision making rule, but it was shown by Hori that it is included in the subjective modification of the Wald function. This indicates that fuzzy · Bayes decision making rule is included in subjective Bayes theory. However, Hori has mentioned that Type 2 fuzzy is unique in fuzzy-Bayes decision making law. In addition, Markov decision process in the fuzzy event after the mapped and transformed the state of nature by the decision-making membership function, subjectivity after mapping the state of nature by subjective distribution and utility function Markov decision process in utility was derived. In this paper, the subjective view is a subjective distribution of the state of nature, after conversion and mapping, and utility regards the state of nature as a utility function after conversion and mapping, and map technique in the expansion principle of The mapping We formulates the stochastic differential equations based on. Furthermore, as a mapping of the mapping, fuzzy theory formalizes the stochastic differential equations based on the subjective distribution and the utility function in the fuzzy event as type 3 fuzzy. Here, it is considered that solutions of these stochastic differential equations follow Ito's integral. As a future subject, I would like to study the effectiveness and validity of fuzzy theory by actually guiding the difference in optimal behavior when not introducing fuzzy logic theory from Ito integral and Max product law. In particular, by this formulation, when the membership function is an identity function, the probability differential equations are equal when introducing fuzzy logic and not introducing it. This proves that fuzzy
ENTROPY PENALTY: TOWARDS GENERALIZATION BE-

It has been shown that instead of learning actual object features, deep networks tend to exploit non-robust (spurious) discriminative features that are shared between training and test sets. Therefore, while they achieve state of the art performance on such test sets, they achieve poor generalization on out of distribution (OOD) samples where the IID (independent, identical distribution) assumption breaks and the distribution of non-robust features shifts. Through theoretical and empirical analysis, we show that this happens because maximum likelihood training (without appropriate regularization) leads the model to depend on all the correlations (including spurious ones) present between inputs and targets in the dataset. We then show evidence that the information bottleneck (IB) principle can address this problem. To do so, we propose a regularization approach based on IB called Entropy Penalty, that reduces the model’s dependence on spurious features– features corresponding to such spurious correlations. This allows deep networks trained with Entropy Penalty to generalize well even under distribution shift of spurious features. As a controlled test-bed for evaluating our claim, we train deep networks with Entropy Penalty on a colored MNIST (C-MNIST) dataset and show that it is able to generalize well on vanilla MNIST, MNIST-M and SVHN datasets in addition to an OOD version of C-MNIST itself. The baseline regularization methods we compare against fail to generalize on this test-bed.
Secure UAV-Enabled Communication Using Han–Kobayashi Signaling

This paper proposes Han-Kobayashi signaling (HKS), under which each pair of users decodes a common message to improve their throughput, for UAV-enabled multi-user communication. Given that only a single transmit antenna is used and thus there is no null space of users’ channels for inserting an artificial noise that would effectively help to jam an eavesdropper without interfering the users’ desired signals, a new information and artificial noise transfer scheme to address physical layer security (PLS) for the considered networks is investigated. Under this scheme, the UAV sends the confidential information to its users within a fraction of the time slot and sends the artificial noise within the remaining fraction. Accordingly, the problem of jointly optimizing the time-fraction, bandwidth and power allocation to maximize the users’ worst secrecy throughput is formulated. New inner approximations are proposed for developing path-following algorithms for its computation. Simulation shows that the proposed information and artificial noise transfer enables not only HKS but also orthogonal multi-access and nonorthogonal multi-access to provide PLS for UAV-enabled communication even when the eavesdropper is in the best channel condition. HKS outperforms the other two schemes in terms of users’ worst secrecy throughput.
Sentiment Augmented Attention Network for Cantonese Restaurant Review Analysis

Online reviews written in Cantonese style are widely utilized by native Cantonese speakers and a large amount of Cantonese reviews are available on the Internet. However, only few studies on Cantonese sentiment analysis are reported as there is a serious lack of resources including annotated corpora and adequate lexical collections. In this work, we present a novel approach for sentiment analysis of Cantonese style text by incorporating sentiment knowledge into the attention mechanism in the state-of-the-art deep learning based Long Short-Term Memory network, referred to as the sentiment augmented attention (LSTM-SAT). A restaurant review dataset is first collected from Openrice, a popular restaurant review website mostly written in Cantonese style with naturally annotated rating labels. We then extract a Cantonese sentiment lexicon based on an automatic construction method to obtain both the sentiment terms and their polarities using sentiment scores of the review text. The automatically obtained terms can then be used to augment a manually obtained small Cantonese sentiment lexicon. Furthermore, we propose a novel method to incorporate lexical knowledge in the sentiment lexicon to the attention layer as the prior knowledge in an LSTM model to further highlight the importance of sentiment words. Experimental results show that our automatically constructed Cantonese sentiment lexicon helps improve coverage and this type of sentiment knowledge can be a semantically meaningful information in deep learning models. This information indeed serves as effective information as our proposed LSTM-SAT shows a significant improvement on the performance of sentiment classification.
Audit publicity and tax compliance: a quasi-natural experiment

We use confidential data on Value Added Tax payments at the sector level, in two large Italian cities, to estimate the effect of audits publicity on tax compliance of local sellers. By employing a Difference-in-Differences identification strategy, we find that such publicity has a positive effect on fiscal declarations made shortly after. The results suggest that increasing awareness on future audits via the media can be an important instrument in the hands of tax authorities.
Twentieth International Working Seminar on Production Economics PREPRINTS

Additive manufacturing is now being increasingly utilised in industry especially when the batches are small but the customisation is high. The decisions on the allocation of orders to the machines used for additive manufacturing is important in terms of the completion time related measures (e.g. makespan, flow time) and delivery date related performance measures (e.g. total lateness, maximum lateness, number of tardy jobs), as well as the cost for production. This research focuses on the production scheduling problem in a multiple additive manufaturing/3D printing machine environment. Parts have release dates and due dates. A genetic algorithm approach is developed to minimise the maximum lateness where there are more than one additive manufacturing machines with different capacity specifications (i.e. height and width). The parts are allocated to these parallel additive manufacturing machines in batches considering their release dates, due dates as well as the resource constraints. A detailed numerical example is provided to illustrate the running mechanism of the GA and some improvements are made to increase its performance.
Experimental Vascular Research

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A pan-cancer transcriptome analysis identifies replication fork and innate immunity genes as modifiers of response to the CHK1 inhibitor prexasertib

The combined influence of oncogenic drivers, genomic instability, and/or DNA damage repair deficiencies increases replication stress in cancer. Cells with high replication stress rely on the upregulation of checkpoints like those governed by CHK1 for survival. Previous studies of the CHK1 inhibitor prexasertib demonstrated activity across multiple cancer types. Therefore, we sought to (1) identify markers of prexasertib sensitivity and (2) define the molecular mechanism(s) of intrinsic and acquired resistance using preclinical models representing multiple tumor types. Our findings indicate that while cyclin E dysregulation is a driving mechanism of prexasertib response, biomarkers associated with this aberration lack sufficient predictive power to render them clinically actionable for patient selection. Transcriptome analysis of a pan-cancer cell line panel and in vivo models revealed an association between expression of E2F target genes and prexasertib sensitivity and identified innate immunity genes associated with prexasertib resistance. Functional RNAi studies supported a causal role of replication fork components as modulators of prexasertib response. Mechanisms that protect cells from oncogene-induced replication stress may safeguard tumors from such stress induced by a CHK1 inhibitor, resulting in acquired drug resistance. Furthermore, resistance to prexasertib may be shaped by innate immunity.
Post-failure impression management: A typology of entrepreneurs’ public narratives after business closure

What are the strategies entrepreneurs apply to present business closure to public audiences? Most entrepreneurs choose to communicate venture failure publicly so as to foster a favorable impression of failure, in effect engaging in impression management to maintain and/or repair their professional reputation for future career actions. To date, however, the focus of most research has been on managing failure within organizational settings, where organizational actors can interact closely with their audiences. We know little about entrepreneurs’ strategies in presenting failure to public audiences in cases where they have limited opportunities for interaction. In response to this, we present an analysis of public business-closure statements to generate a typology of five venture-failure narratives—Triumph, Harmony, Embrace, Offset, and Show—that explains entrepreneurs’ distinct sets of impression-management strategies to portray failure in public. In conclusion, we theorize from our public venture-failure typology to discuss how our work advances understanding of the interaction between organizational failure, impression management, and entrepreneurial narratives.
Do profit sharing investment account holders have a market discipline power in Islamic banking system ?

Market discipline is one of the main pillars for stability and resiliency in banking system (Basel II, 2004). The mechanism of market discipline primarily relies on the role of depositors who receive timely information and act accordingly through their respective accounts. Empirical evidence shows the presence of market discipline, whereby non-insured depositors react to risk factors of the bank accordingly either by withdrawing their deposit (quantity mechanism) or demanding higher return (price mechanism). In tandem with conventional banking system, Islamic banking also emphasize on market discipline, signified by the global standard number 4 issued by Islamic Financial Services Board in 2007. However, unlike conventional banking, market discipline in Islamic Banking is conjectured to work via profit-loss sharing system. That is, the role of Profit Sharing Investment Account (PSIA) holders who are exposed to the variability of profit generated from their investment. The intriguing question of whether PSIA would be more effective and efficient in implementing market discipline remains an on-going debate. Even there is no empirical study attempts to address this issue. Therefore, we perform empirical research to discover it. In this study, we use panel data of 44 Islamic banks across different regions to research the presence of market discipline in the global Islamic banking system focussing on the behaviour of the PSIA holders and their rule on governance of the Islamic banks. This study applies GMM panel technique and finds weak relationships between the behaviour of PSIA holders and the risk variables of the Islamic banks (measured by: Capital adequacy, Asset quality, Management, Earnings or Liquidity). This implies negligible effect of the current profit sharing system in exercising market discipline. This might indicates that PSIA holders remain unaware of bank’s risks as well as low quality of disclosure process from Islamic banks. Nevertheless, our empirical results for Malaysia indicate different conclusions, suggesting that PSIAH observe and correspond to the changes in the asset quality risk as well as to the changes in liquidity risk by quantity mechanism.
Symbolic Regression Analysis on the EUROfusion JET-ILW Pedestal Database

Our present understanding of the pedestal physics is still limited and its extrapolation to future JET-ILW experimental scenarios is a challenging task. To face this issue a new multi-objective genetic programming (GP) code has been implemented. This code is able to perform a multi-objective symbolic regression analysis on the EUROfusion JET-ILW pedestal database [1]. In order to improve our understanding of the obtained analytical expressions and to make easier the comparison with established results in power law form, we drive the GP search towards a class of scaling laws called generalized power laws. Possible new analytical nonlinear regression models for the pedestal thermal stored energy has been found. These results suggest that some of the new scaling laws might capture interesting experimental features that otherwise it would not be possible to obtain with ordinary power laws. Introduction The EPED model [2] can predict the H-mode JET-ILW pedestal within a relative error of about 20% when the pedestal is close to the Peeling-Ballooning (PB) boundary. However, when the pedestal is far from the PB boundary, our present understanding of the pedestal physics is still lacking [3,4]. Moreover, the extrapolation to future JET-ILW experimental scenarios and to different tokamaks is even more challenging. In order to improve our modelling capabilities of the JET-ILW pedestal, a new multi-objective genetic programming (GP) [5] code has been implemented. GP is an evolutionary search algorithm inspired by natural evolution which encodes a solution to a problem as a computer program. When the instruction set used to build new solutions is made of mathematical functions only, GP becomes an instance of symbolic regression (SR) [5,6]. SR is a non-parametric nonlinear regression technique where, not only the model’s parameters are fitted to the data, but also the regression model is selected in a data-driven way. Symbolic regression via GP (GPSR) is a powerful technique particularly suited to 46 EPS Conference on Plasma Physics P2.1023
Secure Secondary Authentication Framework for Efficient Mutual Authentication on a 5G Data Network

The service-based architecture of the Fifth Generation(5G) had combined the services and security architectures and enhanced the authentication process of services to expand the coverage of the network, including heterogeneous devices. This architecture uses the secondary authentication for mutual authentication between the User Equipment (UE) and the Data Network (DN) to authenticate devices and services. However, this authentication mechanism can cause a signaling storm in the Non-Access Stratum (NAS) because the end node needs to communicate with the authentication server of the NAS area. This problem could affect the availability of the network when the network is extended. This research proposes a mutual authentication framework that can efficiently perform a mutual authentication process through secondary authentication between UE and DN. The proposed framework uses newly devised network functions: Secondary Authentication Function (SAF) and the Authentication Data Management Function (ADMF). This framework proposes a methodology at the protocol level for efficient mutual authentication using the mobile edge computing architecture. We analyzed the proposed framework in the point of security considerations, and we evaluated the effect of the framework on the traffic of the NAS layer and user experience. Our simulation results show that the proposed framework can reduce the NAS traffic by 39% and total traffic of the overall network by 10%.