6,095 research outputs found
Revisão taxonómica do género Calendula L. (Asteraceae - Calenduleae) na Península Ibérica e Marrocos
The genus Calendula L. (Asteraceae - Calenduleae) includes, depending on the author, 10 to 25 species, distributed mainly in the Mediterranean basin. The taxonomy of this genus is considered to be extremely difficult, due to a great morphological variability, doubtfull relevance of some of the characters used to distinguish its species (e.g. the life form: annual or perennial; the habit: erect or diffuse, shape of the leaves, indumentum, relative size of the capitula and colour of disc or ray florets, achene morphology), but also due to the hybridization and polyploidization. Despite the numerous studies that have been published, no agreement on the classification and characters used to discriminate between taxa has been reached. A taxonomic study of the genus Calendula was conducted for the Iberian Peninsula and Morocco, aiming at (1) access the morphological variability between and within taxa, (2) confirm the chromosome numbers, (3) increase the nuclear DNA content estimations, (4) re-evaluate taxa delimitations and circumscription, and (5) reassess, and redefine, the
descriptions and characters useful to distinguish taxa. In order to achieve a satisfying taxonomic core, extensive fieldwork, detailed morphometric analysis,
chorological, karyological and genome size studies were conducted. For the Iberian Peninsula, four species were recognized, including nine subspecies (between these two new subspecies were described). For Morocco, including some taxa from Algeria and Tunisia 13 species were recognized (two new species and a nomenclatural change), including 15 subspecies (among these eight new subspecies were described). To corroborate the results obtained and to evaluate the evolutionary relationships among taxa, phylogenetic studies using molecular methods, such as ITS, microsatellites or other molecular markers, should be used.O género Calendula L. (Asteraceae - Calenduleae) inclui, dependendo do autor, 10 a 25 espécies, distribuídas essencialmente na bacia do Mediterrâneo. A taxonomia deste género é considerada extremamente difícil, devido à grande variabilidade morfológica, discutivel relevância de alguns dos caracteres utilizados para distinguir suas espécies (por exemplo, a forma de vida: anual ou perene, o hábito: erecto ou difuso, a forma das folhas, o indumento, o tamanho e a cor dos capítulos e a morfologia dos aquénios), mas também devido à
hibridização e poliploidização. Apesar dos inúmeros estudos que foram publicados, não foi alcançado um acordo sobre a classificação e os caracteres utilizados para discriminar as suas espécies. Um estudo taxonómico do género Calendula foi realizado para a Península Ibérica e Marrocos, com o objectivo de (1) verificar a variabilidade morfológica, (2) confirmar o número de cromossomas, (3) aumentar as estimativas de conteúdo em ADN, (4) reavaliar a delimitação e a circunscrição dos taxa, e (5) reavaliar e redefinir as descrições e caracteres úteis para os distinguir. Para alcançar uma robustês taxonómica satisfatória, foram realizados extensos trabalhos de campo, análise morfométrica detalhada, abordagens corológicas, cariológicas e quanto ao conteúdo em ADN. Para a Península Ibérica, quatro espécies foram reconhecidas, incluindo nove subespécies (entre essas duas novas subespécies foram descritas). Para Marrocos, incluindo alguns taxa da Argelia e Tunisia, foram reconhecidas 13 espécies (duas novas e uma mudança nomenclatural), incluindo 15 subespécies (entre essas oito novas subespécies foram descritas). Para corroborar os resultados obtidos e avaliar as relações evolutivas e filogenéticas entre os taxa, estudos que utilizem diferentes métodos
moleculares, tais como ITS, microsatélites ou outros marcadores moleculares, devem ser utilizados.Apoio financeiro do Laboratório Associado CESAM - Centro de Estudos do Ambiente e do Mar (AMB/50017) financiado por fundos nacionais através da FCT/MCTES e cofinanciado pelo FEDER (POCI-01-0145-FEDER-007638), no âmbito do Acordo de Parceria PT2020, e Compete 2020Programa Doutoral em Biologi
Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions
Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units.
The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures.
The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system.
Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter.
The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation
3D transdimensional seismic tomography of the Earth's inner core using body waves and normal modes
Since the discovery of the inner core almost 100 years ago, the seismological community has found that the inner core contains significant heterogeneity in its elastic structure. This observation is significant and in many ways unexpected; we believe the inner core to be (relatively) chemically homogeneous consisting primarily of iron and nickel. Yet we observe that seismic waves which pass through the inner core travel faster in a north-south direction than an east-west direction and that the spectra of whole Earth oscillations are anomalously split in a way which is consistent with the same velocity difference. This difference in velocity between two directions through the inner core is called anisotropy, and from mineral physics we have reason to believe that this anisotropy is caused by the alignment of iron crystals which are themselves anisotropic at inner core temperatures and pressures. The primary goal of this thesis is to constrain, as well as possible, the elastic structure of the inner core. We expand upon the body wave dataset by adding new observations of paths which travel almost parallel to Earth's axis of rotation, giving us improved sensitivity to velocity in the north-south direction in the inner core. We combine our new data with other body wave datasets to produce a 3D seismic tomographic model of the inner core. This model utilised a transdimensional Markov chain Monte Carlo methodology which not only determines the best fitting anisotropy structure in the inner core, but also the uncertainties in our model and it does not require any prior assumptions on the parameterization of the inner core. The advantage of this method is significant, especially because the relatively poor sampling of the inner core means that prior assumptions on the parameterization may significantly affect the final model. In the transdimensional approach the parameterization is a part of the inversion. In our new transdimensional model we confirmed many previous observations, including an isotropic layer of 100 km thickness at the top of the inner core and that the inner core is split broadly into a western region and an eastern region. We are now able to make new robust observations, seeing for the first time that the western anisotropic zone is isolated to the northern hemisphere and that the inner most inner core exists but primarily in the eastern region. These observations are significant as it provides new insight into the mechanisms of inner core formation and dynamics, and we discuss the potential implications for inner core geodynamics. It is important in deep Earth research to bring together as many sources of information as possible. We have also measured 18 normal modes sensitive to the inner core. We used a splitting function approximation and a grid search methodology to constrain the uncertainties in the measurement. The data were then used to produce a preliminary 1D transdimensional model of inner core anisotropy using polynomial basis functions and find a model which agrees reasonably well with the spherical average of compressional anisotropy from the body wave model
Spatial Invasion of Cooperative Parasites
In this paper we study invasion probabilities and invasion times of
cooperative parasites spreading in spatially structured host populations. The
spatial structure of the host population is given by a random geometric graph
on , , with a Poisson()-distributed number of
vertices and in which vertices are connected over an edge when they have a
distance of at most for some
and . At a host infection many parasites are
generated and parasites move along edges to neighbouring hosts. We assume that
parasites have to cooperate to infect hosts, in the sense that at least two
parasites need to attack a host simultaneously. We find lower and upper bounds
on the invasion probability of the parasites in terms of survival probabilities
of branching processes with cooperation. Furthermore, we characterize the
asymptotic invasion time.
An important ingredient of the proofs is a comparison with infection dynamics
of cooperative parasites in host populations structured according to a complete
graph, i.e. in well-mixed host populations. For these infection processes we
can show that invasion probabilities are asymptotically equal to survival
probabilities of branching processes with cooperation.
Furthermore, we build in the proofs on techniques developed in [BP22], where
an analogous invasion process has been studied for host populations structured
according to a configuration model.
We substantiate our results with simulations
Robustness and Interpretability of Neural Networks’ Predictions under Adversarial Attacks
Le reti neurali profonde (DNNs) sono potenti modelli predittivi, che superano le capacità umane in una varietà di task. Imparano sistemi decisionali complessi e flessibili dai dati a disposizione e raggiungono prestazioni eccezionali in molteplici campi di apprendimento automatico, dalle applicazioni dell'intelligenza artificiale, come il riconoscimento di immagini, parole e testi, alle scienze più tradizionali, tra cui medicina, fisica e biologia. Nonostante i risultati eccezionali, le prestazioni elevate e l’alta precisione predittiva non sono sufficienti per le applicazioni nel mondo reale, specialmente in ambienti critici per la sicurezza, dove l'utilizzo dei DNNs è fortemente limitato dalla loro natura black-box. Vi è una crescente necessità di comprendere come vengono eseguite le predizioni, fornire stime di incertezza, garantire robustezza agli attacchi avversari e prevenire comportamenti indesiderati.
Anche le migliori architetture sono vulnerabili a piccole perturbazioni nei dati di input, note come attacchi avversari: manipolazioni malevole degli input che sono percettivamente indistinguibili dai campioni originali ma sono in grado di ingannare il modello in predizioni errate. In questo lavoro, dimostriamo che tale fragilità è correlata alla geometria del manifold dei dati ed è quindi probabile che sia una caratteristica intrinseca delle predizioni dei DNNs. Questa
condizione suggerisce una possibile direzione al fine di ottenere robustezza agli attacchi: studiamo la geometria degli attacchi avversari nel limite di un numero infinito di dati e di pesi per le reti neurali Bayesiane, dimostrando che, in questo limite, sono immuni agli attacchi avversari gradient-based. Inoltre, proponiamo alcune tecniche di training per migliorare la robustezza delle architetture deterministiche. In particolare, osserviamo sperimentalmente che ensembles di reti neurali addestrati su proiezioni casuali degli input originali in spazi basso-dimensionali sono più resistenti agli attacchi.
Successivamente, ci concentriamo sul problema dell'interpretabilità delle predizioni delle reti nel contesto delle saliency-based explanations. Analizziamo la stabilità delle explanations soggette ad attacchi avversari e dimostriamo che, nel limite di un numero infinito di dati e di pesi, le interpretazioni Bayesiane sono più stabili di quelle fornite dalle reti deterministiche. Confermiamo questo comportamento in modo sperimentale nel regime di un numero finito di dati.
Infine, introduciamo il concetto di attacco avversario alle sequenze di amminoacidi per protein Language Models (LM). I modelli di Deep Learning per la predizione della struttura delle proteine, come AlphaFold2, sfruttano le architetture Transformer e il loro meccanismo di attention per catturare le proprietà strutturali e funzionali delle sequenze di amminoacidi. Nonostante l'elevata precisione delle predizioni, perturbazioni biologicamente piccole delle sequenze di input, o anche mutazioni di un singolo amminoacido, possono portare a strutture 3D sostanzialmente diverse. Al contempo, i protein LMs sono insensibili alle mutazioni che inducono misfolding o disfunzione (ad esempio le missense mutations). In particolare, le predizioni delle coordinate 3D non rivelano l'effetto di unfolding indotto da queste mutazioni. Pertanto, esiste un'evidente incoerenza tra l'importanza biologica delle mutazioni e il conseguente cambiamento nella predizione strutturale. Ispirati da questo problema, introduciamo il concetto di perturbazione avversaria delle sequenze proteiche negli embedding continui dei protein LMs. Il nostro metodo utilizza i valori di attention per rilevare le posizioni degli amminoacidi più vulnerabili nelle sequenze di input. Le mutazioni avversarie sono biologicamente diverse dalle sequenze di riferimento e sono in grado di alterare in modo significativo le strutture 3D.Deep Neural Networks (DNNs) are powerful predictive models, exceeding human capabilities in a variety of tasks. They learn complex and flexible decision systems from the available data and achieve exceptional performances in multiple machine learning fields, spanning from applications in artificial intelligence, such as image, speech and text recognition, to the more traditional sciences, including medicine, physics and biology. Despite the outstanding achievements, high performance and high predictive accuracy are not sufficient for real-world applications, especially in safety-critical settings, where the usage of DNNs is severely limited by their black-box nature. There is an increasing need to understand how predictions are performed, to provide uncertainty estimates, to guarantee robustness to malicious attacks and to prevent unwanted behaviours.
State-of-the-art DNNs are vulnerable to small perturbations in the input data, known as adversarial attacks: maliciously crafted manipulations of the inputs that are perceptually indistinguishable from the original samples but are capable of fooling the model into incorrect predictions. In this work, we prove that such brittleness is related to the geometry of the data manifold and is therefore likely to be an intrinsic feature of DNNs’ predictions. This negative
condition suggests a possible direction to overcome such limitation: we study the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks and prove that, in this limit, they are immune to gradient-based adversarial attacks. Furthermore, we propose some training techniques to improve the adversarial robustness of deterministic architectures. In particular, we experimentally observe that ensembles of NNs trained on random projections of the original inputs into lower dimensional spaces are more resilient to the attacks.
Next, we focus on the problem of interpretability of NNs’ predictions in the setting of saliency-based explanations. We analyze the stability of the explanations under adversarial attacks on the inputs and we prove that, in the large-data and overparameterized limit, Bayesian interpretations are more stable than those provided by deterministic networks. We validate this behaviour in multiple experimental settings in the finite data regime.
Finally, we introduce the concept of adversarial perturbations of amino acid sequences for protein Language Models (LMs). Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically small perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are insensitive to mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3D structures
International Academic Symposium of Social Science 2022
This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
Deconstructing and Reconstructing Local Identities in the Physical Landscape: The role(s) of Roman remains in the social changes of the sixth and seventh centuries in the former province of Britain
This thesis examines the evidence for engagement with and avoidance of Roman remains in the landscape of two regions within the former province of Britain, Sussex and the eastern part of the northern military frontier. This information is used to consider the attitudes that localised societies held towards the remain of the past, and how this engagement related to the social changes of the period.
Chapter 1 introduces the research context and the aims, setting out research questions. Chapter 2 presents the state of current knowledge and prior approaches to studies of the landscape and the early medieval period, and places the study within the wider theoretical and methodological contexts of landscape studies, the use of GIS, and the consideration of ‘the past in the past’. It then examines attitudes towards the Roman past as evidence in other forms of cultural expression, ranging from modes of displaying identity and authority to the recycling of Roman metalwork, considering the degree of consistency in attitudes towards the past. This is followed in chapter 3 by an explanation of the methodology adopted.
The following chapters look at engagement with Roman remains, in post-Roman Sussex in chapter 4, and the north-east military frontier, from southern Northumberland south to the North York Moors, in chapter 5. The evidence is contextualised against the distribution of activity in the physical landscape and the presence of prehistoric remains.
Chapter 6 pulls together these threads together with previous regional studies, with a focus on identifying regional and chronological similarities and contrasts, and the reasons underlying these patterns. Finally, chapter 7 considers the strengths and weaknesses of the study, and areas for future work
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