9,268 research outputs found

    Alʔilbīrī’s Book of the rational conclusions. Introduction, Critical Edition of the Arabic Text and Materials for the History of the Ḫawāṣṣic Genre in Early Andalus

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    [eng] The Book of the rational conclusions, written perhaps somewhen in the 10th c. by a physician from Ilbīrah (Andalus), is a multi-section medical pandect. The author brings together, from a diversity of sources, materials dealing with matters related to drug-handling, natural philosophy, therapeutics, medical applications of the specific properties of things, a regimen, and a dispensatory. This dissertation includes three different parts. First the transmission of the text, its contents, and its possible context are discussed. Then a critical edition of the Arabic text is offered. Last, but certainly not least, the subject of the specific properties is approached from several points of view. The analysis of Section III of the original book leads to an exploration of the early Andalusī assimilation of this epistemic tradition and to the establishment of a well-defined textual family in which our text must be inscribed. On the other hand, the concept itself of ‘specific property’ is often misconstrued and it is usually made synonymous to magic and superstition. Upon closer inspection, however, the alleged irrationality of the knowledge of these properties appears to be largely the result of anachronistic interpretation. As a complement of this particular research and as an illustration of the genre, a sample from an ongoing integral commentary on this section of the book is presented.[cat] El Llibre de les conclusions racionals d’un desconegut metge d’Ilbīrah (l’Àndalus) va ser compilat probablement durant la segona meitat del s. X. Es tracta d’un rudimentari però notablement complet kunnaix (un gènere epistèmic que és definit sovint com a ‘enciclopèdia mèdica’) en què l’autor aplega materials manllevats (sovint de manera literal i no-explícita) de diversos gèneres. El llibre obre amb una secció sobre apoteconomia (una mena de manual d’apotecaris) però se centra després en les diferents branques de la medicina. A continuació d’uns prolegòmens filosòfics l’autor copia, amb mínima adaptació lingüística, un tractat sencer de terapèutica, després un altre sobre les aplicacions mèdiques de les propietats específiques de les coses, una sèrie de fragments relacionats amb la dietètica (un règim en termes tradicionals) i, finalment, una col·lecció de receptes mèdiques. Cadascuna d’aquestes seccions mostren evidents lligams d’intertextualitat que apunten cap a una intensa activitat sintetitzadora de diverses tradicions aliades a la medicina a l’Àndalus califal. El text és, de fet, un magnífic objecte sobre el qual aplicar la metodologia de la crítica textual i de fonts. L’edició crítica del text incorpora la dimensió cronològica dins l’aparat, que esdevé així un element contextualitzador. Quant l’estudi de les fonts, si tot al llarg de la primera part d’aquesta tesi és només secundari, aquesta disciplina pren un protagonisme gairebé absolut en la tercera part, especialment en el capítol dedicat a l’anàlisi individual de cada passatge recollit en la secció sobre les propietats específiques de les coses

    Enhancing robustness in video recognition models : Sparse adversarial attacks and beyond

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    Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this paper, we propose a sparse adversarial attack strategy on videos (DeepSAVA). Our model aims to add a small human-imperceptible perturbation to the key frame of the input video to fool the classifiers. To carry out an effective attack that mirrors real-world scenarios, our algorithm integrates spatial transformation perturbations into the frame. Instead of using the norm to gauge the disparity between the perturbed frame and the original frame, we employ the structural similarity index (SSIM), which has been established as a more suitable metric for quantifying image alterations resulting from spatial perturbations. We employ a unified optimisation framework to combine spatial transformation with additive perturbation, thereby attaining a more potent attack. We design an effective and novel optimisation scheme that alternatively utilises Bayesian Optimisation (BO) to identify the most critical frame in a video and stochastic gradient descent (SGD) based optimisation to produce both additive and spatial-transformed perturbations. Doing so enables DeepSAVA to perform a very sparse attack on videos for maintaining human imperceptibility while still achieving state-of-the-art performance in terms of both attack success rate and adversarial transferability. Furthermore, built upon the strong perturbations produced by DeepSAVA, we design a novel adversarial training framework to improve the robustness of video classification models. Our intensive experiments on various types of deep neural networks and video datasets confirm the superiority of DeepSAVA in terms of attacking performance and efficiency. When compared to the baseline techniques, DeepSAVA exhibits the highest level of performance in generating adversarial videos for three distinct video classifiers. Remarkably, it achieves an impressive fooling rate ranging from 99.5% to 100% for the I3D model, with the perturbation of just a single frame. Additionally, DeepSAVA demonstrates favorable transferability across various time series models. The proposed adversarial training strategy is also empirically demonstrated with better performance on training robust video classifiers compared with the state-of-the-art adversarial training with projected gradient descent (PGD) adversary

    Multi-epoch machine learning for galaxy formation

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    In this thesis I utilise a range of machine learning techniques in conjunction with hydrodynamical cosmological simulations. In Chapter 2 I present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos taken from N-body simulations. The model is built using a tree-based algorithm and incorporates subhalo properties over a wide range of redshifts as its input features. I train the model using a hydrodynamical simulation which enables it to predict black hole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. This new model surpasses the performance of previous models. Furthermore, I explore the predictive power of each input property by looking at feature importance scores from the tree-based model. By applying the method to the LEGACY N-body simulation I generate a large volume mock catalog of the quasar population at z=3. By comparing this mock catalog with observations, I demonstrate that the IllustrisTNG subgrid model for black holes is not accurately capturing the growth of the most massive objects. In Chapter 3 I apply my method to investigate the evolution of galaxy properties in different simulations, and in various environments within a single simulation. By comparing the Illustris, EAGLE, and TNG simulations I show that subgrid model physics plays a more significant role than the choice of hydrodynamics method. Using the CAMELS simulation suite I consider the impact of cosmological and astrophysical parameters on the buildup of stellar mass within the TNG and SIMBA models. In the final chapter I apply a combination of neural networks and symbolic regression methods to construct a semi-analytic model which reproduces the galaxy population from a cosmological simulation. The neural network based approach is capable of producing a more accurate population than a previous method of binning based on halo mass. The equations resulting from symbolic regression are found to be a good approximation of the neural network

    Bridging formal methods and machine learning with model checking and global optimisation

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    Formal methods and machine learning are two research fields with drastically different foundations and philosophies. Formal methods utilise mathematically rigorous techniques for software and hardware systems' specification, development and verification. Machine learning focuses on pragmatic approaches to gradually improve a parameterised model by observing a training data set. While historically, the two fields lack communication, this trend has changed in the past few years with an outburst of research interest in the robustness verification of neural networks. This paper will briefly review these works, and focus on the urgent need for broader and more in-depth communication between the two fields, with the ultimate goal of developing learning-enabled systems with excellent performance and acceptable safety and security. We present a specification language, MLS2, and show that it can express a set of known safety and security properties, including generalisation, uncertainty, robustness, data poisoning, backdoor, model stealing, membership inference, model inversion, interpretability, and fairness. To verify MLS2 properties, we promote the global optimisation-based methods, which have provable guarantees on the convergence to the optimal solution. Many of them have theoretical bounds on the gap between current solutions and the optimal solution

    ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation

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    Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triple-branch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to improve model’s performance further. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fully-supervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer

    Capsule networks with residual pose routing

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    Capsule networks (CapsNets) have been known difficult to develop a deeper architecture, which is desirable for high performance in the deep learning era, due to the complex capsule routing algorithms. In this article, we present a simple yet effective capsule routing algorithm, which is presented by a residual pose routing. Specifically, the higher-layer capsule pose is achieved by an identity mapping on the adjacently lower-layer capsule pose. Such simple residual pose routing has two advantages: 1) reducing the routing computation complexity and 2) avoiding gradient vanishing due to its residual learning framework. On top of that, we explicitly reformulate the capsule layers by building a residual pose block. Stacking multiple such blocks results in a deep residual CapsNets (ResCaps) with a ResNet-like architecture. Results on MNIST, AffNIST, SmallNORB, and CIFAR-10/100 show the effectiveness of ResCaps for image classification. Furthermore, we successfully extend our residual pose routing to large-scale real-world applications, including 3-D object reconstruction and classification, and 2-D saliency dense prediction. The source code has been released on https://github.com/liuyi1989/ResCaps

    The Fallacy of Systemic Racism in the American Criminal Justice System

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    Critics of the criminal justice system have repeatedly charged it with systemic racism. It is a tenet of the “war” on the “War on Drugs,” it is a justification used by the so-called “progressive prosecutors” to reject the “Broken Windows” theory of law enforcement, and it is an article of faith of the “Defund the Police!” movement. Even President Joe Biden and his chief lieutenants leveled the same allegation early in this administration. Although the President has eschewed the belief that Americans are a racist people, others have not, proclaiming that virtually anyone who is white is a racist. Yet, few people have defined what they mean by that term. This Article examines what it could mean and tests the truth of the systemic racism claim under each possible definition. None stands up to scrutiny. One argument is that the American citizens who run our many institutions are motivated by racial animus. But the evidence is that racial animus is no longer tolerated in society, and what is more, the criminal justice system strives to identify it when it does occur and to remedy it. Another argument says that the overtly racist beliefs and practices of the past have created lingering racist effects, but this argument cherry-picks historical facts (when it does not ignore them altogether) and fails to grapple with the country’s historic and ongoing efforts to eliminate racial discrimination. It also assumes a causal relationship between past discrimination and present disparities that is unsupported and often contradicted by the evidence. Yet another argument relies psychological research to claim that white Americans are animated by a subconscious racial animus. That research, however, has been debunked. Still another argument says that the criminal justice system is systemically racist because it has disparate effects across racial groups, but this argument looks only at the offenders’ side of the criminal justice system and fails to consider the effect of the criminal justice system on victims. Proponents of the systemic racism theory often proffer “solutions” to it. This Article examines those too and finds that many would, in fact, harm the very people they aim to help. In the context of the “War on Drugs,” where so much of the rhetoric is focused, the authors examine these arguments and solutions. The bottom line is this: the claim of systemic racism in the criminal justice system is unjustified

    Self-supervised learning for transferable representations

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    Machine learning has undeniably achieved remarkable advances thanks to large labelled datasets and supervised learning. However, this progress is constrained by the labour-intensive annotation process. It is not feasible to generate extensive labelled datasets for every problem we aim to address. Consequently, there has been a notable shift in recent times toward approaches that solely leverage raw data. Among these, self-supervised learning has emerged as a particularly powerful approach, offering scalability to massive datasets and showcasing considerable potential for effective knowledge transfer. This thesis investigates self-supervised representation learning with a strong focus on computer vision applications. We provide a comprehensive survey of self-supervised methods across various modalities, introducing a taxonomy that categorises them into four distinct families while also highlighting practical considerations for real-world implementation. Our focus thenceforth is on the computer vision modality, where we perform a comprehensive benchmark evaluation of state-of-the-art self supervised models against many diverse downstream transfer tasks. Our findings reveal that self-supervised models often outperform supervised learning across a spectrum of tasks, albeit with correlations weakening as tasks transition beyond classification, particularly for datasets with distribution shifts. Digging deeper, we investigate the influence of data augmentation on the transferability of contrastive learners, uncovering a trade-off between spatial and appearance-based invariances that generalise to real-world transformations. This begins to explain the differing empirical performances achieved by self-supervised learners on different downstream tasks, and it showcases the advantages of specialised representations produced with tailored augmentation. Finally, we introduce a novel self-supervised pre-training algorithm for object detection, aligning pre-training with downstream architecture and objectives, leading to reduced localisation errors and improved label efficiency. In conclusion, this thesis contributes a comprehensive understanding of self-supervised representation learning and its role in enabling effective transfer across computer vision tasks

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
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