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Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised model for MRI contrast translation (ssGAN) that can be trained directly using undersampled k-space data. To enable semi-supervised learning on undersampled data, ssGAN introduces novel multi-coil losses in image, k-space, and adversarial domains. The multi-coil losses are selectively enforced on acquired k-space samples unlike traditional losses in single-coil synthesis models. Comprehensive experiments on retrospectively undersampled multi-contrast brain MRI datasets are provided. Our results demonstrate that ssGAN yields on par performance to a supervised model, while outperforming single-coil models trained on coil-combined magnitude images. It also outperforms cascaded reconstruction-synthesis models where a supervised synthesis model is trained following self-supervised reconstruction of undersampled data. Thus, ssGAN holds great promise to improve the feasibility of learning-based multi-contrast MRI synthesis.

Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods.

The Onion Router (Tor) is used by adversaries and warfighters alike to encrypt session information and gain anonymity on the internet. Since its creation in 2002, Tor has gained popularity by terrorist organizations, human traffickers, and illegal drug distributors who wish to use Tor services to mask their identity while engaging in illegal activities. Fingerprinting attacks assist in thwarting these attempts. Website fingerprinting (WF) attacks have been proven successful at linking a user to the website they have viewed over an encrypted Tor connection. With consumer video streaming traffic making up a large majority of internet traffic and sites like YouTube remaining in the top visited sites in the world, it is just as likely that adversaries are using videos to spread misinformation, illegal content, and terrorist propaganda. Video fingerprinting (VF) attacks look to use encrypted network traffic to predict the content of encrypted video sessions in closed- and open-world scenarios. This research builds upon an existing dataset of encrypted video session data and use statistical analysis to train a machine-learning classifier, using deep fingerprinting (DF), to predict videos viewed over Tor. DF is a machine learning technique that relies on the use of convolutional neural networks (CNN) and can be used to conduct VF attacks against Tor. By analyzing the results of these experiments, we can more accurately identify malicious video streaming activity over Tor. Civilian Approved for public release. Distribution is unlimited.

The Contestation Manifesto and its associated paraphernalia are artefacts from a speculative, near-future community action known as the Contestation Café. Being one in a series of research through design projects on contestation, the Contestation Café is a critical, yet also practical, design intervention rooted in how the act of repair has moved through the physical into the digital, and the shared values therein. Tracing the history of electronic repair - from the early valve radios, the invention of the transistor, microchips, programmable devices and through to IoT, connected devices and the “fluid assemblages” that emerge - we can see the shared values of the physical act of repair with the more intangible act of contestation in the digital world of algorithmic systems. Overlaying the values and tactics of the Right to Repair movement with the emerging concerns around data-driven systems we find ourselves examining the Repair Café as a potential model for community contestation and the construction of publics. The Repair Café started in Amsterdam in 2009 and has since spread to 35 countries with over 1700 instances of these cafés. The Repair Café is not intended to be a place where you bring your broken appliances for someone else to repair - rather, it is a place where you learn how to repair and recycle your own products and devices, and - more importantly - a place where you simply learn that things can be fixed rather than thrown out. In a similar spirit, the Contestation Café would be a place for those who feel mistreated by automated decision systems, AIs and algorithms, to bring their broken interactions and their unfair decisions to learn how to contest, push back and reclaim their agency and autonomy. Rather than Repairers, the Contestation Café has a panel of Fixers - people who inhabit the space between designers and users, with a particular knowledge of these systems and how to map and navigate them - who are there to share their experience and knowledge, and to guide the user in the ways of contestation and to become Fixers of their own futures. Although the Contestation Café is a speculative concept based on research into the shared values of contestation and repair, it has developed a life of its own through the process of imagining how it would work and what it would look like. Through the act of writing a manifesto for contestation, the space has manifested itself in the present and is waiting only for the fixers and public to arrive. Plans are already in progress for its first real instance and all of the imagined artefacts presented here will inform this process. https://contestationcafe.org/

Jurors’ evaluations of expert testimony should focus on the quality of experts’ testimony to inform decisions. However, when experts are highly paid and testify frequently, jurors are more likely to see them as ‘hired guns’, biasing their decisions and ratings of the expert’s credibility. In this study, I examine whether using court appointed experts can attenuate the effects of these heuristics on jurors’ decisions by varying who the expert testifies for, the quality of the testimony, and the presence of the heuristics. Thus, I will test whether using court-appointed experts can eliminate the biasing hired gun effect on jurors’ decisions.

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models' robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability., Comment: MDPI Mach. Learn. Knowl. Extr. 2021, 3(2), 333-356; https://www.mdpi.com/2624-800X/1/2/14

Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of-the-art classification algorithms in tabular data, tree boosting outperforms deep learning (Zhang et al. in Expert Syst Appl 82:128–150, 2017). For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S. The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on four popular data sets and compare against state-of-the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions.

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning.} ResViT's generator employs a central bottleneck comprising novel aggregated residual transformer (ART) blocks that synergistically combine residual convolutional and transformer modules. Residual connections in ART blocks promote diversity in captured representations, while a channel compression module distills task-relevant information. A weight sharing strategy is introduced among ART blocks to mitigate computational burden. A unified implementation is introduced to avoid the need to rebuild separate synthesis models for varying source-target modality configurations. Comprehensive demonstrations are performed for synthesizing missing sequences in multi-contrast MRI, and CT images from MRI. Our results indicate superiority of ResViT against competing CNN- and transformer-based methods in terms of qualitative observations and quantitative metrics.

The database of faces containing sensitive information is at risk of being targeted by unauthorized automatic recognition systems, which is a significant concern for privacy. Although there are existing methods that aim to conceal identifiable information by adding adversarial perturbations to faces, they suffer from noticeable distortions that significantly compromise visual perception, and therefore, offer limited protection to privacy. Furthermore, the increasing prevalence of appearance anxiety on social media has led to users preferring to beautify their faces before uploading images. In this paper, we design a novel face database protection scheme via beautification with chaotic systems. Specifically, we construct the adversarial face with better visual perception via beautification for each face in the database. In the training, the face matcher and the beautification discriminator are federated against the generator, prompting it to generate beauty-like perturbations on the face to confuse the face matcher. Namely, the pixel changes produced by face beautification mask the adversarial perturbations. Moreover, we use chaotic systems to disrupt the order of adversarial faces in the database, further mitigating the risk of privacy leakage. Our scheme has been extensively evaluated through experiments, which show that it effectively defends against unauthorized attacks while also yielding good visual results.

En el presente trabajo se realiza un estudio histórico, analítico, sistemático y jurisprudencial del Ministerio Público en Colombia y su participación en el proceso penal, como interviniente especial, lo cual permite establecer la necesidad y/o utilidad de su intervención al interior del proceso penal con tendencia acusatoria, bajo el entendido de que estamos en presencia de un proceso penal, adaptado a nuestras especiales realidades, distando mucho de parecerse a un sistema acusatorio puro, lo cual permitirá al lector comprender el grado de incidencia que tiene su participación en el proceso penal y la utilidad de la figura en estudio frente al tema de la defensa de los intereses de la sociedad, de los derechos humanos y del debido proceso, conceptos que han sido caros en el sistema procesal penal colombiano y que se han querido proteger a ultranza de la arbitrariedad en el proceso penal, razones que fueron entendidas por el legislador, cuando previó su participación en el proceso penal, como importante para el mantenimiento de las garantías y derechos de los ciudadanos, quienes se ven expuestos a la pugna entre eficientismo y garantismo. In the present work, a historical, analytical, systematic and jurisprudential study of the Public Ministry in Colombia and its participation in the criminal process, as a special intervener, is carried out, which allows establishing the need and usefulness of its intervention within the criminal process. with an accusatory tendency, under the understanding that we are in the presence of a criminal process, adapted to our special realities, far from resembling a pure accusatory system, which will allow the reader to understand the degree of incidence that their participation has in the process criminal law and the usefulness of the figure under study in relation to the issue of defending the interests of society, human rights and due process, concepts that have been expensive in the Colombian criminal procedure system and that have been sought to be protected at all arbitrariness in the criminal process, reasons that were understood by the legislator, when he foresaw his participation in the criminal process, as important for the maintenance of the guarantees and rights of citizens, who are exposed to conflict between efficiency and guarantee. Maestría Magíster en Justicia y Tutela de los Derechos con énfasis en Ciencias Penales y Criminológicas (Modalidad de Investigación)

We present SNEAD, an extension of an algorithmic framework by McKelvey (1998) under which one can reduce the problem of finding Nash equilibria in finite games to a tractable unified optimization problem. Specifically, we adapt the method to tractably optimize continuous-strategy games and improve asymptotic running time over finite-strategy games. We further prove that convergence of our algorithm to zero loss guarantees a Nash equilibrium in quasiconcave games. Experimentally, our algorithm achieves state of the art performance on a variety of games, even in the face of high-dimensional, complex payoff functions. Finally, we provide supplementary theoretical frameworks which further adapt SNEAD to locate symmetric equilibria or subgame-perfect equilibria with novel algorithmic efficiency.

À partir d'études de cas centrées sur des demandes de séparation de couples par le moyen judiciaire de l'adultère masculin ou du concubinage aux xvie et premier xviie siècles, cet article mène une comparaison entre deux types de juridictions compétentes pour ce chef, l'une laïque et royale, l'autre ecclésiastique ; et deux types de procédures, l'une accusatoire et l'autre inquisitoire. Pour les premières, l'accusation d'adultère renvoie à plusieurs séries de manquements du mari qui sont d'ordre sexuels, génératifs, économiques et patrimoniaux mais aussi, moraux et politiques. Dans les secondes, la mise en défaut est d'ordre formaliste et de contrevenue à l'ordre sacramentel, à l'exclusion d'autres considérations. Les cas soumis à l'examen judiciaire montrent de leurs côtés des pratiques de couple non conformes aux normes officielles. Based on case studies centered on couples' requests for separation by the judicial means of male adultery or cohabitation in the sixteenth and early seventeenth centuries, this article compares two types of competent jurisdictions for this charge, one secular and royal, the other ecclesiastical ; and two types of procedures, one accusatory and the other inquisitorial. For the former, the accusation of adultery refers to several series of failures by the husband of a sexual, generative, economic or patrimonial order but also, moral or political. In the second case, the accusation is of a formal nature and a breach of the sacramental order, to the exclusion of other considerations. The cases submitted for judicial review show , the practices of couples that did not conform to official norms.

The article gives a brief historical description of the role of the court in the collection of evidence. The arguments in favor of strengthening the role of the court for the recovery of evidence are presented. In particular, the Author substantiates the conclusion that it is necessary to impose on the court the obligation to independently demand evidence in civil proceedings. The Author comes to the conclusion that strengthening the role of the court in the process of proof in civil proceedings are dictated by the requirements due to the many flaws in civil procedural law, the unequal social status of participants in the process, making decisions not in accordance with actual circumstances of the case.

We propose an online algorithm for sequential learning in the contextual multiarmed bandit setting. Our approach is to partition the context space and, then, optimally combine all of the possible mappings between the partition regions and the set of bandit arms in a data-driven manner. We show that in our approach, the best mapping is able to approximate the best arm selection policy to any desired degree under mild Lipschitz conditions. Therefore, we design our algorithm based on the optimal adaptive combination and asymptotically achieve the performance of the best mapping as well as the best arm selection policy. This optimality is also guaranteed to hold even in adversarial environments since we do not rely on any statistical assumptions regarding the contexts or the loss of the bandit arms. Moreover, we design an efficient implementation for our algorithm using various hierarchical partitioning structures, such as lexicographical or arbitrary position splitting and binary trees (BTs) (and several other partitioning examples). For instance, in the case of BT partitioning, the computational complexity is only log-linear in the number of regions in the finest partition. In conclusion, we provide significant performance improvements by introducing upper bounds (with respect to the best arm selection policy) that are mathematically proven to vanish in the average loss per round sense at a faster rate compared to the state of the art. Our experimental work extensively covers various scenarios ranging from bandit settings to multiclass classification with real and synthetic data. In these experiments, we show that our algorithm is highly superior to the state-of-the-art techniques while maintaining the introduced mathematical guarantees and a computationally decent scalability. IEEE

Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems., Horizon 2020 Framework Programme. Grant Number: 740920 Ministerio de Economía y Competitividad. Grant Number: MTM2014‐56949‐C3‐1‐R AXA Research Fund. Grant Number: AXA‐ICMAT Chair on Adversarial Risk Analysis European Cooperation in Science and Technology. Grant Number: IS1304

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In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, for the task of audio synthesis with Generative Adversarial Networks (GANs). We conduct the experiments on a subset of the NSynth dataset. The architecture follows the benchmark Progressive Growing Wasserstein GAN. We perform experiments both in a fully non-conditional manner as well as conditioning the network on the pitch information. We quantitatively evaluate the generated material utilizing standard metrics for assessing generative models, and compare training and sampling times. We show that complex-valued as well as the magnitude and Instantaneous Frequency of the Short-Time Fourier Transform achieve the best results, and yield fast generation and inversion times. The code for feature extraction, training and evaluating the model is available online., 5 pages, 1 figure, 5 tables, to be published in European Signal Processing Conference (EUSIPCO)

This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods.

Modern-day machine learning algorithms are vulnerable to adversarial perturbations. Before machine learning is employed in operational environments it is important for the Department of Defense to understand the vulnerabilities in adversarial domains. With an emphasis on applications to image classification, this thesis intends to investigate methods for constructing adversarial manipulations intended to fool a statistical classifier in order to establish best practices prior to employment in a mission-critical environment. The state-of-the-art method for generating adversarial perturbations utilizes an easy to obtain linear approximation of the defender’s loss function and applies change to each pixel’s value in the direction of the gradient by a pre-determined constant in order to maximize the defender’s loss function. This results in misclassification of the image. This thesis aims to improve existing methods for developing adversarial inputs to image classifiers by attempting to find new ways to make perturbations less perceptible to human vision. We develop two new perturbation methods. Color Aware attempts to constrain perturbation changes to match human perception by mapping to CIELAB color space, a color space better suited to represent human vision, while Color & Edge Aware constrains perturbation changes in visually smooth regions in images. Outstanding Thesis Captain, United States Marine Corps Approved for public release. distribution is unlimited

Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes to solving data-analysis problems. Industry are conducting their digital shifts, and AI is becoming a cornerstone technology for making decisions out of the huge amount of (sensors-based) data available in the production floor. However, such technology may be disappointing when deployed in real conditions. Despite good theoretical performances and high accuracy when trained and tested in isolation, a Machine-Learning (M-L) model may provide degraded performances in real conditions. One reason may be fragility in treating properly unexpected or perturbed data. The objective of the paper is therefore to study the robustness of seven M-L and Deep-Learning (D-L) algorithms, when classifying univariate time-series under perturbations. A systematic approach is proposed for artificially injecting perturbations in the data and for evaluating the robustness of the models. This approach focuses on two perturbations that are likely to happen during data collection. Our experimental study, conducted on twenty sensors’ datasets from the public University of California Riverside (UCR) repository, shows a great disparity of the models’ robustness under data quality degradation. Those results are used to analyse whether the impact of such robustness can be predictable—thanks to decision trees—which would prevent us from testing all perturbations scenarios. Our study shows that building such a predictor is not straightforward and suggests that such a systematic approach needs to be used for evaluating AI models’ robustness.

Рассматриваются отдельные аспекты состязательности в уголовном судопроизводстве как основополагающего принципа уголовного процесса, участия сторон в доказывании и оценки доказательств, наделении их определенными полномочиями на досудебной стадии уголовного процесса и законодательном закреплении этих полномочий, обеспечении охраны прав и законных интересов участников уголовного судопроизводства. Анализируется возможность законодательного совершенствования состязательности на досудебной стадии уголовного судопроизводства, освещается характер взаимоотношений обвинения и защиты при производстве расследования уголовного дела., Some aspects of the principle of competition in criminal proceedings, as a fundamental principle of the criminal process, participation of the parties in proving and evaluating evidence, granting them certain powers at the pre-trial stage of the criminal process and legislative consolidation of these powers, ensuring the protection of the rights and legitimate interests of participants in criminal proceedings areconsidered. The article analyzes the possibility of legislative improvement of competition at the pre-trial stage of criminal proceedings, the nature of the relationship between the prosecution and the defense during the investigation of a criminal case.

Open-source software is open to anyone by design, whether it is a community of developers, hackers or malicious users. Authors of open-source software typically hide their identity through nicknames and avatars. However, they have no protection against authorship attribution techniques that are able to create software author profiles just by analyzing software characteristics. In this paper we present an author imitation attack that allows to deceive current authorship attribution systems and mimic a coding style of a target developer. Withing this context we explore the potential of the existing attribution techniques to be deceived. Our results show that we are able to imitate the coding style of the developers based on the data collected from the popular source code repository, GitHub. To subvert author imitation attack, we propose a novel author obfuscation approach that allows us to hide the coding style of the author. Unlike existing obfuscation tools, this new obfuscation technique uses transformations that preserve code readability. We assess the effectiveness of our attacks on several datasets produced by actual developers from GitHub, and participants of the GoogleCodeJam competition. Throughout our experiments we show that the author hiding can be achieved by making sensible transformations which significantly reduce the likelihood of identifying the author's style to 0% by current authorship attribution systems.

Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data—a necessity for and pitfall of current supervised Deep Learning—and ii) theoretically allows to detect arbitrary, even rare pathologies which supervised approaches might fail to find. To date, the experimental design of most works hinders a valid comparison, because i) they are evaluated against different datasets and different pathologies, ii) use different image resolutions and iii) different model architectures with varying complexity. The intent of this work is to establish comparability among recent methods by utilizing a single architecture, a single resolution and the same dataset(s). Besides providing a ranking of the methods, we also try to answer questions like i) how many healthy training subjects are needed to model normality and ii) if the reviewed approaches are also sensitive to domain shift. Further, we identify open challenges and provide suggestions for future community efforts and research directions.

Agents that perform intelligent tasks interacting with humans in a seamless manner are becoming a reality. In contexts in which interactions among agents repeat over time, they might evolve from a cooperative to a competitive attitude, and vice versa, depending on environmental factors and other contextual circumstances. We provide a framework to model transitions between competition and cooperation in a community of agents. Competition is dealt with through the paradigm of adversarial risk analysis, which provides a disagreement solution; implicitly, we minimize the distance to such solution. Cooperation is handled through a concept of maximal separation from the disagreement solution. Mixtures of both problems are used to refer to in-between behaviour. We illustrate the ideas with several simulations in relation with a group of robots. Our motivation is the constitution of communities of robotic agents that interact among them and with one or more users., This research is supported by Singapore Ministry of Education Academic Research Fund Tier 2, MOE2016-T2-2-156.

В статье излагаются основные положения нового подхода к разделению уголовного процесса на типы. В основе авторского подхода лежит концепция эволюции уголовного процесса по двум направлениям — диалектическому и софистическому. Указываются недостатки состязательного типа уголовного процесса. Подчеркиваются преимущества уголовного процесса объективно-истинного типа., Main provisions of a new approach to the separation of the criminal process on types are expounded in the article. The concept of the evolution of the criminal process in two directions — dialectical and sophistical lies at the foundations of the author’s approach. The disadvantages of adversarial type of criminal procedure are indicated. The advantages of a criminal process of an objective-true type are underlined.

An increasingly popular branch of graph theory is the concept of community detection. However, the field of adversarial community detection currently has very little scientific literature. Initial tests suggest that an attacker in an adversarial community detection situation may not need specific information about a network as a whole in order to effectively mask a single community. In some cases, the attacker can achieve this with even random attacks on the network. However, some community detection algorithms are far more robust than others, and so the results of these tests vary greatly as a result.

DOI: http://dx.doi.org/10.22197/rbdpp.v1i1.7 Submitted by Rafaella Monterei (rcarine@stj.jus.br) on 2016-09-08T17:14:18Z No. of bitstreams: 2 debido_proceso_sistemas_deu.pdf: 762310 bytes, checksum: f72f52980b113a98e4846a7dd14107dd (MD5) license.txt: 1239 bytes, checksum: c9b4c351324448672315a00808efb725 (MD5) Approved for entry into archive by Roberta Marins (rmarins@stj.jus.br) on 2016-11-18T18:04:30Z (GMT) No. of bitstreams: 2 debido_proceso_sistemas_deu.pdf: 762310 bytes, checksum: f72f52980b113a98e4846a7dd14107dd (MD5) license.txt: 1239 bytes, checksum: c9b4c351324448672315a00808efb725 (MD5) Made available in DSpace on 2016-11-18T18:04:30Z (GMT). No. of bitstreams: 2 debido_proceso_sistemas_deu.pdf: 762310 bytes, checksum: f72f52980b113a98e4846a7dd14107dd (MD5) license.txt: 1239 bytes, checksum: c9b4c351324448672315a00808efb725 (MD5) Previous issue date: 2015 Examina e apresenta as características dos modelos processuais provindos de reformas em países Ibero-Americanos.

We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained during execution of one task has value for the execution of another task. Consequently, the agent is intrinsically motivated to explore its environment beyond the degree necessary to solve the current task it has at hand. We develop a decision theoretic setting that generalises standard reinforcement learning tasks and captures this intuition. More precisely, we consider a multi-stage stochastic game between a learning agent and an opponent. We posit that the setting is a good model for the problem of life-long learning in uncertain environments, where while resources must be spent learning about currently important tasks, there is also the need to allocate effort towards learning about aspects of the world which are not relevant at the moment. This is due to the fact that unpredictable future events may lead to a change of priorities for the decision maker. Thus, in some sense, the model "explains" the necessity of curiosity. Apart from introducing the general formalism, the paper provides algorithms. These are evaluated experimentally in some exemplary domains. In addition, performance bounds are proven for some cases of this problem., Comment: 14 pages, 4 figures

This paper aims to understand why common law countries have more developed financial markets than civil law countries. One difference between these two legal origins is the procedure of evidence collection for a trial: It is adversarial in common law and inquisitorial in civil law. The adversarial system delegates the collection of evidence to a larger extent to lawyers than the inquisitorial system does. The paper presents a model of law and finance in which investors use courts to enforce their financial contracts with entrepreneurs. Investors are willing to lend more if courts collect evidence more efficiently. Financial markets are more developed in the adversarial than in the inquisitorial system if investors are richer than entrepreneurs or if lawyers are more productive than judges. Manipulation of evidence by lawyers has an ambiguous impact on finance.

An arbiter can decide a case on the basis of his priors, or the two parties to the conflict may present further evidence. The parties may misrepresent evidence in their favor at a cost. At equilibrium the two parties never testify together. When the evidence is much in favor of one party, this party testifies. When the evidence is close to the prior mean, no party testifies. We compare this outcome under a purely adversarial procedure with the outcome under a purely inquisitorial procedure (Emons and Fluet 2009). We provide sufficient conditions on when one procedure is better than the other one.

An arbiter can decide a case on the basis of his priors or he can ask for further evidence from the two parties to the conflict. The parties may misrepresent evidence in their favor at a cost. The arbiter is concerned about accuracy and low procedural costs. When both parties testify, each of them distorts the evidence less than when they testify alone. When the fixed cost of testifying is low, the arbiter hears both, for intermediate values one, and for high values no party at all. The ability to commit to an adjudication scheme makes it more likely that the arbiter requires evidence