Moralität und Gerechtigkeit. Politische Gerechtigkeit als die Sittlichkeit im Zusammenleben der Personen in Kants praktischer Philosophie

Ziel dieser Arbeit ist es, das Verhältnis zwischen Recht und Moral im Denken Kants, insbesondere ihre auf dem Begriff der Sittlichkeit beruhende Gemeinsamkeit, zu erläutern. Mit anderen Worten, insofern es sich bei hierbei um ein Verhältnis handelt, das im negativen Sinn den Unterschied und im positiven Sinn die Gemeinsamkeit zwischen beiden bezeichnet, liefert die Untersuchung des Verhältnisses zwischen Recht und Moral zunächst eine Antwort auf die Frage, ob es zwischen den beiden eine Verbindung gibt, und falls ja, auf welche Weise, d.h. in welchem Sinne, sie miteinander verknüpft sind. Während der erste Abschnitt darauf fokussiert, die Möglichkeit einer Verbindung zwischen Recht und Moral zu finden, liegt der Schwerpunkt des zweiten Abschnitts darauf, beider Verhältnis, d.h. sowohl den Unterschied zwischen beiden als auch ihre Gemeinsamkeit, in Kants Denken näher zu erläutern. Verglichen mit dem im ersten Abschnitt unternommenen Versuch, eine Verbindung herauszuarbeiten, ist es die Aufgabe des zweiten Abschnitts, einen Vergleich vorzunehmen. Dabei ziele ich letztlich darauf, zu beleuchten, in welchem Sinne Kants Rechts- und Staatsphilosophie als eine Rechts- und Staatsethik bezeichnet werden kann. Der dritte Abschnitt dieser Arbeit ist eine kasuistische Untersuchung im Sinne Kants. In diesem Abschnitt vollziehe ich auf das Ergebnis des zweiten Abschnitts hin für die folgenden vier Themen eine kasuistische Übung: (1) Rechtsstaat, (2) direkte Demokratie, (3) Widerstandsrecht und (4) Todesstrafe. Mit anderen Worten, ich versuche, eine Richtung zu weisen, in der sich für sie eine Lösung entsprechend Kants Moral- und Rechtsgedanken finden lässt. Die jeweilige Diskussion setzt sich aus zwei Teilen zusammen: Zunächst führe ich Kants Ansichten aus, sodann die heutigen. Indem ich beide Positionen erörtere, versuche ich zu klären, inwieweit Kants Gedanken für diese vier Themen Einfluss auch auf die heutigen Ansichten haben können, und, im Falle eines Konflikts zwischen den beiden, ob sie sich miteinander versöhnen lassen

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search(NAS) for searching efficient building blocks of neural architectures. Our method is node-based and thus can learn flexible network patterns in cell structures within a topological search space. Directed Acyclic Graphs (DAGs) are used to abstract DNN architectures and progressively optimize the cell structure through edge shrinking. As the search space intrinsically reduces as the edges are progressively shrunk, AutoShrink explores more flexible search space with even less search time. We evaluate AutoShrink on image classification and language tasks by crafting ShrinkCNN and ShrinkRNN models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34% Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1.5 GPU hours, which is 7.2x and 6.7x faster than the crafting time of SOTA CNN and RNN models, respectively

LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning

Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, we propose a new learning algorithm LEASGD (Leader-Follower Elastic Averaging Stochastic Gradient Descent), which is driven by a novel Leader-Follower topology and a differential privacy model.We provide a theoretical analysis of the convergence rate and the trade-off between the performance and privacy in the private setting.The experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving steadily lower loss within the same iterations and by reducing the communication cost by 30%. In addition, LEASGD spends less differential privacy budget and has higher final accuracy result than DPSGD under private setting

Implementing a Cooking and Dietary Management System Using RFID Technology

The establishment of a database of nutritional ingredients with electronic scales feature provides chefs with conducted cooking; as long as the food category is selected and weighed by order, the nutritional content of the dish will be known. By RFID electronic scales and electronic plate function, the meal intake of nutrients and measurement of food nutrients per meal can reach a daily diet control purpose. The so-called RFID electronic plate is embedded into the chassis and aplate composed of a plurality of dishes to detect information in the chassis and the various subdishes through RFID reader. The chassis provides users with automatic identification such as physiological signals and doctor’s prescription as an individual dietary recommendation through dietary database. The dietary database, in addition to providing essential food nutrients, can be used to query using keywords and classification methods so as to quickly find the sum ingredients for cooking and ingredients. The chefs instantly know the meals nutrition during ingredients weighing by using RFID electronic scales. End users only need to place the allocated cooking dishes on electronic scales; they can easily know the total own meal intake nutrition