\u3cem\u3ePacta Sunt Servanda\u3c/em\u3e and Empire: A Critical Examination of the Evolution, Invocation, and Application of an International Law Axiom

In public international law, pacta sunt servanda is the foundational principle that international agreements are binding on treaty parties and must be kept. Insufficient attention, however, has been given to the role played by this international law axiom in organizing and shaping the international legal order. Accordingly, this note undertakes a critical historical analysis of how pacta sunt servanda was, and continues to be, applied as a legal basis and used as an argumentative method for the formation and maintenance of empire despite its conceptual evolution across time. Importantly, it does not argue that pacta sunt servanda should be abandoned as an international law rule or that pacta sunt servanda is not essential to the functioning of the international legal order. This note instead examines the conceptual evolution, invocation and application of pacta sunt servanda, and its relation to informal empire, across time

Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard the clients' data from disclosure during training and control data leakage through trained models when exposed to untrusted domains. Distributed differential privacy (DP) offers an appealing solution in this regard as it achieves a balanced tradeoff between privacy and utility without a trusted server. However, existing distributed DP mechanisms are impractical in the presence of client dropout, resulting in poor privacy guarantees or degraded training accuracy. In addition, these mechanisms suffer from severe efficiency issues. We present Dordis, a distributed differentially private FL framework that is highly efficient and resilient to client dropout. Specifically, we develop a novel `add-then-remove' scheme that enforces a required noise level precisely in each training round, even if some sampled clients drop out. This ensures that the privacy budget is utilized prudently, despite unpredictable client dynamics. To boost performance, Dordis operates as a distributed parallel architecture via encapsulating the communication and computation operations into stages. It automatically divides the global model aggregation into several chunk-aggregation tasks and pipelines them for optimal speedup. Large-scale deployment evaluations demonstrate that Dordis efficiently handles client dropout in various realistic FL scenarios, achieving the optimal privacy-utility tradeoff and accelerating training by up to 2.4$\times$ compared to existing solutions.Comment: This article has been accepted to ACM EuroSys '2

Synthesis of tributyl citrate using SO42-/Zr-MCM-41 as catalyst

Zirconium-containing mesoporous molecular sieve SO42-/Zr-MCM-41 was synthesized for catalyst in synthesis of tributyl citrate. The structure was characterized by XRD, N2 Ad/De isotherms and FT-IR. The results indicated that the solid acids show good catalytic performance and are reusable. Under optimum conditions and using SO42-/Zr-MCM-41 as catalyst, the conversion of citric acid was 95%. After easy separation of the products from the solid acid catalyst, it could be reused three times and gave a conversion of citric acid not less than 92%. The structure of tributyl citrate was characterized by FT-IR and 1H-NMR.KEY WORDS: Mesoporous molecular sieve, Tributyl citrate, Synthesis Bull. Chem. Soc. Ethiop. 2011, 25(1), 147-150

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

Tetragonal Mexican-Hat Dispersion and Switchable Half-Metal State with Multiple Anisotropic Weyl Fermions in Penta-Graphene

In past decades, the ever-expanding library of 2D carbon allotropes has yielded a broad range of exotic properties for the future carbon-based electronics. However, the known allotropes are all intrinsic nonmagnetic due to the paired valence electrons configuration. Based on the reported 2D carbon structure database and first-principles calculations, herein we demonstrate that inherent ferromagnetism can be obtained in the prominent allotrope, penta-graphene, which has an unique Mexican-hat valence band edge, giving rise to van Hove singularities and electronic instability. Induced by modest hole-doping, being achievable in electrolyte gate, the semiconducting pentagraphene can transform into different ferromagnetic half-metals with room temperature stability and switchable spin directions. In particular, multiple anisotropic Weyl states, including type-I and type-II Weyl cones and hybrid quasi Weyl nodal loop, can be found in a sizable energy window of spin-down half-metal under proper strains. These findings not only identify a promising carbon allotrope to obtain the inherent magnetism for carbon-based spintronic devices, but highlight the possibility to realize different Weyl states by combining the electronic and mechanical means as well