\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

Dual-Path Coupled Image Deraining Network via Spatial-Frequency Interaction

Transformers have recently emerged as a significant force in the field of image deraining. Existing image deraining methods utilize extensive research on self-attention. Though showcasing impressive results, they tend to neglect critical frequency information, as self-attention is generally less adept at capturing high-frequency details. To overcome this shortcoming, we have developed an innovative Dual-Path Coupled Deraining Network (DPCNet) that integrates information from both spatial and frequency domains through Spatial Feature Extraction Block (SFEBlock) and Frequency Feature Extraction Block (FFEBlock). We have further introduced an effective Adaptive Fusion Module (AFM) for the dual-path feature aggregation. Extensive experiments on six public deraining benchmarks and downstream vision tasks have demonstrated that our proposed method not only outperforms the existing state-of-the-art deraining method but also achieves visually pleasuring results with excellent robustness on downstream vision tasks

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

Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning

In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest majority among participants to withstand various attacks. In practice, however, servers are not always trusted, and an adversarial server can strategically select compromised clients to create a dishonest majority, thereby undermining the system's security guarantees. In this paper, we present Lotto, an FL system that addresses this fundamental, yet underexplored issue by providing secure participant selection against an adversarial server. Lotto supports two selection algorithms: random and informed. To ensure random selection without a trusted server, Lotto enables each client to autonomously determine their participation using verifiable randomness. For informed selection, which is more vulnerable to manipulation, Lotto approximates the algorithm by employing random selection within a refined client pool. Our theoretical analysis shows that Lotto effectively aligns the proportion of server-selected compromised participants with the base rate of dishonest clients in the population. Large-scale experiments further reveal that Lotto achieves time-to-accuracy performance comparable to that of insecure selection methods, indicating a low computational overhead for secure selection.Comment: This article has been accepted to USENIX Security '2

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