Abstract

Editing articulated objects within a single image requires precise kinematic control--specifying exactly what moves, along which axis, and by how much. Existing text- and drag-based image editing methods provide only weak or ambiguous control over articulated object structure, often failing to localize the correct movable part or execute precise articulation extents, particularly under occlusion. We propose RigIT, a novel framework that enables precise object articulation by directly "rigging" parts within a 2D image. Our method extracts the underlying geometry of a user-selected part to fit a 3D kinematic proxy, represented as an oriented bounding box (OBB). Users interactively manipulate this explicit 3D handle, which the system projects into a 2D wireframe conditioning signal. A fine-tuned diffusion model then processes this signal, guided by a localized articulation-aware loss, to generate photorealistic edits. Remarkably, despite being trained exclusively on a small synthetic dataset of six categories, RigIT generalizes robustly to real-world, in-the-wild images and unseen object classes. Extensive evaluations on both synthetic and real-world benchmarks demonstrate that our approach significantly outperforms state-of-the-art text and drag-based baselines, achieving over 90% user preference for preserving articulation intent and scene consistency. All code, trained models and dataset will be publicly released at the https://rigit-project.github.io/ .

Abstract

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Abstract

A distributed storage system with graph-based replication consists of a collection of databases and the files they contain. The databases (or servers) are represented as the vertices of a graph, while each file is stored in a distinct pair of servers and is represented by an edge of this graph. Private information retrieval (G-PIR) on such a graph-based storage system involves a client which seeks to retrieve a desired file via a query-response protocol, without leaking the identity of
the desired file index to any database. The goal of G-PIR is to maximize the rate (reciprocal of the total normalized download) under the privacy constraint. Prior work on GPIR has involved perfect information-theoretic privacy (i.e., null leakage). However, if the privacy constraint is relaxed, then PIR protocols could be designed that have even higher rates. We
term such protocols as Graph-based Weak Private Information Retrieval (G-WPIR) protocols and initiate their formal study in this work. We propose a G-WPIR scheme for arbitrary graphs, and identify the trade-offs it achieves between rate and privacy, under two well known leakage metrics: mutual information leakage and maximal leakage. Our protocol employs minimal subpacketization (representing a file-size constraint) and employs a simple probabilistic query realization to obtain the smooth trade-off. We extend this protocol with some modifications to two special classes of graphs, the complete graphs and the complete
bipartite graphs, and identify the corresponding rate-privacy trade-offs achieved.

Abstract

While traditional stable matching algorithms, such as the Gale-Shapley algorithm, prioritize stability, they may fall short of achieving equitable outcomes among participants. We study the role of emph{Nash social welfare} (NSW) as a fairness objective in the classic emph{stable marriage problem}. We develop texttt{SNSW-Alg} that finds a stable matching that maximizes Nash social welfare under rank-induced utilities in $tilde{mathcal{O}}(n^4)$ time, where $n$ is the number of men or women. We demonstrate that texttt{SNSW-Alg} balances equity while preserving stability. We empirically evaluate our methods across diverse preference distributions, demonstrating significant gains in fairness without substantial losses in other key measures such as regret, egalitarian criterion, and sex equality. Our findings suggest that the stable matching produced by texttt{SNSW-Alg} is statistically Pareto-undominated by stable matchings based on other fairness measures - regret, egalitarian, and sex equality. This study offers compelling insights into designing fair stable matching.

Abstract

Diffusion language models denoise a masked canvas, making them a natural setting for selective generation decisions such as answering, waiting, clarifying, or refusing. We introduce TAP-DiLM, which adds a typed policy variable to masked diffusion LMs, and study how this policy should be attached to the denoising process. Across BERT-base, MDLMOWT, LLaDA-8B, and Dream-7B, we compare shared-vocabulary slots, dedicated slot heads, and CLS-based external heads. The
results reveal a clear interface-backbone interaction: dedicated heads provide robust policy learning across scales, while sharedvocabulary heads can be effective when the
LM-head geometry is favorable. A simple LMhead sharpness probe predicts when sharedhead policy internalization is reliable, giving a practical diagnostic for interface selection.
Causal clamp and slot-injection tests show that dedicated policy slots can influence body denoising through feedback, especially at 8B scale. Safety-refusal experiments further show
that TAP-DiLM supports competitive typed refusal behavior when trained with diverse unsafe prompts.

Abstract

The growing significance of data-intensive technologies has positioned data as a critical geopolitical asset, prompting the emergence of data diplomacy as an increasingly important domain in contemporary foreign policy. This article examines the strategic relevance of data diplomacy in India's foreign policy through a qualitative analysis of policy documents, legislative frameworks and international engagements in the digital domain. It examines data diplomacy as a mutually re-enforcing process in which domestic data governance and international digital strategy are interrelated. The article analyses how domestic regulatory choices shape India's global negotiating positions while geopolitical, technological and economic imperatives simultaneously influence the evolution of domestic data governance. By locating areas of cooperation and contestation, the article demonstrates how data diplomacy is emerging as a crucial mechanism in India's digital strategic outlook through which it seeks to reconcile multiple objectives such as developmental priorities, digital sovereignty, economic competitiveness and strategic autonomy within an evolving global digital order.

Abstract

As data flows across borders become increasingly critical, governments are faced with the challenge
of governing these flows while maximizing their economic and strategic benefits. This article applies
dependency theory (Dos Santos, 1970, The American Economic Review, 60, 231-236; Wallerstein,
1974, The modern world-system i: Capitalist agriculture and the origins of the European world-economy in the
sixteenth century, University of California Press) as an analytical framework, examining how data
flows and governance systems create and reinforce dependent relationships between countries.
This framework proposes three categories of actors in the global economy: the core, periphery and
semi-periphery. Originally developed to analyse colonial and post-colonial economic relationships, this
theory can be used to examine how contemporary data governance may perpetuate global inequali-
ties through new mechanisms. In this article, we extend the dependency theory framework to the
global data economy, by analysing the evolving characteristics of the participating actors. Countries that
control key technological infrastructure, set global standards and capture the majority of economic
value from data flows can be characterized as the 'core'. Countries which serve as data sources, with
limited domestic technological capabilities and minimal value capture from their own data generation,
would be considered the 'periphery'. On the other hand, countries that are able to maintain a strate-
gic and dynamic trade and regulatory positioning, can exhibit selective participation and have growing
indigenous technological capabilities would form the 'semi-periphery'. Through this article, we seek to
apply dependency theory to analyse the global data economy and to situate India within it, highlighting
its characteristics as an actor in the semi-periphery.

Abstract

Identity-aware SIEM alerting compares activity with expected role context such as locations, managed devices, schedules, and entitlements. Legitimate organizational change makes that context stale, while unconstrained adaptation can learn attacker-controlled context as normal. We study who may mutate this persistent security state. CHANGEGUARD treats statistical drift and an exact governance record as evidence for a candidate, rather than permission to update the live baseline. Their intersection creates a role-scoped shadow; promotion additionally requires measured alert benefit, evidence from several principals, an attack-evidence veto, and bounded regression on frozen known attack families. On 467030 controlled synthetic windows from 12 fresh held-out profiles and 3 replays, CHANGEGUARD reduces post-change false-positive rate from 6.44% to 2.55%. Its absolute later-exploit recall is 48.27%, retaining 98.59% of the detections available to the frozen detector; persistent drift reaches a similar 2.59% false-positive rate but only 25.62% recall. Under mixed governance faults, drift-plus-record makes 26 over-broad mutations and CHANGEGUARD makes none. Applying the same frozen model and controller to a separately implemented 1356831-window process generator reproduces both findings. White-box stress also identifies the information boundary: coordinated attacks that reproduce every trusted input can pass. These results establish controlled mechanism behavior rather than production-SOC performance.

Abstract

An identity-related security alert can retain a low risk score even when the records needed to justify deprioritizing it are missing. CAW-Triage separates risk scoring, bounded waiting for telemetry, and permission to assign low priority. A versioned contract specifies required observations, completeness, and score/support conditions. Predicted recovery cannot substitute for observation. We evaluate 12,480 synthetic alert windows from twelve parameterizations of one scenario generator, with twenty degraded-telemetry replays per profile. At identical action counts, unsafe downgrade falls from 4.85% for a 15-minute ranker to 0.97%, a 3.88 percentage-point reduction (paired crossed-bootstrap 95% interval 3.08-4.68). High and review together account for 47.21% of alerts; review alone accounts for 26.89%. Fixed-window contracts achieve lower unsafe downgrade but higher load; recovery sensitivity exposes the additional delay caused by optimistic health estimates. A learned selector also achieves lower label-based error, but violates the declared contract on 23.0% of its low assignments. The results support conditional enforcement with measured trade-offs, not universal predictive superiority or production safety. Independent policy validation and enterprise telemetry remain necessary.

Abstract

Traditional public-key cryptographic schemes are primarily based on the computational hardness of problems such as the "Integer Factorization Problem (IFP)" and the "Elliptic Curve Discrete Logarithm Problem (ECDLP)". However, with the advent of large-scale quantum computing, these problems can be solved efficiently, thereby compromising the security of conventional cryptographic systems. Lattice-based cryptography has emerged as a promising solution due to its strong resistance to quantum attacks. In this work, we propose a post-quantum secure access control scheme based on lattice cryptography for IoMT-enabled healthcare environments. The scheme ensures that only authenticated and integrity-protected medical data are recorded in a private blockchain maintained collaboratively by multiple healthcare institutions. The proposed framework not only achieves strong security against both classical and quantum attacks but also maintains computational efficiency when compared to existing literature solutions.