Abstract

Graph Neural Network (GNN) research is rapidly advancing due to GNNs’ capacity to learn distributed representations from graph-structured data. However, centralizing large volumes of real-world graph data for GNN training is often impractical due to privacy concerns, regulatory restrictions, and commercial competition. Federated learning (FL), a distributed learning paradigm, offers a solution by preserving data privacy with collaborative model training. Despite progress in training huge vision and language models, federated learning for GNNs remains underexplored. To address this challenge, we present a novel method for federated learning on GNNs based on spectral GNNs equipped with neural ordinary differential equations (ODE) for better information capture, showing promising results across both homophilic and heterophilic graphs. Our approach effectively handles non-Independent and Identically Distributed (non-IID) data, while also achieving performance comparable to existing methods that only operate on IID data. It is designed to be privacy-preserving and bandwidth-optimized, making it suitable for real-world applications such as social network analysis, recommendation systems, and fraud detection, which often involve complex, non-IID, and heterophilic graph structures. Our results in the area of federated learning on non-IID heterophilic graphs demonstrate significant improvements while also achieving better performance on homophilic graphs. This work highlights the potential of federated learning in diverse and challenging graph settings.

Abstract

Mann ki Baat is a series of speeches made by the 14th Prime Minister of India, Narendra Modi addressing the nation. Mann ki Baat holds significant sway over the Indian population as it is broadcast directly by the Prime Minister. As such, it is possible for this stream of speeches to carry hidden political intent or to act as a symbol to show that the government is in touch with the people. For this purpose, the English transcripts of 84 speeches from this series starting from October 2014 to March 2022 were analysed. For an overarching general analysis, Natural Language Processing tools were employed and later on, the speeches were juxtaposed with important events that have occurred in the country to investigate the responses as shown in Mann ki Baat. Moreover, 25 speeches were analysed individually in connection with 3 major events, each spanning a course of multiple months to validate the observations found through the employment of NLP tools and add to them. Observations have shown that Mann ki Baat does indeed carry political intent and works to better the image the people have of the Prime Minister and the ruling government, even at the cost of bending facts at times. However, it has also come to light that Mann ki Baat also works as a highly efficient means of spreading general awareness such as the instances observed during the Covid pandemic. Keywords: Mann ki Baat, NLP, Textual Analysis, Indian Politics

Abstract

The possibility of a quantum system to exhibit properties that are akin to both the classically held notions of being a particle and a wave, is one of the most intriguing aspects of the quantum description of nature. These aspects have been instrumental in understanding paradigmatic natural phenomena as well as to provide non-classical applications. A conceptual foundation for the wave nature of a quantum state has recently been presented, through the notion of quantum coherence. We introduce here a parallel notion for the particle nature of a quantum state of an arbitrary physical system. We provide elements of a resource theory of particleness, and give a quantification of the same. Finally, we provide evidence for a complementarity between the particleness thus introduced, and the coherence of an arbitrary quantum state.

Abstract

The fully entangled fraction (FEF) measures the proximity of a quantum state to maximally entangled states. FEF , in systems, is a significant benchmark for various quantum information processing protocols including teleportation. Quantum conditional entropy (QCE) on the other hand is a measure of correlation in quantum systems. Conditional entropies for quantum systems can be negative, marking a departure from conventional classical systems. The negativity of quantum conditional entropies plays a decisive role in tasks like state merging and dense coding. In the present work, we investigate the relation of these two important yardsticks. Our probe is mainly done in the ambit of states with maximally mixed marginals, with a few illustrations from other classes of quantum states. We start our study in two-qubit systems, where for the Werner states, we obtain lower bounds to its FEF when the conditional Rényi entropy is negative. We then obtain relations between FEF and QCE for two-qubit Weyl states. Moving on to two qudit states, we find a necessary and sufficient condition based on FEF, for the isotropic state to have negative conditional entropy. In two qudit systems, the relation between FEF and QCE is probed for the rank-deficient and generalized Bell diagonal states. FEF is intricately linked with k-copy nonlocality and k- copy steerability. The relations between FEF and QCE facilitates to find conditions for k- copy nonlocality and k- copy steerability based on QCE. We obtain such conditions for certain classes of states in two qubits and two qudits. Applications of the relations obtained are provided in the context of work extraction, faithful entanglement and entropic uncertainty relations.

Abstract

Linearly Transformed Spherical Distributions (LTSDs), a superset of the commonly known Linearly Transformed Cosines (LTCs) for analytic area light rendering, applied in the context of analytic area light rendering with participating media.

Abstract

Effective decision-making in complex environments with multi-discrete action spaces poses significant challenges for agent architectures, particularly in image-based settings. While Decision Transformers have shown promise in various domains, their performance often suffers in environments where agents must handle multi-discrete actions. Existing enhancements to Decision Transformer architectures have yet to address this critical issue, limiting their ability to support agents in learning robust policies in these environments. To address this gap, we propose Multi-State Action Tokenisation (M-SAT), a novel approach designed to improve agent decision-making by tokenising actions at the individual action level and incorporating auxiliary state information. This disentanglement of actions improves both the performance of agents and the interpretability of individual actions within attention layers, fostering better visibility into agent decision processes. Importantly, M-SAT facilitates the development of more interpretable and transparent agents capable of making complex decisions in dynamic environments involving multi-discrete action spaces. We evaluate M-SAT on the challenging ViZDoom environments, focusing on scenarios with multi-discrete action spaces and image-based observations, such as Deadly Corridor, My Way Home and Death Match. Our approach demonstrates superior performance compared to baseline Decision Transformers, with no additional data or significant computational overheads. Furthermore, we observe that M-SAT does not require positional encoding to achieve high performance, with its removal occasionally leading to further improvements. These findings suggest that M-SAT enables more efficient and interpretable agent-based decision-making in multi-discrete action spaces.

Abstract

Time-series data from sensor networks often has missing entries due to sensor failures, poor communication, scheduled downtime, etc. Missing data imputation improves perfor- mance of downstream tasks such as forecasting and anomaly detection. Therefore, highly accurate imputation methods are desirable. Multi-variate time series imputation methods using interpolation [1–3], or matrix factor- ization [4, 5] have limited accuracy. Auto-regressive methods like ARIMA [6] and RNNs like GRIN [7] are subject to error propagation and are slow. More recently, attention [8] has been successfully used to design effective networks for various time series tasks – imputation (SPIN, SPIN-H and Transformer) [9], forecasting [10, 11] and anomaly detection [12]. However, as evident from Figure 2, these state-of-the-art (SOTA) networks are effective only on certain datasets. It motivates a more general meta-network which can effectively aggregate these experts using a gating network. We provide the scope of improvement when these frozen experts are selected optimally using an oracle. The possible reduction in error can be as high as 28 − 40 percent by using existing imputation networks.

Abstract

The study of information revivals, witnessing the violation of certain data-processing inequalities, has provided an important paradigm in the study of non-Markovian quantum stochastic processes. Although often used interchangeably, we argue here that the notions of “revivals” and “backflows,” i.e., flows of information from the environment back into the system, are distinct: an information revival can occur without any backflow ever taking place. In this paper, we examine in detail the phenomenon of noncausal revivals and relate them to the theory of short Markov chains and squashed non-Markovianity. We also provide an operational condition, in terms of system-only degrees of freedom, to witness the presence of genuine backflow that cannot be explained by noncausal revivals. As a byproduct, we demonstrate that focusing on processes with genuine backflows, while excluding those with only noncausal revivals, resolves the issue of nonconvexity of Markovianity, thus enabling the construction of a convex resource theory of genuine quantum non-Markovianity

Abstract

An influential mathematical model of memory, the temporal context model (TCM), posits that we encode items and their associations with temporal context (Howard & Kahana, 2002). Temporal context is conceived of as a recency-weighted average of past experiences. Critically, the model assumes that when an item is retrieved later, the associated temporal context is also obligatorily retrieved. Existing evidence for the idea of retrieved temporal context primarily comes from free-recall studies. However, free recall introduces some critical confounds that are difficult to resolve (Folkerts et al., 2018) and also encourages memory strategies that may mimic temporal context effects (Hintzman, 2011). Schwartz et al. (2005) showed that temporal context influences image recognition within short experimental lists. We extend this by using the Natural Scenes Dataset (NSD) to demonstrate that reinstating temporal context enhances recognition accuracy even across long timescales. We demonstrate that when the relevant temporal context is successfully reinstated for a reference image, the recognition accuracy of subsequent images increases. Critically, we show that this influence falls off with temporal distance at encoding from the reference image only when the temporal context is successfully retrieved, as predicted by TCM. Furthermore, the slope of this temporal gradient increases as a function of the strength of the influence of the retrieved temporal context. These findings extend our understanding of temporal context effects in episodic memory by showing that temporal context is retrieved even in tasks that do not encourage linking between items as a memory strategy.

Abstract

There is a high prevalence of disinformation and manipulative narratives in online new sources today, and verification of its informative integrity is a vital need as online audience is highly susceptible to being affected by such propaganda or disinformation. The task of verifying any online information is, however, a significant challenge. The task Multilingual Characterization and Extraction of Narratives from Online News, therefore focuses on developing novel methods of analyzing news ecosystems and detecting manipulation attempts to address this challenge. As a part of this effort, we focus on the subtask of Entity Framing, which involves assigning named entities in news articles one of three main roles ( Protagonist, Antagonist, and Innocent) with a further fine-grained role distinction. We propose a pipeline that involves summarizing the article with the summary being centered around the entity. The entity and its entity-centric summary is then used as input for a BERT-based classifier to carry out the final role classification. Finally, we experiment with different approaches in the steps of the pipeline and compare the results obtained by them.