NTT Data

Sequential recommender systems must model long-range user behavior while operating under strict memory and latency constraints. Transformer-based approaches achieve strong accuracy but suffer from quadratic attention complexity, forcing aggressive truncation of user histories and limiting their practicality for long-horizon modeling. This paper presents HoloMambaRec, a lightweight sequential recommendation architecture that combines holographic reduced representations for attribute-aware embedding with a selective state space encoder for linear-time sequence processing. Item and attribute information are bound using circular convolution, preserving embedding dimensionality while encoding structured metadata. A shallow selective state space backbone, inspired by recent Mamba-style models, enables efficient training and constant-time recurrent inference. Experiments on Amazon Beauty and MovieLens-1M datasets demonstrate that HoloMambaRec consistently outperforms SASRec and achieves competitive performance with GRU4Rec under a constrained 10-epoch training budget, while maintaining substantially lower memory complexity. The design further incorporates forward-compatible mechanisms for temporal bundling and inference-time compression, positioning HoloMambaRec as a practical and extensible alternative for scalable, metadata-aware sequential recommendation.

Measurement-Based Quantum Computing (MBQC) is inherently well-suited for Distributed Quantum Computing (DQC): once a resource state is prepared and distributed across a network of quantum nodes, computation proceeds through local measurements coordinated by classical communication. However, since non-local gates acting on different Quantum Processing Units (QPUs) are a bottleneck, it is crucial to optimize the qubit assignment to minimize inter-node entanglement of the shared resource. For graph state resources shared across two QPUs, this task reduces to finding bipartitions with minimal cut rank. We introduce a simulated annealing-based algorithm that efficiently updates the cut rank when two vertices swap sides across a bipartition, such that computing the new cut rank from scratch, which would be much more expensive, is not necessary. We show that the approach is highly effective for determining qubit assignments in distributed MBQC by testing it on grid graphs and the measurement-based Quantum Approximate Optimization Algorithm (QAOA).

The rapid proliferation of edge devices, cyber-physical systems, autonomous platforms, and large-scale IoT infrastructures has fundamentally transformed how intelligence is computed and deployed. Traditional centralized cloud-based AI architectures are increasingly limited by latency, bandwidth, privacy, energy efficiency, and reliability constraints. As a result, distributed intelligence—where sensing, learning, inference, and decision-making are performed collaboratively across networked nodes—has emerged as a critical paradigm shift. While significant progress is made in creating smart AI algorithms and systems that can learn from each other, but there's still a big hole when it comes to the underlying technology that makes it all work. Basically, the circuits and systems that are developed aren't really built for this kind of distributed intelligence, where lots of devices are working together and sharing information in real-time. Most of the current hardware is just adapted from old centralized computing systems, which aren't ideal for collaborative, on-device, and federated intelligence. Hence there is a need for major innovations at the circuit and system level to make distributed intelligence scalable, secure, energy-efficient, and fast.

Geographic access to healthcare remains a critical barrier to health equity in low- and middle-income countries, where infrastructure and service provision are unevenly distributed. Using the UN-endorsed Degree of Urbanisation framework, we assessed disparities in geographic healthcare accessibility across Nigeria and Zambia in 2020. Travel times to health facilities were modeled for both walking and motorized transport at 1 km resolution, and stratified by settlement type and demographic group. Results showed marked urban–rural disparities: while city residents could typically reach hospitals within minutes, rural populations faced journeys exceeding 4 h on foot. Motorized transport substantially improved accessibility but remained unavailable to many, leaving only 8

The shift towards sustainable agriculture has become increasingly necessary due to the increasing demand for food globally, environmental degradation, and the shortage of labor. Robotics, Artificial Intelligence (AI), Internet of Things (IoT), and data-driven technologies are a new category of technologies that have become central to agricultural transformation through automation. The review paper presents a synthesis of the world's innovations in agricultural automation over the last five years, with a specific focus on the development of India and the peculiarities of its situation. Even though the world has made tremendous steps, the major research gap is in the application of automation technologies in various agroecological, socio-economic, and policy settings, especially in emerging economies such as India. This review takes a multidimensional approach through a systematic review of the scholarly literature, government reports, and industrial case studies as a means of assessing technological advancements, adoption forces, economic feasibility, environmental assessment, and socio-political preparedness. According to the findings, developing countries are crippled by factors such as high prices, poor infrastructure, and poor policy incentives, when compared to developed countries that have been keen to automate to achieve the full utilisation of resources and crop yields. Precision agriculture, drone uses and autonomous machines are pilot projects in India, but remain fragmented. The review concludes that in order to make sure that automation generates sustainable agricultural change in India, a local, inclusive strategy has to be developed in terms of attention to scalable innovation, capacity building, and well-built institutional frameworks.