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In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learning user preferences. We specifically explore the use of ILASP (Inductive Learning of Answer Set Programs) to approximate preference learning systems through weak constraints. We have created a dataset on user preferences over a set of recipes, which is used to train the NNs that we aim to approximate with ILASP. Our experiments investigate […]

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This position paper argues that remote-capable knowledge work should default to AI-enabled flexibility because the workflow-integrated foundation-model stack changes the coordination economics that once favored daily co-presence. By foundation-model stack, we mean systems that combine natural-language interaction, multimodal capture, long context, retrieval, transcription, translation, and increasingly bounded tool use inside everyday workflows. Their organizational significance is not generic automation but the accumulation of artifact capital: durable, queryable, reusable traces such as transcripts, summaries, decisions, tickets, code comments, and […]

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The numerical simulation of incompressible viscous flows remains a central pillar of modern computational fluid dynamics (CFD). Over the past decades, a wide spectrum of numerical methodologies has been developed, reflecting fundamentally different mathematical formulations and discretization philosophies. Among these, domain-based approaches—such as finite difference, finite element, finite volume, and meshfree methods—have emerged as versatile and general-purpose frameworks, while boundary element methods provide efficient alternatives for problems governed by linear physics, particularly in unbounded domains. This review presents […]

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This paper presents CBM-Dual, the first silicon-proven digital chaotic dynamics processor (CDP) supporting both simulated annealing (SA) and reservoir computing (RC). CBM-Dual enables real-time decision-making and lightweight adaptation for autonomous Edge AI, employing the largest-scale fully connected 1024-neuron chaotic Boltzmann machine (CBM). To address the high computational and area costs of digital CDPs, we propose: 1) a CBM-specific scheduler that exploits an inherently low neuron flip rate to reduce multiply-accumulate operations by 99%, and 2) an efficient multiply […]

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World models aim to enable agents to perceive states, predict future outcomes, and reason for decision-making by simulating real-world environments, and are widely regarded as a crucial pathway toward artificial general intelligence (AGI). Video, as one of the most accessible and intuitively representative media of dynamic environments, naturally contains rich implicit representations of the physical world. Consequently, learning world models from videos has become a prominent research direction. However, a significant gap remains between video data and the […]

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Quantum key distribution (QKD) networks require relaying when distant key management entities share no direct quantum link. Most relay strategies, however, rely on centralized control or globally maintained routing state. This paper asks whether useful security and efficiency can still be obtained with topology-oblivious stochastic forwarding. It studies the security-overhead trade-off in a model in which fragmented key material is relayed via random-walk variants and reconstructed under privacy amplification. Under a restricted model with at most one compromised […]

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:::info Design, implementation, and benchmarks of a native BM25 index for Postgres. Now generally available to all Tiger Cloud customers and freely available via open source. ::: If you have used Postgres’s built-in ts_rank for full-text search at any meaningful scale, you already know the limitations. Ranking quality degrades as your corpus grows. There is no inverse document frequency, so common words carry the same weight as rare ones. There is no term frequency saturation, so a document that mentions […]

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TLS 1.3 zero-round-trip-time (0-RTT) resumption reduces reconnection latency by allowing clients to transmit early application data using pre-shared keys (PSK) derived from previously established session tickets. This mechanism is pivotal for latency-sensitive web services, API gateways, and IoT applications. However, the cryptographic foundations of current session tickets—symmetric keys derived from classical X25519 key exchange—are fundamentally vulnerable to Harvest-Now-Decrypt-Later (HNDL) quantum attacks: an adversary capturing session ticket exchanges today can retroactively decrypt PSKs and all 0-RTT early data once […]

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