AI Insights

• The initial configuration had some limitations, such as precomputed metrics for the search template bank and no prior applied to center the first iteration bank. (ML: 0.86)👍👎
• The MBTA SNR optimizer has undergone several configuration changes since its online deployment on June 13, 2025, and has achieved improved performance with the final configuration. (ML: 0.65)👍👎
• The MBTA SNR optimizer has been used in several studies, including the O4c implementation, which showed improved performance compared to the initial configuration. (ML: 0.65)👍👎
• The MBTA SNR optimizer has been operating online since June 13, 2025, and has undergone several configuration changes. (ML: 0.65)👍👎
• SNR: Signal-to-noise ratio P–P plot: Plot of the posterior distribution against the prior distribution ROC curve: Receiver operating characteristic curve HasMassGap: Property indicating whether a binary system has a mass gap HasSSM: Property indicating whether a binary system is a supernova-supernova merger The MBTA SNR optimizer has shown improved performance with the final configuration, achieving better estimation of source parameters and more accurate classification. (ML: 0.57)👍👎

Abstract
In this paper, we describe the procedure implemented in the Multi-Band Template Analysis (MBTA) search pipeline to produce online posterior distributions of compact binary coalescence (CBC) gravitational-wave parameters. This procedure relies on an SNR optimizer technique, which consists of filtering dense local template banks. We present how these banks are constructed using information from the initial detection and detail how the results of the filtering are used to estimate source parameters and provide posterior distributions. We demonstrate the performance of our procedure on simulations and compare our source parameter estimates with the results from the first part of the fourth observing run (O4a) recently released by the LIGO-Virgo-KAGRA (LVK) collaboration.

Why we are recommending this paper?
Due to your Interest in Low latency

This paper directly addresses low-latency requirements through its focus on an SNR optimizer technique for CBC parameter estimation. The MBTA analysis pipeline aligns with the user's interest in efficient, real-time data processing.

AI Insights

• The technique is demonstrated through several examples, including an automatic emergency braking system, where consistency and availability are crucial properties. (ML: 0.88)👍👎
• The technique is demonstrated through several examples, including an automatic emergency braking system, where consistency and availability are crucial properties. (ML: 0.88)👍👎
• The paper discusses the importance of consistency and availability in distributed software systems, particularly in real-time systems. (ML: 0.87)👍👎
• The technique may lead to STP violations if not used carefully. (ML: 0.86)👍👎
• Logical Execution Time (LET): A principle for decoupling concurrent computations without introducing nondeterminism. (ML: 0.84)👍👎
• tardy: A message that arrives with larger-than-expected latency. (ML: 0.84)👍👎
• The maxwait coordinator technique provides a flexible way to balance consistency and availability in distributed software systems, particularly in real-time systems. (ML: 0.83)👍👎
• maxwait: The maximum amount of time that a federate can wait for input before it is considered tardy. (ML: 0.77)👍👎
• It allows for dynamically changing the maximum wait time in a reaction body using an lf_set_fed_maxwait API function. (ML: 0.71)👍👎
• It proposes a new technique called maxwait coordinator that allows for dynamically changing the maximum wait time in a reaction body using an lf_set_fed_maxwait API function. (ML: 0.69)👍👎

Abstract
Distributed time-sensitive systems must balance timing requirements (availability) and consistency in the presence of communication delays and synchronization uncertainty. This paper presents maxwait, a simple coordination mechanism with surprising generality that makes these tradeoffs explicit and configurable. We demonstrate that this mechanism subsumes classical distributed system methods such as PTIDES, Chandy-and-Misra with or without null messages, Jefferson's Time-Warp, and Lamport's time-based fault detection, while enabling real-time behavior in distributed cyber-physical applications. The mechanism can also realize many commonly used distributed system patterns, including logical execution time (LET), publish and subscribe, actors, conflict-free replicated data types (CRDTs), and remote procedure calls with futures. More importantly, it adds to these mechanisms better control over timing, bounded time fault detection, and the option of making them more deterministic, all within a single semantic framework. Implemented as an extension of the Lingua Franca coordination language, maxwait enforces logical-time consistency when communication latencies are bounded and provides structured fault handling when bounds are violated.

Why we are recommending this paper?
Due to your Interest in Distributed Systems

This work presents a mechanism specifically designed for distributed time-sensitive systems, directly addressing the user's need for resilience and low latency in complex networks. The 'maxwait' approach offers a generalized solution for managing timing constraints.

AI Insights

• The exact functional form of the scaling functions F, G, s, w is not important for the economic message. (ML: 0.90)👍👎
• Finite-size scaling analysis reveals that the probability P_N[τ_c ≤ T] of an economy crashing at a time τ_c less than a given T follows a universal scaling form, independent of model parameters. (ML: 0.90)👍👎
• Finite-size scaling analysis reveals that the probability P_N[τ_c ≤ T] of an economy crashing at a time τ_c less than a given T follows a universal scaling form, independent of model parameters. (ML: 0.90)👍👎
• The numerical values of σ_c, a, s_0, w_0 will not be universal. (ML: 0.84)👍👎
• The problem of liquidity crises in supply chains is studied using a non-substitutable Leontief economy model. (ML: 0.80)👍👎
• PN[τc≤T,σ] The model exhibits a phase transition as the noise amplitude σ increases, with a critical value σ_c(κ) beyond which large economies surely crash after a finite time. (ML: 0.79)👍👎
• The model exhibits a phase transition as the noise amplitude σ increases, with a critical value σ_c(κ) beyond which large economies surely crash after a finite time. (ML: 0.79)👍👎
• The critical shock amplitude σ_c can be identified as the value beyond which very large economies surely crash after a finite time. (ML: 0.78)👍👎
• The width of the transition w(v) decreases with T and becomes independent of N for large enough N and T. (ML: 0.75)👍👎

Abstract
We study the disequilibrium dynamics of a stylised model of production networks in which firms use perishable and non-substitutable intermediate inputs, so that adverse idiosyncratic productivity shocks can trigger downstream shortages and output losses. To protect against such disruptions, firms hold precautionary inventories that act as buffer stocks. We show that, for a given dispersion of firm-level productivity shocks, there exists a critical level of inventories above which the economy remains in a stable stochastic steady state. Below this critical level, the system becomes fragile, i.e., it becomes prone to system-wide crises. As this resilience-fragility boundary is approached from above, aggregate output volatility rises sharply and diverges, even though shocks are purely idiosyncratic. Because inventories are costly, competitive pressures induce firms to economize on buffers. Although we do not explicitly model such costs, we argue that the resulting behaviour of individual firms drives the system close to criticality, generating persistent excess macroeconomic volatility -- in other words, ``small shocks, large cycles'' -- in line with other settings where efficiency and resilience are in tension with each other. In the language of phase transitions, the resilient-to-fragile transition is continuous (supercritical): the economy exhibits a well-defined stochastic equilibrium with finite volatility on one side of the boundary, while beyond it the probability of a collapse in finite time tends to one. We characterize this transition primarily through numerical simulations and derive an analytical description in a high-perishability, high-connectivity limit.

Why we are recommending this paper?
Due to your Interest in Resilience

The paper investigates disruptions in supply chains, a key factor in achieving resilience. Analyzing how firms respond to shocks is highly relevant to the user's interest in robust systems.

AI Insights

• Finetune-Informed Pretraining (FIP): A pretraining strategy that prioritizes the target modality during pretraining through asymmetric masking, decoder depth, and loss weighting. (ML: 0.91)👍👎
• Finetune-Informed Pretraining (FIP) improves downstream performance by prioritizing the target modality during pretraining through asymmetric masking, decoder depth, and loss weighting. (ML: 0.90)👍👎
• FIP prioritizes the target modality during pretraining, leading to improved downstream performance and robustness in low-SNR regimes. (ML: 0.87)👍👎
• The weighted reconstruction loss function (LFAP) plays a crucial role in guiding the shared encoder to prioritize the target modality's reconstruction quality. (ML: 0.86)👍👎
• Asymmetric Masking: A technique where a higher masking ratio is applied to the target modality and lower ratios are used for other modalities. (ML: 0.85)👍👎
• Weighted Reconstruction Loss Function (LFAP): An objective function that guides the shared encoder to prioritize the target modality's reconstruction quality over other modalities. (ML: 0.83)👍👎
• The weighted reconstruction loss function (LFAP) guides the shared encoder to prioritize the target modality's reconstruction quality over other modalities, leading to improved performance. (ML: 0.83)👍👎
• FIP-DenoMAE outperforms DenoMAE and ViT baselines in classification accuracy, especially in low-SNR regimes (-10 dB), achieving 69.2% versus DenoMAE's 68.4% and ViT's 55.4%. (ML: 0.83)👍👎
• Asymmetric masking with a higher masking ratio for the target modality (p_target = 0.80) and lower ratios for other modalities (p_other = 0.60) enhances reconstruction quality and robustness. (ML: 0.82)👍👎
• FIP is a model-agnostic method that can be applied to various multimodal masked modeling pipelines without modifying the shared encoder or requiring additional supervision. (ML: 0.81)👍👎

Abstract
Multimodal pretraining is effective for building general-purpose representations, but in many practical deployments, only one modality is heavily used during downstream fine-tuning. Standard pretraining strategies treat all modalities uniformly, which can lead to under-optimized representations for the modality that actually matters. We propose Finetune-Informed Pretraining (FIP), a model-agnostic method that biases representation learning toward a designated target modality needed at fine-tuning time. FIP combines higher masking difficulty, stronger loss weighting, and increased decoder capacity for the target modality, without modifying the shared encoder or requiring additional supervision. When applied to masked modeling on constellation diagrams for wireless signals, FIP consistently improves downstream fine-tuned performance with no extra data or compute. FIP is simple to implement, architecture-compatible, and broadly applicable across multimodal masked modeling pipelines.

Why we are recommending this paper?
Due to your Interest in High throughput

This paper explores optimizing representations for downstream tasks, which can be applied to improving the efficiency and performance of distributed systems. The focus on fine-tuning aligns with the need for tailored solutions.

AI Insights

• The paper studies distributed log-linear learning under noisy communication and distinguishes between two canonical regimes: snapshot communication and fast communication. (ML: 0.90)👍👎
• distributed log-linear learning snapshot communication fast communication Gibbs sampler scaled coordination potential reversible Markov chain (ML: 0.78)👍👎
• Snapshot communication induces a non-reversible, non-equilibrium Markov process whose stationary distribution admits no closed-form expression. (ML: 0.71)👍👎
• The finite-K model interpolates between snapshot and fast communication, capturing practical communication constraints such as retransmissions or limited averaging. (ML: 0.69)👍👎
• Fast communication induces a Gibbs sampler associated with a scaled coordination potential, yielding a reversible Markov chain with a closed-form stationary distribution. (ML: 0.66)👍👎

Abstract
We study binary coordination games over graphs under log-linear learning when neighbor actions are conveyed through explicit noisy communication links. Each edge is modeled as either a binary symmetric channel (BSC) or a binary erasure channel (BEC). We analyze two operational regimes. For binary symmetric and binary erasure channels, we provide a structural characterization of the induced learning dynamics. In a fast-communication regime, agents update using channel-averaged payoffs; the resulting learning dynamics coincide with a Gibbs sampler for a scaled coordination potential, where channel reliability enters only through a scalar attenuation coefficient. In a snapshot regime, agents update from a single noisy realization and ignore channel statistics; the induced Markov chain is generally nonreversible, but admits a high-temperature expansion whose drift matches that of the fast Gibbs sampler with the same attenuation. We further formalize a finite-$K$ communication budget, which interpolates between snapshot and fast behavior as the number of channel uses per update grows. This viewpoint yields a communication-theoretic interpretation in terms of retransmissions and repetition coding, and extends naturally to heterogeneous link reliabilities via effective edge weights. Numerical experiments illustrate the theory and quantify the tradeoff between communication resources and steady-state coordination quality.

Why we are recommending this paper?
Due to your Interest in Distributed Systems

The research tackles the challenges of learning in networks with noisy communication, a critical aspect of building resilient and high-throughput distributed systems. Analyzing communication delays and uncertainty is directly relevant to the user's interests.