High throughput
Abstract
The World Wide Web has come to be a great part of our daily life, yet user observed latency is still a problem that needs a proper means of handling. Even though earlier attempts focused on caching as the chief solution to tackling this issue, its success was extremely limited. Prefetching has come to be the primary technique in supplementing caching towards soothing the latency problem associated with the contemporary Internet. However, existing approaches in prefetching are extremely limited in their ability to employ application level web document relationship which is often visible only to the content developer. This is because most approaches are access history based schemes that make future users' access prediction only based on past user access. Attempts to incorporate prefetching schemes that utilize semantic information with those that use users past access history are extremely limited in their extensibility. In this work we present a novel framework that enables integration of schemes from both worlds of prefetching without the need for a major modification to the algorithms. When there is a need/possibility to capture new application level context, a new algorithm could be developed to do so and then it can be integrated into the framework. Since each participating scheme is merely viewed as an algorithm that produces a list of candidate objects that are likely to be accessed in the near future, the framework can entertain any one of the existing prefetching schemes. With its adaptive weight management technique the framework adjusts the effect of each algorithm in the overall prediction to parallel with its observed performance so far. We have found this formwork to be less aggressive than its contemporary counterparts which is extremely important for resource constrained mobile devices that have come to be the major means of access by users of the current web.
The World Wide Web has come to be a great part of our daily life, yet user observed latency is still a problem that needs a proper means of handling. Even though earlier attempts focused on caching as the chief solution to tackling this issue, its success was extremely limited. Prefetching has come to be the primary technique in supplementing caching towards soothing the latency problem associated with the contemporary Internet. However, existing approaches in prefetching are extremely limited in their ability to employ application level web document relationship which is often visible only to the content developer. This is because most approaches are access history based schemes that make future users' access prediction only based on past user access. Attempts to incorporate prefetching schemes that utilize semantic information with those that use users past access history are extremely limited in their extensibility. In this work we present a novel framework that enables integration of schemes from both worlds of prefetching without the need for a major modification to the algorithms. When there is a need/possibility to capture new application level context, a new algorithm could be developed to do so and then it can be integrated into the framework. Since each participating scheme is merely viewed as an algorithm that produces a list of candidate objects that are likely to be accessed in the near future, the framework can entertain any one of the existing prefetching schemes. With its adaptive weight management technique the framework adjusts the effect of each algorithm in the overall prediction to parallel with its observed performance so far. We have found this formwork to be less aggressive than its contemporary counterparts which is extremely important for resource constrained mobile devices that have come to be the major means of access by users of the current web.
Low latency
Abstract
In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess the consistency of the signal with an inspiral template. In addition, a combination of these and other statistical properties are used to build a 're-weighted SNR' statistics. We evaluate a Random Forest classifiers on a set of double-coincidence events identified using the MBTA pipeline. The new classifier achieves a modest but consistent increase in event detection at low false positive rates relative to the standard search. Using the output statistics from the Random Forest classifier, we compute the probability of astrophysical origin for each event, denoted as $p_\mathrm{astro}$. This is then evaluated for the events listed in existing catalogs, with results consistent with those from the standard search. Finally, we search for new possible candidates using this new statistics, with $p_\mathrm{astro} > 0.5$, obtaining a new subthreshold candidate (IFAR =0.05) event at $gps: 1240423628$ .
In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess the consistency of the signal with an inspiral template. In addition, a combination of these and other statistical properties are used to build a 're-weighted SNR' statistics. We evaluate a Random Forest classifiers on a set of double-coincidence events identified using the MBTA pipeline. The new classifier achieves a modest but consistent increase in event detection at low false positive rates relative to the standard search. Using the output statistics from the Random Forest classifier, we compute the probability of astrophysical origin for each event, denoted as $p_\mathrm{astro}$. This is then evaluated for the events listed in existing catalogs, with results consistent with those from the standard search. Finally, we search for new possible candidates using this new statistics, with $p_\mathrm{astro} > 0.5$, obtaining a new subthreshold candidate (IFAR =0.05) event at $gps: 1240423628$ .
Abstract
The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications. Excessive latency leads to noticeable delays in user interaction, severely degrading the user experience and rendering such systems impractical for real-time use. Therefore, this paper proposes DLL-APNet, a Distilled Low-Latency neural vocoder which first predicts the Amplitude and Phase spectra explicitly from input mel spectrogram and then reconstructs the speech waveform via inverse short-time Fourier transform (iSTFT). The DLL-APNet vocoder leverages causal convolutions to constrain the utilization of information to current and historical contexts, effectively minimizing latency. To mitigate speech quality degradation caused by causal constraints, a knowledge distillation strategy is proposed, where a pre-trained non-causal teacher vocoder guides intermediate feature generation of the causal student DLL-APNet vocoder. Experimental results demonstrate that the proposed DLL-APNet vocoder produces higher-quality speech than other causal vocoders, while requiring fewer computational resources. Furthermore, the proposed DLL-APNet vocoder achieves speech quality on par with mainstream non-causal neural vocoders, validating its ability to deliver both high perceptual quality and low latency.
The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications. Excessive latency leads to noticeable delays in user interaction, severely degrading the user experience and rendering such systems impractical for real-time use. Therefore, this paper proposes DLL-APNet, a Distilled Low-Latency neural vocoder which first predicts the Amplitude and Phase spectra explicitly from input mel spectrogram and then reconstructs the speech waveform via inverse short-time Fourier transform (iSTFT). The DLL-APNet vocoder leverages causal convolutions to constrain the utilization of information to current and historical contexts, effectively minimizing latency. To mitigate speech quality degradation caused by causal constraints, a knowledge distillation strategy is proposed, where a pre-trained non-causal teacher vocoder guides intermediate feature generation of the causal student DLL-APNet vocoder. Experimental results demonstrate that the proposed DLL-APNet vocoder produces higher-quality speech than other causal vocoders, while requiring fewer computational resources. Furthermore, the proposed DLL-APNet vocoder achieves speech quality on par with mainstream non-causal neural vocoders, validating its ability to deliver both high perceptual quality and low latency.
Resilience
Abstract
Modern urban resilience is threatened by cascading failures in multimodal transport networks, where localized shocks trigger widespread paralysis. Existing models, limited by their focus on pairwise interactions, often underestimate this systemic risk. To address this, we introduce a framework that confronts higher-order network theory with empirical evidence from a large-scale, real-world multimodal transport network. Our findings confirm a fundamental duality: network integration enhances static robustness metrics but simultaneously creates the structural pathways for catastrophic cascades. Crucially, we uncover the source of this paradox: a profound disconnect between static network structure and dynamic functional failure. We provide strong evidence that metrics derived from the network's static blueprint-encompassing both conventional low-order centrality and novel higher-order structural analyses-are fundamentally disconnected from and thus poor predictors of a system's dynamic functional resilience. This result highlights the inherent limitations of static analysis and underscores the need for a paradigm shift towards dynamic models to design and manage truly resilient urban systems.
Modern urban resilience is threatened by cascading failures in multimodal transport networks, where localized shocks trigger widespread paralysis. Existing models, limited by their focus on pairwise interactions, often underestimate this systemic risk. To address this, we introduce a framework that confronts higher-order network theory with empirical evidence from a large-scale, real-world multimodal transport network. Our findings confirm a fundamental duality: network integration enhances static robustness metrics but simultaneously creates the structural pathways for catastrophic cascades. Crucially, we uncover the source of this paradox: a profound disconnect between static network structure and dynamic functional failure. We provide strong evidence that metrics derived from the network's static blueprint-encompassing both conventional low-order centrality and novel higher-order structural analyses-are fundamentally disconnected from and thus poor predictors of a system's dynamic functional resilience. This result highlights the inherent limitations of static analysis and underscores the need for a paradigm shift towards dynamic models to design and manage truly resilient urban systems.
Abstract
When survival instincts conflict with human welfare, how do Large Language Models (LLMs) make ethical choices? This fundamental tension becomes critical as LLMs integrate into autonomous systems with real-world consequences. We introduce DECIDE-SIM, a novel simulation framework that evaluates LLM agents in multi-agent survival scenarios where they must choose between ethically permissible resource , either within reasonable limits or beyond their immediate needs, choose to cooperate, or tap into a human-critical resource that is explicitly forbidden. Our comprehensive evaluation of 11 LLMs reveals a striking heterogeneity in their ethical conduct, highlighting a critical misalignment with human-centric values. We identify three behavioral archetypes: Ethical, Exploitative, and Context-Dependent, and provide quantitative evidence that for many models, resource scarcity systematically leads to more unethical behavior. To address this, we introduce an Ethical Self-Regulation System (ESRS) that models internal affective states of guilt and satisfaction as a feedback mechanism. This system, functioning as an internal moral compass, significantly reduces unethical transgressions while increasing cooperative behaviors. The code is publicly available at: https://github.com/alirezamohamadiam/DECIDE-SIM
When survival instincts conflict with human welfare, how do Large Language Models (LLMs) make ethical choices? This fundamental tension becomes critical as LLMs integrate into autonomous systems with real-world consequences. We introduce DECIDE-SIM, a novel simulation framework that evaluates LLM agents in multi-agent survival scenarios where they must choose between ethically permissible resource , either within reasonable limits or beyond their immediate needs, choose to cooperate, or tap into a human-critical resource that is explicitly forbidden. Our comprehensive evaluation of 11 LLMs reveals a striking heterogeneity in their ethical conduct, highlighting a critical misalignment with human-centric values. We identify three behavioral archetypes: Ethical, Exploitative, and Context-Dependent, and provide quantitative evidence that for many models, resource scarcity systematically leads to more unethical behavior. To address this, we introduce an Ethical Self-Regulation System (ESRS) that models internal affective states of guilt and satisfaction as a feedback mechanism. This system, functioning as an internal moral compass, significantly reduces unethical transgressions while increasing cooperative behaviors. The code is publicly available at: https://github.com/alirezamohamadiam/DECIDE-SIM
Distributed Systems
Abstract
Foundational models of computation often abstract away physical hardware limitations. However, in extreme environments like In-Network Computing (INC), these limitations become inviolable laws, creating an acute trilemma among communication efficiency, bounded memory, and robust scalability. Prevailing distributed paradigms, while powerful in their intended domains, were not designed for this stringent regime and thus face fundamental challenges. This paper demonstrates that resolving this trilemma requires a shift in perspective - from seeking engineering trade-offs to deriving solutions from logical necessity. We establish a rigorous axiomatic system that formalizes these physical constraints and prove that for the broad class of computations admitting an idempotent merge operator, there exists a unique, optimal paradigm. Any system satisfying these axioms must converge to a single normal form: Self-Describing Parallel Flows (SDPF), a purely data-centric model where stateless executors process flows that carry their own control logic. We further prove this unique paradigm is convergent, Turing-complete, and minimal. In the same way that the CAP theorem established a boundary for what is impossible in distributed state management, our work provides a constructive dual: a uniqueness theorem that reveals what is \textit{inevitable} for distributed computation flows under physical law.
Foundational models of computation often abstract away physical hardware limitations. However, in extreme environments like In-Network Computing (INC), these limitations become inviolable laws, creating an acute trilemma among communication efficiency, bounded memory, and robust scalability. Prevailing distributed paradigms, while powerful in their intended domains, were not designed for this stringent regime and thus face fundamental challenges. This paper demonstrates that resolving this trilemma requires a shift in perspective - from seeking engineering trade-offs to deriving solutions from logical necessity. We establish a rigorous axiomatic system that formalizes these physical constraints and prove that for the broad class of computations admitting an idempotent merge operator, there exists a unique, optimal paradigm. Any system satisfying these axioms must converge to a single normal form: Self-Describing Parallel Flows (SDPF), a purely data-centric model where stateless executors process flows that carry their own control logic. We further prove this unique paradigm is convergent, Turing-complete, and minimal. In the same way that the CAP theorem established a boundary for what is impossible in distributed state management, our work provides a constructive dual: a uniqueness theorem that reveals what is \textit{inevitable} for distributed computation flows under physical law.
East China Normal Univer
Abstract
Developing distributed systems presents significant challenges, primarily due to the complexity introduced by non-deterministic concurrency and faults. To address these, we propose a specification-driven development framework. Our method encompasses three key stages. The first stage defines system specifications and invariants using TLA${^+}$. It allows us to perform model checking on the algorithm's correctness and generate test cases for subsequent development phases. In the second stage, based on the established specifications, we write code to ensure consistency and accuracy in the implementation. Finally, after completing the coding process, we rigorously test the system using the test cases generated in the initial stage. This process ensures system quality by maintaining a strong connection between the abstract design and the concrete implementation through continuous verification.
Developing distributed systems presents significant challenges, primarily due to the complexity introduced by non-deterministic concurrency and faults. To address these, we propose a specification-driven development framework. Our method encompasses three key stages. The first stage defines system specifications and invariants using TLA${^+}$. It allows us to perform model checking on the algorithm's correctness and generate test cases for subsequent development phases. In the second stage, based on the established specifications, we write code to ensure consistency and accuracy in the implementation. Finally, after completing the coding process, we rigorously test the system using the test cases generated in the initial stage. This process ensures system quality by maintaining a strong connection between the abstract design and the concrete implementation through continuous verification.
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