The reliable provision of entangled qubits is an essential precondition in a variety of schemes for distributed quantum computing. This is challenged by multiple nuisances, such as errors during the transmission over quantum links, but also due to degradation of the entanglement over time due to decoherence. The latter can be seen as a constraint on the latency of the quantum protocol, which brings the problem of quantum protocol design into the context of latency-reliability constraints. We address the problem through hybrid schemes that combine: (1) indirect transmission based on teleportation and distillation; (2) direct transmission, based on quantum error correction (QEC). The intuition is that, at present, the quantum hardware offers low fidelity, which demands distillation; on the other hand, low latency can be obtained by QEC techniques. It is shown that, in the proposed framework, the distillation protocol gives rise to asymmetries that can be exploited by asymmetric quantum error correcting code (QECC), which sets the basis for unique hybrid distillation and coding design. Our results show that ad-hoc asymmetric codes give, compared to conventional QEC, a performance boost and codeword size reduction both in a single link and in a quantum network scenario.
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Game semantics is a denotational semantics presenting compositionally the computational behaviour of various kinds of effectful programs. One of its celebrated achievement is to have obtained full abstraction results for programming languages with a variety of computational effects, in a single framework. This is known as the semantic cube or Abramsky's cube, which for sequential deterministic programs establishes a correspondence between certain conditions on strategies (''innocence'', ''well-bracketing'', ''visibility'') and the absence of matching computational effects. Outside of the sequential deterministic realm, there are still a wealth of game semantics-based full abstraction results; but they no longer fit in a unified canvas. In particular, Ghica and Murawski's fully abstract model for shared state concurrency (IA) does not have a matching notion of pure parallel program-we say that parallelism and interference (i.e. state plus semaphores) are entangled. In this paper we construct a causal version of Ghica and Murawski's model, also fully abstract for IA. We provide compositional conditions parallel innocence and sequentiality, respectively banning interference and parallelism, and leading to four full abstraction results. To our knowledge, this is the first extension of Abramsky's semantic cube programme beyond the sequential deterministic world.
Functional magnetic resonance imaging (fMRI) is essential for developing encoding models that identify functional changes in language-related brain areas of individuals with Neurocognitive Disorders (NCD). While large language model (LLM)-based fMRI encoding has shown promise, existing studies predominantly focus on healthy, young adults, overlooking older NCD populations and cognitive level correlations. This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores, addressing current limitations. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus. This study highlights the potential of fMRI encoding models and brain scores for detecting early functional changes in NCD patients.
Existing high-performance computing (HPC) interconnection architectures are based on high-radix switches, which limits the injection/local performance and introduces latency/energy/cost overhead. The new wafer-scale packaging and high-speed wireline technologies provide high-density, low-latency, and high-bandwidth connectivity, thus promising to support direct-connected high-radix interconnection architecture. In this paper, we propose a wafer-based interconnection architecture called Switch-Less-Dragonfly-on-Wafers. By utilizing distributed high-bandwidth networks-on-chip-on-wafer, costly high-radix switches of the Dragonfly topology are eliminated while increasing the injection/local throughput and maintaining the global throughput. Based on the proposed architecture, we also introduce baseline and improved deadlock-free minimal/non-minimal routing algorithms with only one additional virtual channel. Extensive evaluations show that the Switch-Less-Dragonfly-on-Wafers outperforms the traditional switch-based Dragonfly in both cost and performance. Similar approaches can be applied to other switch-based direct topologies, thus promising to power future large-scale supercomputers.
We propose a computationally efficient algorithm for gradient-based linear dimension reduction and high-dimensional regression. The algorithm initially computes a Mondrian forest and uses this estimator to identify a relevant feature subspace of the inputs from an estimate of the expected gradient outer product (EGOP) of the regression function. In addition, we introduce an iterative approach known as Transformed Iterative Mondrian (TrIM) forest to improve the Mondrian forest estimator by using the EGOP estimate to update the set of features and weights used by the Mondrian partitioning mechanism. We obtain consistency guarantees and convergence rates for the estimation of the EGOP matrix and the random forest estimator obtained from one iteration of the TrIM algorithm. Lastly, we demonstrate the effectiveness of our proposed algorithm for learning the relevant feature subspace across a variety of settings with both simulated and real data.
Density-based distances (DBDs) offer an elegant solution to the problem of metric learning. By defining a Riemannian metric which increases with decreasing probability density, shortest paths naturally follow the data manifold and points are clustered according to the modes of the data. We show that existing methods to estimate Fermat distances, a particular choice of DBD, suffer from poor convergence in both low and high dimensions due to i) inaccurate density estimates and ii) reliance on graph-based paths which are increasingly rough in high dimensions. To address these issues, we propose learning the densities using a normalizing flow, a generative model with tractable density estimation, and employing a smooth relaxation method using a score model initialized from a graph-based proposal. Additionally, we introduce a dimension-adapted Fermat distance that exhibits more intuitive behavior when scaled to high dimensions and offers better numerical properties. Our work paves the way for practical use of density-based distances, especially in high-dimensional spaces.
We introduce a practical sign-dependent sequence selection metric for probabilistic amplitude shaping and propose a simple method to predict the gains in signal-to-noise ratio (SNR) for sequence selection. The proposed metric provides a $0.5$ dB SNR gain for single-polarized 256-QAM transmission over a long-haul fiber link.
Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert space. In this paper, we systematically investigate the complexity of digital quantum simulation based on product formulas in the low-energy subspace. We show that the simulation error depends on the effective low-energy norm of the Hamiltonian for a variety of digital quantum simulation algorithms and quantum systems, allowing improvements over the previous complexities for full unitary simulations even for imperfect state preparations due to thermalization. In particular, for simulating spin models in the low-energy subspace, we prove that randomized product formulas such as qDRIFT and random permutation require smaller Trotter numbers. Such improvement also persists in symmetry-protected digital quantum simulations. We prove a similar improvement in simulating the dynamics of power-law quantum interactions. We also provide a query lower bound for general digital quantum simulations in the low-energy subspace.
Biometric systems based on brain activity have been proposed as an alternative to passwords or to complement current authentication techniques. By leveraging the unique brainwave patterns of individuals, these systems offer the possibility of creating authentication solutions that are resistant to theft, hands-free, accessible, and potentially even revocable. However, despite the growing stream of research in this area, faster advance is hindered by reproducibility problems. Issues such as the lack of standard reporting schemes for performance results and system configuration, or the absence of common evaluation benchmarks, make comparability and proper assessment of different biometric solutions challenging. Further, barriers are erected to future work when, as so often, source code is not published open access. To bridge this gap, we introduce NeuroIDBench, a flexible open source tool to benchmark brainwave-based authentication models. It incorporates nine diverse datasets, implements a comprehensive set of pre-processing parameters and machine learning algorithms, enables testing under two common adversary models (known vs unknown attacker), and allows researchers to generate full performance reports and visualizations. We use NeuroIDBench to investigate the shallow classifiers and deep learning-based approaches proposed in the literature, and to test robustness across multiple sessions. We observe a 37.6% reduction in Equal Error Rate (EER) for unknown attacker scenarios (typically not tested in the literature), and we highlight the importance of session variability to brainwave authentication. All in all, our results demonstrate the viability and relevance of NeuroIDBench in streamlining fair comparisons of algorithms, thereby furthering the advancement of brainwave-based authentication through robust methodological practices.
The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.
Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.
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