Triggerless Data Acquisition Systems (DAQs) require transmitting the data stream from multiple links to the processing node. The short input data words must be concentrated and packed into the longer bit vectors the output interface (e.g. PCI Express) uses. In that process, the unneeded data must be eliminated, and a dense stream of useful DAQ data must be created. Additionally, the time order of the data should be preserved. This paper presents a new solution using the Baseline Network with Reversed Outputs (BNRO)for high-speed data routing.A thorough analysis of the network operation enabled increased scalability compared to the previously published concentrator based on 8x8 network. The presented solution may be scaled by adding additional layers to the BNRO network while minimizing resource consumption. Simulations were done for 4 and 5 layers (16 and 32 inputs). The FPGA synthesis has been performed for 16inputs. The pipeline registers may be added in each network independently, shortening the critical path and increasing the maximum acceptable clock frequency.
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Networking：IFIP International Conferences on Networking。
Explanation：國際網絡會議。
Publisher：IFIP。
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Bayesian inference and kernel methods are well established in machine learning. The neural network Gaussian process in particular provides a concept to investigate neural networks in the limit of infinitely wide hidden layers by using kernel and inference methods. Here we build upon this limit and provide a field-theoretic formalism which covers the generalization properties of infinitely wide networks. We systematically compute generalization properties of linear, non-linear, and deep non-linear networks for kernel matrices with heterogeneous entries. In contrast to currently employed spectral methods we derive the generalization properties from the statistical properties of the input, elucidating the interplay of input dimensionality, size of the training data set, and variability of the data. We show that data variability leads to a non-Gaussian action reminiscent of a ($\varphi^3+\varphi^4$)-theory. Using our formalism on a synthetic task and on MNIST we obtain a homogeneous kernel matrix approximation for the learning curve as well as corrections due to data variability which allow the estimation of the generalization properties and exact results for the bounds of the learning curves in the case of infinitely many training data points.
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent studies have shown that GNNs can approximate any function on graphs, modulo the equivalence relation on graphs defined by the Weisfeiler--Lehman (WL) test. However, these results suffer from some limitations, both because they were derived using the Stone--Weierstrass theorem -- which is existential in nature, -- and because they assume that the target function to be approximated must be continuous. Furthermore, all current results are dedicated to graph classification/regression tasks, where the GNN must produce a single output for the whole graph, while also node classification/regression problems, in which an output is returned for each node, are very common. In this paper, we propose an alternative way to demonstrate the approximation capability of GNNs that overcomes these limitations. Indeed, we show that GNNs are universal approximators in probability for node classification/regression tasks, as they can approximate any measurable function that satisfies the 1--WL equivalence on nodes. The proposed theoretical framework allows the approximation of generic discontinuous target functions and also suggests the GNN architecture that can reach a desired approximation. In addition, we provide a bound on the number of the GNN layers required to achieve the desired degree of approximation, namely $2r-1$, where $r$ is the maximum number of nodes for the graphs in the domain.
This study presents a conceptual design of laparoscopic forceps whose grasping torque can be directly controlled by the user. By integrating an adjustable constant torque mechanism, the handle opening angle is converted to the grasping torque irrespective of the jaw opening angle. This feature overcomes the limitation regarding of the lack of direct haptic feedback in laparoscopic minimally invasive surgery, preventing damage of delicate tissue during forceps grasping.
The expanding hardware diversity in high performance computing adds enormous complexity to scientific software development. Developers who aim to write maintainable software have two options: 1) To use a so-called data locality abstraction that handles portability internally, thereby, performance-productivity becomes a trade off. Such abstractions usually come in the form of libraries, domain-specific languages, and run-time systems. 2) To use generic programming where performance, productivity and portability are subject to software design. In the direction of the second, this work describes a design approach that allows the integration of low-level and verbose programming tools into high-level generic algorithms based on template meta-programming in C++. This enables the development of performance-portable applications targeting host-device computer architectures, such as CPUs and GPUs. With a suitable design in place, the extensibility of generic algorithms to new hardware becomes a well defined procedure that can be developed in isolation from other parts of the code. That allows scientific software to be maintainable and efficient in a period of diversifying hardware in HPC. As proof of concept, a finite-difference modelling algorithm for the acoustic wave equation is developed and benchmarked using roofline model analysis on Intel Xeon Gold 6248 CPU, Nvidia Tesla V100 GPU, and AMD MI100 GPU.
The recent surge in Large Language Model (LLM) related applications has led to a concurrent escalation in expectations for LLMs to accommodate a myriad of personas and encompass a broad spectrum of perspectives. An important first step towards addressing this demand is to align language models with specific personas, be it groups of users or individuals. Towards this goal, we first present a new conceptualization of a persona. Moving beyond the traditional reliance on demographics like age, gender, or political party affiliation, we introduce a data-driven persona definition methodology built on collaborative-filtering. In this methodology, users are embedded into a continuous vector space based on their opinions and clustered into cohorts that manifest coherent views across specific inquiries. This methodology allows for a more nuanced understanding of different latent social groups present in the overall population (as opposed to simply using demographic groups) and enhances the applicability of model steerability. Finally, we present an efficient method to steer LLMs towards a particular persona. We learn a soft-prompting model to map the continuous representation of users into sequences of virtual tokens which, when prepended to the LLM input, enables the LLM to produce responses aligned with a given user. Our results show that our steerability algorithm is superior in performance compared to a collection of baselines.
Pre-trained Large Language Models (LLMs) have shown success in a diverse set of language inference and understanding tasks. The pre-training stage of LLMs looks at a large corpus of raw textual data. The BabyLM shared task compares LLM pre-training to human language acquisition, where the number of tokens seen by 13-year-old kids is magnitudes smaller than the number of tokens seen by LLMs. In this work, we pre-train and evaluate LLMs on their ability to learn contextual word representations using roughly the same number of tokens as seen by children. We provide a strong set of baselines; with different architectures, evaluation of changes in performance across epochs, and reported pre-training metrics for the strict small and strict tracks of the task. We also try to loosely replicate the RoBERTa baseline given by the task organizers to observe the training robustness to hyperparameter selection and replicability. We provide the submission details to the strict and strict-small tracks in this report.
Numerous applications in the field of molecular communications (MC) such as healthcare systems are often event-driven. The conventional Shannon capacity may not be the appropriate metric for assessing performance in such cases. We propose the identification (ID) capacity as an alternative metric. Particularly, we consider randomized identification (RI) over the discrete-time Poisson channel (DTPC), which is typically used as a model for MC systems that utilize molecule-counting receivers. In the ID paradigm, the receiver's focus is not on decoding the message sent. However, he wants to determine whether a message of particular significance to him has been sent or not. In contrast to Shannon transmission codes, the size of ID codes for a Discrete Memoryless Channel (DMC) grows doubly exponentially fast with the blocklength, if randomized encoding is used. In this paper, we derive the capacity formula for RI over the DTPC subject to some peak and average power constraints. Furthermore, we analyze the case of state-dependent DTPC.
Many networks can be characterised by the presence of communities, which are groups of units that are closely linked and can be relevant in understanding the system's overall function. Recently, hypergraphs have emerged as a fundamental tool for modelling systems where interactions are not limited to pairs but may involve an arbitrary number of nodes. Using a dual approach to community detection, in this study we extend the concept of link communities to hypergraphs, allowing us to extract informative clusters of highly related hyperedges. We analyze the dendrograms obtained by applying hierarchical clustering to distance matrices among hyperedges on a variety of real-world data, showing that hyperlink communities naturally highlight the hierarchical and multiscale structure of higher-order networks. Moreover, by using hyperlink communities, we are able to extract overlapping memberships from nodes, overcoming limitations of traditional hard clustering methods. Finally, we introduce higher-order network cartography as a practical tool for categorizing nodes into different structural roles based on their interaction patterns and community participation. This approach helps identify different types of individuals in a variety of real-world social systems. Our work contributes to a better understanding of the structural organization of real-world higher-order systems.
The modelling of electrokinetic flows is a critical aspect spanning many industrial applications and research fields. This has introduced great demand in flexible numerical solvers to describe these flows. The underlying phenomena are microscopic, non-linear, and often involve multiple domains. Therefore often model assumptions and several numerical approximations are introduced to simplify the solution. In this work, we present a multi-domain multi-species electrokinetic flow model including complex interface and bulk reactions. After a dimensional analysis and an overview of some limiting regimes, we present a set of general purpose finite-volume solvers, based on \of, capable of describing an arbitrary number of electrochemical species over multiple interacting (solid or fluid) domains \cite{spnpfoam}. We provide verification of the computational approach for several cases involving electrokinetic flows, reactions between species, and complex geometries. We first present three one-dimensional verification test cases, and then show the capability of the solver to tackle two- and three-dimensional electrically driven flows and ionic transport in random porous structures. The purpose of this work is to lay the foundation for a general-purpose open-source flexible modelling tool for problems in electrochemistry and electrokinetics at different scales.
In large-scale systems there are fundamental challenges when centralised techniques are used for task allocation. The number of interactions is limited by resource constraints such as on computation, storage, and network communication. We can increase scalability by implementing the system as a distributed task-allocation system, sharing tasks across many agents. However, this also increases the resource cost of communications and synchronisation, and is difficult to scale. In this paper we present four algorithms to solve these problems. The combination of these algorithms enable each agent to improve their task allocation strategy through reinforcement learning, while changing how much they explore the system in response to how optimal they believe their current strategy is, given their past experience. We focus on distributed agent systems where the agents' behaviours are constrained by resource usage limits, limiting agents to local rather than system-wide knowledge. We evaluate these algorithms in a simulated environment where agents are given a task composed of multiple subtasks that must be allocated to other agents with differing capabilities, to then carry out those tasks. We also simulate real-life system effects such as networking instability. Our solution is shown to solve the task allocation problem to 6.7% of the theoretical optimal within the system configurations considered. It provides 5x better performance recovery over no-knowledge retention approaches when system connectivity is impacted, and is tested against systems up to 100 agents with less than a 9% impact on the algorithms' performance.
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