搜索结果：Daisy

Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailored to specific execution environments. In this paper, we present DaiSy, a unified library for exact data series similarity search that integrates multiple state-of-the-art (iSAX-based) algorithms within a single, coherent framework. DaiSy is the first library to support exact similarity search across diverse execution environments, including implementations for disk-based, in-memory, GPU-accelerated, and distributed scalable similarity search. Although designed for data series, DaiSy is also directly applicable to exact similarity search over vector data, enabling its use in a broader range of applications. The library supports interfaces in both C++ and Python, enabling users to easily integrate its functionality into a variety of tasks. DaiSy is open-sourced and available at: https://github.com/MChatzakis/DaiSy.

Daisy cubes are partial cubes whose vertices can be represented by a down-set of a Boolean lattice. This paper gives a label-free characterization: a finite partial cube is a daisy cube if and only if every Djoković--Winkler $Θ$-class is peripheral. The proof orients each $Θ$-class toward a peripheral halfspace and shows that the resulting $Θ$-coordinate labels are closed downward. The characterization turns recognition into a condition on the halfspace structure and gives an exact obstruction formulation: the minimal forbidden pc-minors for daisy cubes are precisely the pc-minor-minimal partial cubes containing a non-peripheral $Θ$-class. We also give an infinite product family of such obstructions. For all $r\ge 2$ and $s\ge 1$, the graph obtained from $P_3^{\square r}\sq Q_s$ by deleting the two opposite corners is a minimal forbidden partial cube-minor for the class of daisy cubes.

Let $X\subseteq\{0,1\}^n$ be a set of binary strings of length $n$. The daisy cube $Q_n(X)$ is the subgraph of the hypercube $Q_n$ induced by the union of the intervals $I(x,0^n)$ for $x\in X$. As a subclass of partial cubes, it generalizes Fibonacci cubes and Lucas cubes. For a graph $G$ and a vertex $u\in V(G)$, we consider the cube polynomial $C_G(x)$, the distance cube polynomial $D_{G,u}(x,y)$, and the polynomial $W_{G,u}(x)$, which count $k$-cubes, $k$-cubes at distance from $u$, and vertices at distance $k$ from $u$, respectively. In this paper, we prove that for a partial cube $G$ with a vertex $u\in V(G)$, $G$ is a daisy cube and $u=0^n$ if and only if one of the following equivalent conditions holds: (1) $C_{G}(x)=W_{G,u}(x+1)$; (2) $D_{G,u}(x,y)=W_{G,u}(x+y)$; (3) $D_{G,u}(x,y)=C_{G}(x+y-1)$. In particular, conditions (1) and (3) give affirmative answers to two open problems posed by Klavžar and Mollard [European J. Combin., 80 (2019) 214--223]. Further, we obtain that for arbitrary partial cube $G$, $D_{G,u}(x,y)\leq W_{G,u}(x+y)$ and $C_{G}(x)\leq W_{G,u}(x+1)$. Besides, another bound for $C_G(x)$ due to Xie et al. [J. Graph Theory, 106 (2024) 907--922] is given by the

We prove that if $A$ and $B$ are daisy cubes whose $τ$-graphs are forests, then $A$ and $B$ are isomorphic if and only if their $τ$-graphs are isomorphic. The result is applied to show that a daisy cube with at least one edge is the resonance graph of a plane bipartite graph $G$ if and only if its $τ$-graph is a forest which is isomorphic to the inner dual of the subgraph of $G$ obtained by removing all forbidden edges. As a consequence, some well known properties of Fibonacci cubes and Lucas cubes are provided as examples with different proofs.

TD-Link is a custom optical communication architecture that combines high-throughput data readout and sub-nanosecond timing synchronization over a single optical fiber for large-scale detector systems. The protocol adopts a multidrop daisy-chain ring topology connecting a Data Concentrator to up to sixteen FERS front-end boards per link, with up to eight independent links per concentrator. Operating at 3.125~Gb/s, TD-Link carries data, synchronization, and control traffic within the same serial stream through a token-based streaming protocol that minimizes per-hop latency and supports on-the-fly payload fragmentation. Transmitter lane alignment on the concentrator is achieved by exploiting the half-full condition of the multi-gigabit transceiver elastic buffer as a one-bit phase detector: a firmware finite-state machine iteratively adjusts the transmit phase interpolator until the FIFO write-to-read pointer difference reaches half-depth, locking each lane to a deterministic phase condition. A Digital Dual Mixer Time Difference (DDMTD) circuit is employed for inter-concentrator synchronization, measuring and compensating the phase offset between the recovered transceiver clock and t

Time-Sensitive Networking (TSN) is a set of standards aiming to enable deterministic and predictable communication over Ethernet networks. However, as the standards of TSN do not specify how to schedule the data streams, the main open problem around TSN is how to compute schedules efficiently and effectively. In this paper, we solve this open problem for no-wait schedules on the daisy-chain topology, one of the most commonly used topologies. Precisely, we develop an efficient algorithm that optimally computes no-wait schedules for the daisy-chain topology, with a time complexity that scales polynomially in both the number of streams and the network size. The basic idea is to recast the no-wait scheduling problem as a variant of a graph coloring problem where some restrictions are imposed on the colors available for every vertex, and where the underlying graph is an interval graph. Our main technical part is to show that this variant of graph coloring problem can be solved in polynomial time for interval graphs, though it is NP-hard for general graphs. Evaluations based on real-life TSN systems demonstrate its optimality and its ability to scale with up to tens of thousands of strea

The Daisy World model has long served as a foundational framework for understanding the self-regulation of planetary biospheres, providing insights into the feedback mechanisms that may govern inhabited exoplanets. In this study, we extend the classic Daisy World model through the lens of Semantic Information Theory (SIT), aiming to characterize the information flow between the biosphere and planetary environment -- what we term the \emph{information architecture} of Daisy World systems. Our objective is to develop novel methodologies for analyzing the evolution of coupled planetary systems, including biospheres and geospheres, with implications for astrobiological observations and the identification of agnostic biosignatures. To operationalize SIT in this context, we introduce a version of the Daisy World model tailored to reflect potential conditions on M-dwarf exoplanets, formulating a system of stochastic differential equations that describe the co-evolution of the daisies and their planetary environment. Analysis of this Exo-Daisy World model reveals how correlations between the biosphere and environment intensify with rising stellar luminosity, and how these correlations corr

We introduce a new benchmark for Danish culture via cultural heritage, Daisy, based on the curated topics from the Danish Culture Canon 2006. For each artifact in the culture canon, we query the corresponding Wikipedia page and have a language model generate random questions. This yields a sampling strategy within each work, with a mix of central of peripheral questions for each work, not only knowledge of mainstream information, but also in-depth cornerstones defining the heritage of Danish Culture, defined by the Canon committee. Each question-answer pair is humanly approved or corrected in the final dataset consisting of 741 close-ended question answer pairs covering topics, from 1300 BC. archaeological findings, 1700 century poems and musicals pieces to contemporary pop music and Danish design and architecture.

It has recently been shown in [\emph{Discrete Appl. Math.} {\bf 366} (2025) 75--85] that the resonance graph of a plane elementary bipartite graph $G$ is a daisy cube if and only if $G$ is peripherally 2-colorable. Let $G$ be a peripherally 2-colorable graph and $R(G)$ be its resonance graph. We provide a decomposition structure of $R(G)$ with respect to an arbitrary finite face of $G$ together with a proper labelling for the vertex set of $R(G)$. An algorithm is obtained to generate a proper labelling for all perfect matchings of $G$ which induces an isometric embedding of $R(G)$ as a daisy cube into an $n$-dimensional hypercube, where $n$ is the isometric dimension of $R(G)$. Moreover, the algorithm can be applied to generate such a proper labelling for all perfect matchings of any plane weakly elementary bipartite graph whose each elementary component with more than two vertices is peripherally 2-colorable. We also compare two binary codings for all perfect matchings of $G$ which induces distinct structures on $R(G)$: one as a daisy cube and the other as a finite distributive, respectively.

We characterize plane bipartite graphs whose resonance graphs are daisy cubes, and therefore generalize related results on resonance graphs of benzenoid graphs, catacondensed even ring systems, as well as 2-connected outerplane bipartite graphs. Firstly, we prove that if $G$ is a plane elementary bipartite graph other than $K_2$, then the resonance graph of $G$ is a daisy cube if and only if the Fries number of $G$ equals the number of finite faces of $G$. Next, we extend the above characterization from plane elementary bipartite graphs to plane bipartite graphs and show that the resonance graph of a plane bipartite graph $G$ is a daisy cube if and only if $G$ is weakly elementary bipartite such that each of its elementary component $G_i$ other than $K_2$ holds the property that the Fries number of $G_i$ equals the number of finite faces of $G_i$. Along the way, we provide a structural characterization for a plane elementary bipartite graph whose resonance graph is a daisy cube, and show that a Cartesian product graph is a daisy cube if and only if all of its nontrivial factors are daisy cubes.

Sub-terahertz (Sub-THz) waves possess exceptional attributes, capable of penetrating non-metallic and non-polarized materials while ensuring bio-safety. However, their practicality in imaging is marred by the emergence of troublesome speckle artifacts, primarily due to diffraction effects caused by wavelengths comparable to object dimensions. In addressing this limitation, we present the Diffuser-aided sub-THz Imaging System (DaISy), which utilizes a diffuser and a focusing lens to convert coherent waves into incoherent counterparts. The cornerstone of our progress lies in a coherence theory-based theoretical framework, pivotal for designing and validating the THz diffuser, and systematically evaluating speckle phenomena. Our experimental results utilizing DaISy reveal substantial improvements in imaging quality and nearly diffraction-limited spatial resolution. Moreover, we demonstrate a tangible application of DaISy in the scenario of security scanning, highlighting the versatile potential of sub-THz waves in miscellaneous fields.

Pulsar timing arrays have recently observed a stochastic gravitational wave background at nano-Hertz frequencies. This raises the question whether the signal can be of primordial origin. Supercooled first-order phase transitions are among the few early Universe scenarios that can successfully explain it. To further scrutinise this possibility, a precise theoretical understanding of the dynamics of the phase transition is required. Here we perform such an analysis for a dark sector with an Abelian Higgs model in the conformal limit, which is known to admit large supercooling. We compare simple analytic parametrisations of the bounce action, one-loop finite temperature calculations including Daisy resummation, and results of a dimensionally reduced (3D) effective theory including up to two-loop corrections using the DRalgo framework. Consistent renormalisation group evolution (RGE) of the couplings is essential for a meaningful interpretation of the results. We find that the 3D EFT with consistent expansion in the 4D parameters gives a significantly reduced scale dependence of the phase transition parameters. With a suitable choice of RGE scale, the 4D high temperature expanded effec

Tandem repeats in proteins identification, classification and curation is a complex process that requires manual processing from experts, processing power and time. There are recent and relevant advances applying machine learning for protein structure prediction and repeat classification that are useful for this process. However, no service contemplates required databases and software to supplement researching on repeat proteins. In this publication we present Daisy, an integrated repeat protein curation web service. This service can process Protein Data Bank (PDB) and the AlphaFold Database entries for tandem repeats identification. In addition, it uses an algorithm to search a sequence against a library of Pfam hidden Markov model (HMM). Repeat classifications are associated with the identified families through RepeatsDB. This prediction is considered for enhancing the ReUPred algorithm execution and hastening the repeat units identification process. The service can also operate every associated PDB and AlphaFold structure with a UniProt proteome registry. Availability: The Daisy web service is freely accessible at daisy.bioinformatica.org.

Self-supervised speech models have shown to be useful for various tasks, but their large size limits the use in devices with low computing power and memory. In this work, we explore early exit, an approach for reducing latency by exiting the forward process of a network early. Most approaches of early exit need a separate early exit model for each task, with some even requiring fine-tuning of the entire pretrained model. We introduce Data Adaptive Self-Supervised Early Exit (DAISY), an approach that decides when to exit based on the self-supervised loss, eliminating the need for multiple round of training and fine-tuning. DAISY matches the performance of HuBERT on the MiniSUPERB benchmark, but with much faster inference times. Our analysis on the adaptivity of DAISY shows that the model exits early (using fewer layers) on clean data while exits late (using more layers) on noisy data, dynamically adjusting the computational cost of inference based on the noise level of each sample.

We often verbally express emotions in a multifaceted manner, they may vary in their intensities and may be expressed not just as a single but as a mixture of emotions. This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of primary emotions with varying degrees of intensity. In this paper, we propose an emotional text-to-speech design to simulate a wider spectrum of emotions grounded on the structural model. Our proposed design, Daisy-TTS, incorporates a prosody encoder to learn emotionally-separable prosody embedding as a proxy for emotion. This emotion representation allows the model to simulate: (1) Primary emotions, as learned from the training samples, (2) Secondary emotions, as a mixture of primary emotions, (3) Intensity-level, by scaling the emotion embedding, and (4) Emotions polarity, by negating the emotion embedding. Through a series of perceptual evaluations, Daisy-TTS demonstrated overall higher emotional speech naturalness and emotion perceiveability compared to the baseline.

Let $G$ be a plane elementary bipartite graph whose infinite face is forcing. We provide a bijection between the set of maximal hypercubes of its resonance graph and the set of maximal resonant sets of $G$, which generalizes a main result in [MATCH Commun. Math. Comput. Chem. 68 (2012) 65-77], where $G$ was only considered as an elementary benzenoid graph without nice coronenes. For a special case when $G$ is a peripherally 2-colorable graph, it follows that there is a bijection between the set of maximal hypercubes of its resonance graph and the set of maximal independent sets of a tree that is the inner dual of $G$. We then show that the resonance graph of a plane bipartite graph $G$ is a daisy cube if and only if it is the simplex graph of the complement of a forest. Finally, we characterize trees with at most 5 maximal independent sets to determine daisy cubes that are simplex graphs of the complements of trees and having at most five maximal vertices.

We studied daisy-chained extraordinary magnetoresistance (EMR) devices based on high quality monolayer graphene encapsulated in hexagonal boron nitride (h-BN) at room temperature. The largest magnetoresistance (MR) achieved in our devices is 4.6 x 10^7 %, the record for EMR devices to date. The magnetic field sensitivity, dR/dB, reaches 104 kohm/T, exceeding the previous record set by encapsulated graphene by more than 300 %, and is comparable with state-of-the-art graphene Hall sensors at cryogenic temperatures (4.2 K). We demonstrate that daisy chaining multiple EMR devices is a new way to reach arbitrarily high sensitivity and signal-to-noise ratio, and extremely small noise equivalent field for weak magnetic field detection. Finally, we show the evidence of metal contact-induced Fermi-level pinning in the sample and its influence on graphene properties, current distribution and EMR performance. We highlight the EMR geometry as an interesting alternative to the Hall geometry for fundamental physics studies.

Daisy cubes are a recently introduced class of isometric subgraphs of hypercubes $Q_n$. They are induced with intervals between chosen vertices of $Q_n$ and the vertex $0^n\in V(Q_n)$. In this paper we characterize daisy cubes in terms of an expansion procedure thus answering an open problem proposed by Klavžar and Mollard, 2018, in the introductory paper of daisy cubes \cite{KlaMol-18}. To obtain such a characterization several interesting properties of daisy cubes are presented. For a given graph $G$ isomorphic to a daisy cube, but without the corresponding embedding into a hypercube, we present an algorithm which finds a proper embedding of $G$ into a hypercube in $O(mn)$ time. Finally, daisy graphs of a rooted graph are introduced and shown to be a generalization of daisy cubes.

Daisy graphs of a rooted graph $G$ with the root $r$ were recently introduced as a generalization of daisy cubes, a class of isometric subgraphs of hypercubes. In this paper we first solve the problem posed in \cite{Taranenko2020} and characterize rooted graphs $G$ with the root $r$ for which all daisy graphs of $G$ with respect to $r$ are isometric in $G$. We continue the investigation of daisy graphs $G$ (generated by $X$) of a Hamming graph $H$ and characterize those daisy graphs generated by $X$ of cardinality 2 that are isometric in $H$. Finally, we give a characterization of isometric daisy graphs of a Hamming graph $K_{k_1}\Box \ldots \Box K_{k_n}$ with respect to $0^n$ in terms of an expansion procedure.