搜索结果：seeding

Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 124 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no

In high energy physics experiment trigger systems, track segment seeding is a resource consuming function and the primary reason is the computing complexity of the segment finding process. As the Moore's Law is reaching its physical limit, reducing computing complexity should be carefully considered, rather than keep piling up silicon resources. The Tiny Triplet Finder is a scheme that reduces the computing complexity of the segment seeding. As a proof of concept, a 3D track segment seeding engine core based on the Tiny Triplet Finder has been implemented and tested in a low-cost FPGA device. The seeding engine is designed to preselect and group hits (stubs) from detector layers to feed subsequent track fitting stage. The seeding engine consists of a Hough transform space for r-z view and a Tiny Triplet Finder for r-phi view to implement 3D constraints. The seeding engine is organized as a pipeline so that each hit is processed in a single clock cycle. Taking advantage of the register-like storage block scheme which enables effectively resetting of a block RAM within a single clock cycle, clearing or refreshing the seeding engine takes only one clock cycles between two events. The

We revisit the randomized seeding techniques for k-means clustering and k-GMM (Gaussian Mixture model fitting with Expectation-Maximization), formalizing their three key ingredients: the metric used for seed sampling, the number of candidate seeds, and the metric used for seed selection. This analysis yields novel families of initialization methods exploiting a lookahead principle--conditioning the seed selection to an enhanced coherence with the final metric used to assess the algorithm, and a multipass strategy to tame down the effect of randomization. Experiments show a consistent constant factor improvement over classical contenders in terms of the final metric (SSE for k-means, log-likelihood for k-GMM), at a modest overhead. In particular, for k-means, our methods improve on the recently designed multi-swap strategy, which was the first one to outperform the greedy k-means++ seeding. Our experimental analysis also shed light on subtle properties of k-means often overlooked, including the (lack of) correlations between the SSE upon seeding and the final SSE, the variance reduction phenomena observed in iterative seeding methods, and the sensitivity of the final SSE to the pool

The first "seeds" of supermassive black holes (BHs) continue to be an outstanding puzzle, and it is currently unclear whether the imprints of early seed formation survive today. Here we examine the signatures of seeding in the local Universe using five $[18~\mathrm{Mpc}]^3$ BRAHMA simulation boxes run to $z=0$. They initialize $1.5\times10^5~M_{\odot}$ BHs using different seeding models. The first four boxes initialize BHs as heavy seeds using criteria that depend on dense & metal-poor gas, Lyman-Werner radiation, gas spin, and environmental richness. The fifth box initializes BHs as descendants of lower mass seeds ($\sim10^3~M_{\odot}$) using a new stochastic seed model built in our previous work. We find that strong signatures of seeding survive in $\sim10^5-10^6~M_{\odot}$ local BHs hosted in $M_*\lesssim10^{9}~M_{\odot}$ dwarf galaxies. The signatures survive due to two reasons: 1) there is a substantial population of local $\sim10^5~M_{\odot}$ BHs that are ungrown relics of early seeds from $z\sim5-10$; 2) BH growth up to $\sim10^6~M_{\odot}$ is dominated by mergers all the way down to $z\sim0$. As the contribution from gas accretion increases, the signatures of seeding st

We apply methods of particle track reconstruction in High Energy Physics (HEP) to the search for distinct stellar populations in the Milky Way, using the Gaia EDR3 data set. This was motivated by analogies between the 3D space points in HEP detectors and the positions of stars (which are also points in a coordinate space) and the way collections of space points correspond to particle trajectories in the HEP, while collections of stars from distinct populations (such as stellar streams) can resemble tracks. Track reconstruction consists of multiple steps, the first one being seeding. In this note, we describe our implementation and results of the seeding step to the search for distinct stellar populations, and we indicate how the next steps will proceed. Our seeding method uses machine learning tools from the FAISS library, such as the k-nearest neighbors (kNN) search.

Seeded Free Electron Lasers (FELs) demonstrate a good performance and are successfully used in different user experiments in extreme ultraviolet and soft X-ray regimes. In this paper a simple modification of the seeding scenario is proposed relying on generation of two closely spaced bunches with very different properties: a low-current seeding bunch, and a high-current bunch that amplifies coherent radiation, produced by the seeding bunch. This approach eliminates different limitations and mitigates some harmful effects in the standard scenario. In particular, one can generate very high harmonic numbers with a moderate laser power in a simple high-gain harmonic generation (HGHG) scheme. Alternatively, in case of moderate harmonic numbers, one can strongly reduce the required laser power thus simplifying design of high repetition rate seeded FELs. An influence of beam dynamics effects (like nonlinearities of the longitudinal phase space of electron beams, coherent synchrotron radiation, longitudinal space charge, geometrical wakefields, microbunching instabilities etc.) on properties of output radiation (spectrum broadening, pedestals, stability) can be to a large extent reduced in

In this work, we introduce Variational Umbrella Seeding, a novel technique for computing nucleation barriers. This new method, a refinement of the original seeding approach, is far less sensitive to the choice of order parameter for measuring the size of a nucleus. Consequently, it surpasses seeding in accuracy, and Umbrella Sampling in computational speed. We test the method extensively and demonstrate excellent accuracy for crystal nucleation of nearly hard spheres and of two distinct models of water: mW and TIP4P/ICE. This method can easily be extended to calculate nucleation barriers for homogeneous melting, condensation, and cavitation.

Many machine unlearning methods have been proposed recently to uphold users' right to be forgotten. However, offering users verification of their data removal post-unlearning is an important yet under-explored problem. Current verifications typically rely on backdooring, i.e., adding backdoored samples to influence model performance. Nevertheless, the backdoor methods can merely establish a connection between backdoored samples and models but fail to connect the backdoor with genuine samples. Thus, the backdoor removal can only confirm the unlearning of backdoored samples, not users' genuine samples, as genuine samples are independent of backdoored ones. In this paper, we propose a Self-supervised Model Seeding (SMS) scheme to provide unlearning verification for genuine samples. Unlike backdooring, SMS links user-specific seeds (such as users' unique indices), original samples, and models, thereby facilitating the verification of unlearning genuine samples. However, implementing SMS for unlearning verification presents two significant challenges. First, embedding the seeds into the service model while keeping them secret from the server requires a sophisticated approach. We address

The physics of neoclassical tearing mode (NTM) is of great concern to the tokamak plasma stability and performance, especially in the burning plasma regime. Whereas a great deal about the different seeding mechanisms have been understood, and in many situations the seed event can be clearly identified, the potential seeding process of NTM due to the resistive tearing instability driven by the impurity radiation cooling still needs more studies. Recent NIMROD simulations have demonstrated that the local impurity radiation cooling can drive the seed island growth and trigger the subsequent onset of neoclassical tearing mode instability. The seed island is mainly driven by the local helical perturbation of the diamagnetic current induced by the perturbed pressure gradient as a result of the impurity radiative cooling on the rational surface. A heuristic closure for the neoclassical viscosity is adopted, and the seed island is further driven by the perturbed bootstrap current induced from the neoclassical electron viscous stress in the extended Ohm's law. The growth rate of the NTM in simulations is found proportional to the electron neoclassical viscosity, and a theoretical neoclassic

Because early black holes (BHs) grew to $\sim10^{9} ~M_\odot$ in less than 1 Gyr of cosmic time, BH seeding models face stringent constraints. To efficiently constrain the parameter space of possible seeding criteria, we combine the advantages of the cosmological IllustrisTNG (TNG) simulations with the flexibility of semi-analytic modeling. We identify TNG galaxies as BH seeding sites based on various criteria including a minimum gas mass of $10^7$-$10^9~M_\odot$, total host mass of $10^{8.5}$-$10^{10.5}~M_\odot$, and a maximum gas metallicity of $0.01 - 0.1 ~Z_\odot$. Each potential host is assigned a BH seed with a probability of $0.01 - 1$; these BHs are then traced through the TNG galaxy merger tree. This approach improves upon the predictive power of the simple TNG BH seeding prescription, especially in the low-mass regime at high redshift, and it is readily adaptable to other cosmological simulations. Most of our seed models predict $z\lesssim4$ BH mass densities that are consistent with empirical data as well as the TNG BHs. However, high-redshift BH number densities can differ by factors of $\sim$ 10 - 100 between models. In most models, $\lesssim10^5~M_\odot$ BHs substanti

I study how a startup with uncertainty over product quality and no knowledge of the underlying diffusion network optimally chooses initial seeds. To ensure widespread adoption when the product is good while minimizing negative perceptions when it is bad, the optimal number of initial seeds should grow logarithmically with network size. When there are agents of different types that govern their connectivity, it is asymptotically optimal to seed agents of a single type: the type that minimizes the marginal cost per probability of making the product go viral. These results rationalize startup behavior in practice.

It is well known that excessive harvesting or hunting has driven species to extinction both on local and global scales. This leads to one of the fundamental problems of conservation ecology: how should we harvest a population so that economic gain is maximized, while also ensuring that the species is safe from extinction? We study an ecosystem of interacting species that are influenced by random environmental fluctuations. At any point in time, we can either harvest or seed (repopulate) species. Harvesting brings an economic gain while seeding incurs a cost. The problem is to find the optimal harvesting-seeding strategy that maximizes the expected total income from harvesting minus the cost one has to pay for the seeding of various species. We consider what happens when one, or both, of the seeding and harvesting rates are bounded. The focus of this paper is the analysis of these three novel settings: bounded seeding and infinite harvesting, bounded seeding and bounded harvesting, and infinite seeding and bounded harvesting. We prove analytical results and develop numerical approximation methods. By implementing these approximations, we are able to gain qualitative information abou

In public health interventions such as distributing preexposure prophylaxis (PrEP) for HIV prevention, decision makers often use seeding algorithms to identify key individuals who can amplify intervention impact. However, building a complete sexual activity network is typically infeasible due to privacy concerns. Instead, contact tracing can provide influence samples, observed sequences of sexual contacts, without full network reconstruction. This raises two challenges: protecting individual privacy in these samples and adapting seeding algorithms to incomplete data. We study differential privacy guarantees for influence maximization when the input consists of randomly collected cascades. Building on recent advances in costly seeding, we propose privacy-preserving algorithms that introduce randomization in data or outputs and bound the privacy loss of each node. Theoretical analysis and simulations on synthetic and real-world sexual contact data show that performance degrades gracefully as privacy budgets tighten, with central privacy regimes achieving better trade-offs than local ones.

One of the most popular clustering algorithms is the celebrated $D^α$ seeding algorithm (also know as $k$-means++ when $α=2$) by Arthur and Vassilvitskii (2007), who showed that it guarantees in expectation an $O(2^{2α}\cdot \log k)$-approximate solution to the ($k$,$α$)-means cost (where euclidean distances are raised to the power $α$) for any $α\ge 1$. More recently, Balcan, Dick, and White (2018) observed experimentally that using $D^α$ seeding with $α>2$ can lead to a better solution with respect to the standard $k$-means objective (i.e. the $(k,2)$-means cost). In this paper, we provide a rigorous understanding of this phenomenon. For any $α>2$, we show that $D^α$ seeding guarantees in expectation an approximation factor of $$ O_α\left((g_α)^{2/α}\cdot \left(\frac{σ_{\mathrm{max}}}{σ_{\mathrm{min}}}\right)^{2-4/α}\cdot (\min\{\ell,\log k\})^{2/α}\right)$$ with respect to the standard $k$-means cost of any underlying clustering; where $g_α$ is a parameter capturing the concentration of the points in each cluster, $σ_{\mathrm{max}}$ and $σ_{\mathrm{min}}$ are the maximum and minimum standard deviation of the clusters around their means, and $\ell$ is the number of distinct

A coherent seeded SU(1,1) interferometer provides a prominent technique in the field of precision measurement. We theoretically study the phase sensitivity of SU(1,1) interferometer with Kerr state seeding under single intensity and homodyne detection schemes. To find the lower bound in this case we calculate the quantum Cramér-Rao bound using the quantum Fisher information technique. We found that, under some conditions, the Kerr seeding performs better in phase sensitivity compared to the well-known vacuum and coherent seeded case. We expect that the Kerr state might act as an alternative non-classical state in the field of quantum information and sensing technologies.

Differential Privacy (DP) relies on random numbers to preserve privacy, typically utilising Pseudorandom Number Generators (PRNGs) as a source of randomness. In order to allow for consistent reproducibility, testing and bug-fixing in DP algorithms and results, it is important to allow for the seeding of the PRNGs used therein. In this work, we examine the landscape of Random Number Generators (RNGs), and the considerations software engineers should make when choosing and seeding a PRNG for DP. We hope it serves as a suitable guide for DP practitioners, and includes many lessons learned when implementing seeding for diffprivlib.

We analyse a nonlinear interferometer, also known as an SU(1,1) interferometer, in the presence of internal losses and inefficient detectors. To overcome these limitations, we consider the effect of seeding one of the interferometer input modes with either a number state or a coherent state. We derive analytical expressions for the interference visibility, contrast, phase sensitivity, and signal-to-noise ratio, and show a significant enhancement in all these quantities as a function of the seeding photon number. For example, we predict that, even in the presence of substantial losses and highly inefficient detectors, we can achieve the same quantum-limited phase sensitivity of an unseeded nonlinear interferometer by seeding with a few tens of photons. Furthermore, we observe no difference between a number or a coherent seeding state when the interferometer operates in the low-gain regime, which enables seeding with an attenuated laser. Our results expand the nonlinear interferometry capabilities in the field of quantum imaging, metrology, and spectroscopy under realistic experimental conditions.

When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focus solely on maximizing the total number of reached nodes, overlooking the issue of group fairness, in particular, gender imbalance. To tackle the challenge of maximizing information spread on certain target groups, e.g., females, we introduce the concept of the community and gender-aware potential of users. We first show that the network's community structure is closely related to the gender distribution. Then, we propose an algorithm that leverages the information about community structure and its gender potential to iteratively modify a seed set such that the information spread on the target group meets the target ratio. Finally, we validate the algorithm by performing experiments on synthetic and real-world datasets. Our results show that the proposed seeding algorithm achieves not only the target ratio but also the highest information spread, compared to the state-of-the-art gender-aware seeding algorithm.

Information spreading in complex networks is often modeled as diffusing information with certain probability from nodes that possess it to their neighbors that do not. Information cascades are triggered when the activation of a set of initial nodes (seeds) results in diffusion to large number of nodes. Here, several novel approaches for seed initiation that replace the commonly used activation of all seeds at once with a sequence of initiation stages are introduced. Sequential strategies at later stages avoid seeding highly ranked nodes that are already activated by diffusion active between stages. The gain arises when a saved seed is allocated to a node difficult to reach via diffusion. Sequential seeding and a single stage approach are compared using various seed ranking methods and diffusion parameters on real complex networks. The experimental results indicate that, regardless of the seed ranking method used, sequential seeding strategies deliver better coverage than single stage seeding in about 90% of cases. Longer seeding sequences tend to activate more nodes but they also extend the duration of diffusion. Various variants of sequential seeding resolve the trade-off between