391 research outputs found
Random Tur\'an and counting results for general position sets over finite fields
Let denote the maximum size of a general position
set in a -random subset of . We determine the order of
magnitude of up to polylogarithmic factors for all
possible values of , improving the previous best upper bounds obtained by
Roche-Newton--Warren and Bhowmick--Roche-Newton. For we prove upper
bounds for that are essentially tight within certain
intervals of .
We establish the upper bound for the number of general
position sets in , which matches the trivial lower bound
asymptotically in exponent. We also refine this counting result by
proving an asymptotically tight (in exponent) upper bound for the number of
general position sets with fixed size. The latter result for improves a
result of Roche-Newton--Warren.
Our proofs are grounded in the hypergraph container method, and additionally,
for we also leverage the pseudorandomness of the point-line incidence
bipartite graph of .Comment: 24 pages(+2 pages for Appendix), 2 figure
Matching-based Data Valuation for Generative Model
Data valuation is critical in machine learning, as it helps enhance model
transparency and protect data properties. Existing data valuation methods have
primarily focused on discriminative models, neglecting deep generative models
that have recently gained considerable attention. Similar to discriminative
models, there is an urgent need to assess data contributions in deep generative
models as well. However, previous data valuation approaches mainly relied on
discriminative model performance metrics and required model retraining.
Consequently, they cannot be applied directly and efficiently to recent deep
generative models, such as generative adversarial networks and diffusion
models, in practice. To bridge this gap, we formulate the data valuation
problem in generative models from a similarity-matching perspective.
Specifically, we introduce Generative Model Valuator (GMValuator), the first
model-agnostic approach for any generative models, designed to provide data
valuation for generation tasks. We have conducted extensive experiments to
demonstrate the effectiveness of the proposed method. To the best of their
knowledge, GMValuator is the first work that offers a training-free, post-hoc
data valuation strategy for deep generative models
Efficient simulation of open quantum systems coupled to a reservoir through multiple channels
The simulation of open quantum systems coupled to a reservoir through
multiple channels remains a substantial challenge. This kind of open quantum
system arises when considering the radiationless decay of excited states that
are coupled to molecular vibrations, for example. We use the chain mapping
strategy in the interaction picture to study systems linearly coupled to a
harmonic bath through multiple interaction channels. In the interaction
picture, the bare bath Hamiltonian is removed by a unitary transformation (the
system-bath interactions remain), and a chain mapping transforms the bath modes
to a new basis. The transformed Hamiltonian contains time-dependent local
system-bath couplings. The open quantum system is coupled to a limited number
of (transformed) bath modes in the new basis. As such, the entanglement
generated by the system-bath interactions is local, making efficient dynamical
simulations possible with matrix product states. We use this approach to
simulate singlet fission, using a generalized spin-boson Hamiltonian. The
electronic states are coupled to a vibrational bath both diagonally and
off-diagonally. This approach generalizes the chain mapping scheme to the case
of multi-channel system-bath couplings, enabling the efficient simulation of
this class of open quantum systems using matrix product states.Comment: 6 pages, 4 figure
Context-Based Dynamic Pricing with Online Clustering
We consider a context-based dynamic pricing problem of online products which
have low sales. Sales data from Alibaba, a major global online retailer,
illustrate the prevalence of low-sale products. For these products, existing
single-product dynamic pricing algorithms do not work well due to insufficient
data samples. To address this challenge, we propose pricing policies that
concurrently perform clustering over products and set individual pricing
decisions on the fly. By clustering data and identifying products that have
similar demand patterns, we utilize sales data from products within the same
cluster to improve demand estimation and allow for better pricing decisions. We
evaluate the algorithms using the regret, and the result shows that when
product demand functions come from multiple clusters, our algorithms
significantly outperform traditional single-product pricing policies. Numerical
experiments using a real dataset from Alibaba demonstrate that the proposed
policies, compared with several benchmark policies, increase the revenue. The
results show that online clustering is an effective approach to tackling
dynamic pricing problems associated with low-sale products. Our algorithms were
further implemented in a field study at Alibaba with 40 products for 30
consecutive days, and compared to the products which use business-as-usual
pricing policy of Alibaba. The results from the field experiment show that the
overall revenue increased by 10.14%
Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study
Rationale & objectiveA causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.Methods20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran’s Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.ResultsOur current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.ConclusionConsistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN
DeepC2: AI-powered Covert Botnet Command and Control on OSNs
Botnets are one of the major threats to computer security. In previous botnet
command and control (C&C) scenarios using online social networks (OSNs),
methods for addressing (e.g., IDs, links, or DGAs) are hardcoded into bots.
Once a bot is reverse engineered, the botmaster and C&C infrastructure will be
exposed. Additionally, abnormal content from explicit commands may expose
botmasters and raise anomalies on OSNs. To overcome these deficiencies, we
proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural
networks, bots can find botmasters by avatars, which are converted into feature
vectors and embedded into bots. Adversaries cannot infer botmasters' accounts
from the vectors. Commands are embedded into normal contents (e.g., tweets and
comments) using text data augmentation and hash collision. Experiments on
Twitter show that command-embedded contents can be generated efficiently, and
bots can find botmasters and obtain commands accurately. Security analysis on
different scenarios show that DeepC2 is robust and hard to be shut down. By
demonstrating how AI may help promote covert communication on OSNs, this work
provides a new perspective on botnet detection and confrontation.Comment: 13 pages, 15 figures, 7 tables. Discussion on possible
countermeasures update
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Latest diffusion-based methods for many image restoration tasks outperform
traditional models, but they encounter the long-time inference problem. To
tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM) with an
Efficient Conditional Sampling (ECS) strategy. WaveDM learns the distribution
of clean images in the wavelet domain conditioned on the wavelet spectrum of
degraded images after wavelet transform, which is more time-saving in each step
of sampling than modeling in the spatial domain. In addition, ECS follows the
same procedure as the deterministic implicit sampling in the initial sampling
period and then stops to predict clean images directly, which reduces the
number of total sampling steps to around 5. Evaluations on four benchmark
datasets including image raindrop removal, defocus deblurring, demoir\'eing,
and denoising demonstrate that WaveDM achieves state-of-the-art performance
with the efficiency that is comparable to traditional one-pass methods and over
100 times faster than existing image restoration methods using vanilla
diffusion models
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