Recovery from Non-Decomposable Distance Oracles

A line of work has looked at the problem of recovering an input from distance queries. In this setting, there is an unknown sequence $s \in \{0,1\}^{\leq n}$, and one chooses a set of queries $y \in \{0,1\}^{\mathcal{O}(n)}$ and receives $d(s,y)$ for a distance function $d$. The goal is to make as few queries as possible to recover $s$. Although this problem is well-studied for decomposable distances, i.e., distances of the form $d(s,y) = \sum_{i=1}^n f(s_i, y_i)$ for some function $f$, which includes the important cases of Hamming distance, $\ell_p$-norms, and $M$-estimators, to the best of our knowledge this problem has not been studied for non-decomposable distances, for which there are important special cases such as edit distance, dynamic time warping (DTW), Frechet distance, earth mover's distance, and so on. We initiate the study and develop a general framework for such distances. Interestingly, for some distances such as DTW or Frechet, exact recovery of the sequence $s$ is provably impossible, and so we show by allowing the characters in $y$ to be drawn from a slightly larger alphabet this then becomes possible. In a number of cases we obtain optimal or near-optimal query complexity. We also study the role of adaptivity for a number of different distance functions. One motivation for understanding non-adaptivity is that the query sequence can be fixed and the distances of the input to the queries provide a non-linear embedding of the input, which can be used in downstream applications involving, e.g., neural networks for natural language processing.Comment: This work has been presented at conference The 14th Innovations in Theoretical Computer Science (ITCS 2023) and accepted for publishing in the journal IEEE Transactions on Information Theor

Unconstrained Face Detection and Open-Set Face Recognition Challenge

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.Comment: This is an ERRATA version of the paper originally presented at the International Joint Conference on Biometrics. Due to a bug in our evaluation code, the results of the participants changed. The final conclusion, however, is still the sam

Biological Control of the Cucumber Downy Mildew Pathogen Pseudoperonospora cubensis

Cucumber downy mildew (CDM) is a destructive plant disease caused by the air-borne oomycete pathogen Pseudoperonospora cubensis. CDM causes severe yield reduction of cucumber and significant economic losses. Biocontrol is a promising method to control CDM with the advantage of being beneficial to sustainable agricultural development. However, until now, no reviews of biocontrol of CDM have been reported. The objective of this review is to more comprehensively understand the biocontrol of CDM. In this review, the biological characteristics of P. cubensis are introduced, and strategies for screening biocontrol agents to suppress CDM are recommended. Then the current biocontrol agents, including fungi such as Trichoderma and biocontrol bacteria such as Bacillus, which possess the ability to control CDM, and their control characteristics and ability against CDM are also summarized. The potential mechanisms by which these biocontrol agents prevent CDM are discussed. Finally, several suggestions for future research on the biocontrol of CDM are provided

Biological Control of the Cucumber Downy Mildew Pathogen <i>Pseudoperonospora cubensis</i>

Cucumber downy mildew (CDM) is a destructive plant disease caused by the air-borne oomycete pathogen Pseudoperonospora cubensis. CDM causes severe yield reduction of cucumber and significant economic losses. Biocontrol is a promising method to control CDM with the advantage of being beneficial to sustainable agricultural development. However, until now, no reviews of biocontrol of CDM have been reported. The objective of this review is to more comprehensively understand the biocontrol of CDM. In this review, the biological characteristics of P. cubensis are introduced, and strategies for screening biocontrol agents to suppress CDM are recommended. Then the current biocontrol agents, including fungi such as Trichoderma and biocontrol bacteria such as Bacillus, which possess the ability to control CDM, and their control characteristics and ability against CDM are also summarized. The potential mechanisms by which these biocontrol agents prevent CDM are discussed. Finally, several suggestions for future research on the biocontrol of CDM are provided

Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain

Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target&rsquo;s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves

A Distinctive Binary Descriptor and Two-Point RANSACWC for Point Cloud Registration

Point cloud registration is a fundamental problem in many applications. The point cloud registration based on local shape descriptor has been widely researched. In order to further improve the performance of registration, a novel registration method is proposed in this article. First, a binary descriptor is designed to establish correspondences between two point clouds. The descriptor has high descriptiveness. Thus, more correct correspondences are established. Then, a 3-D transformation estimation technique is developed, in which multiple constraints are used to accelerate the computation. When the randomly selected correspondences do not satisfy the constraints, the iteration is skipped. Finally, the experiments are performed to analyze the descriptor and 3-D transformation estimation technique. The comparison with the existing descriptors is implemented on three datasets. The results demonstrate that our descriptor has better matching performance. As for the 3-D transformation estimation technique, the combinations of the constraints are first analyzed. The performance of different constraints is presented and the best combination is chose. The comparative results with the existing techniques demonstrate that the proposed 3-D transformation estimation technique can obtain better registration accuracy and computation efficiency

Determination of Pharmacokinetics of Chrysin and Its Conjugates in Wild-Type FVB and Bcrp1 Knockout Mice Using a Validated LC-MS/MS Method

Chrysin, a flavone found in many plants, is also available as a dietary supplement because of its reported anticancer activities. However, its bioavailability is very poor due to extensive phase II metabolism. The purpose of this study was to develop an UPLC-MS/MS method to simultaneously quantify chrysin and its phase II metabolites, and to determine its pharmacokinetics in FVB wild-type and Bcrp knockout (Bcrp1 −/−) mice. In addition, the role of BCRP in chrysin phase II disposition was further investigated in Caco-2 cells. The results showed that our sensitive and reproducible UPLC-MS/MS method was successfully applied to the pharmacokinetic study of chrysin in wild-type and Bcrp1 (−/−) FVB mice after oral administration (20 mg/kg). Although there was no significant change in systemic exposure of chrysin and its metabolites, it was found that the <i>T</i>_max for chrysin glucuronide was significantly shorter (<i>p</i> < 0.01) in Bcrp1-deficient mice. Furthermore, it was shown that inhibition of BCRP by Ko143 significantly reduced the efflux of chrysin sulfate in Caco-2 cells. In conclusion, BCRP had significant but less than expected impact on pharmacokinetics of chrysin and its conjugates, which were determined using a newly developed and validated LC-MS/MS method