Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase due to the adoption of more sophisticated models and large-scale datasets. Dataset Distillation (DD) or Dataset Condensation(DC) focuses on generating smaller synthetic dataset that retains the original information. Nevertheless, existing DD methods face challenges in maintaining a trade-off between accuracy and efficiency. And the state-of-the-art dataset distillation methods can not expand to all deep hashing retrieval methods. In this paper, we propose an efficient condensation framework that addresses these limitations by matching the feature-embedding between synthetic set and real set. Furthermore, we enhance the diversity of features by incorporating the strategies of early-stage augmented models and multi-formation. Extensive experiments provide compelling evidence of the remarkable superiority of our approach, both in terms of performance and efficiency, compared to state-of-the-art baseline methods

Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects

Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visual-semantics mapping problems, and have made profound progress. Notably, this paradigm differs from existing close-set fine-grained methods and, therefore, can pose unique and nontrivial challenges. However, to the best of our knowledge, there remains a lack of systematic summaries of this topic. To enrich the literature of this domain and provide a sound basis for its future development, in this paper, we present a broad review of recent advances for fine-grained analysis in ZSL. Concretely, we first provide a taxonomy of existing methods and techniques with a thorough analysis of each category. Then, we summarize the benchmark, covering publicly available datasets, models, implementations, and some more details as a library. Last, we sketch out some related applications. In addition, we discuss vital challenges and suggest potential future directions.Comment: 9 pages, 1 figure, 4 table

Procurement with Reverse Auction and Flexible Noncompetitive Contracts

This article investigates a hybrid procurement mechanism that combines a reverse auction with flexible noncompetitive contracts. A buyer adopts such mechanism to procure multiple units of a product from a group of potential suppliers. Specifically, the buyer first offers contracts to some suppliers who, if accepting the contract, do not participate in the auction while committing to selling a unit to the buyer at the price of the subsequent auction. For the suppliers rejecting the offers, they can join the subsequent auction with the other suppliers to compete on the remaining units. When the buyer offers only one flexible noncompetitive contract, we find that the selected supplier may accept the offer regardless of whether he knows his exact cost information. Meanwhile, the buyer can benefit from offering such a contract, as opposed to solely conducting a regular reverse auction or offering a noncompetitive contract that does not allow suppliers declining offers to join the subsequent auction. Moreover, we find that the suppliers\u27 information about their own costs has a significant impact on the buyer\u27s decision. When the buyer makes multiple offers, we analyze the resulting game behavior of the selected suppliers and demonstrate that the buyer can benefit more than just offering one such contract. Therefore, the hybrid procurement mechanism can be mutually beneficial for both the buyer and the selected suppliers

Quantitative evaluation of protein–DNA interactions using an optimized knowledge-based potential

Computational evaluation of protein–DNA interaction is important for the identification of DNA-binding sites and genome annotation. It could validate the predicted binding motifs by sequence-based approaches through the calculation of the binding affinity between a protein and DNA. Such an evaluation should take into account structural information to deal with the complicated effects from DNA structural deformation, distance-dependent multi-body interactions and solvation contributions. In this paper, we present a knowledge-based potential built on interactions between protein residues and DNA tri-nucleotides. The potential, which explicitly considers the distance-dependent two-body, three-body and four-body interactions between protein residues and DNA nucleotides, has been optimized in terms of a Z-score. We have applied this knowledge-based potential to evaluate the binding affinities of zinc-finger protein–DNA complexes. The predicted binding affinities are in good agreement with the experimental data (with a correlation coefficient of 0.950). On a larger test set containing 48 protein–DNA complexes with known experimental binding free energies, our potential has achieved a high correlation coefficient of 0.800, when compared with the experimental data. We have also used this potential to identify binding motifs in DNA sequences of transcription factors (TF). The TFs in 79.4% of the known TF–DNA complexes have accurately found their native binding sequences from a large pool of DNA sequences. When tested in a genome-scale search for TF-binding motifs of the cyclic AMP regulatory protein (CRP) of Escherichia coli, this potential ranks all known binding motifs of CRP in the top 15% of all candidate sequences

In-situ synthesis of single-atom Ir by utilizing metal-organic frameworks: An acid-resistant catalyst for hydrogenation of levulinic acid to γ-valerolactone

The hydrogenation of levulinic acid (LA) to gamma-valerolactone (GVL) is a key reaction for the production of renewable chemicals and fuels, wherein acid-resistant and robust catalysts are highly desired for practical usage. Herein, an ultra-stable 0.6 wt% Ir@ZrO2@C single-atom catalyst was prepared via an in-situ synthesis approach during the assembly of UiO-66, followed by confined pyrolysis. The Ir@ZrO2@C offered not only a quantitative LA conversion and an excellent GVL selectivity (>99%), but also an unprecedented stability during recycling runs under harsh conditions (at T= 453 K, P-H2 = 40 bar in pH = 3 or pH =1 aqueous solution). By thorough spectroscopy characterizations, a well-defined structure of atomically dispersed Ir delta+ atoms onto nano-tetragonal ZrO2 confined in the amorphous carbon was identified for the Ir@ZrO2@C. The strong metal-support interaction and the confinement of the amorphous carbon account for the ultra-stability of the Ir@ZrO2@C. (C) 2019 Elsevier Inc. All rights reserved

The Extended Distribution of Baryons Around Galaxies

We summarize and reanalyze observations bearing upon missing galactic baryons, where we propose a consistent picture for halo gas in L >~ L* galaxies. The hot X-ray emitting halos are detected to 50-70 kpc, where typically, M_hot(<50 kpc) ~ 5E9 Msun, and with density n \propto r^-3/2. When extrapolated to R200, the gas mass is comparable to the stellar mass, but about half of the baryons are still missing from the hot phase. If extrapolated to 1.9-3 R200, the baryon to dark matter ratio approaches the cosmic value. Significantly flatter density profiles are unlikely for R < 50 kpc and they are disfavored but not ruled out for R > 50 kpc. For the Milky Way, the hot halo metallicity lies in the range 0.3-1 solar for R < 50 kpc. Planck measurements of the thermal Sunyaev-Zeldovich effect toward stacked luminous galaxies (primarily early-type) indicate that most of their baryons are hot, near the virial temperature, and extend beyond R200. This stacked SZ signal is nearly an order of magnitude larger than that inferred from the X-ray observations of individual (mostly spiral) galaxies with M_* > 10^11.3 Msun. This difference suggests that the hot halo properties are distinct for early and late type galaxies, possibly due to different evolutionary histories. For the cooler gas detected in UV absorption line studies, we argue that there are two absorption populations: extended halos; and disks extending to ~50 kpc, containing most of this gas, and with masses a few times lower than the stellar masses. Such extended disks are also seen in 21 cm HI observations and in simulations.Comment: 22 pages, 20 figures, 2 tables, submitted to Ap

An XMM-Newton View of the ANdromeda Galaxy as Explored in a Legacy Survey (New-ANGELS) I: the X-ray Source Catalogue

We introduce the New-ANGELS program, an XMM-Newton survey of $\sim7.2\rm~deg^2$ area around M 31, which aims to study the X-ray populations in M 31 disk and the X-ray emitting hot gas in the inner halo of M 31 up to 30 kpc. In this first paper, we report the catalogue of 4506 detected X-ray sources, and attempt to cross-identify or roughly classify them. We identify 352 single stars in the foreground, 35 globular clusters and 27 supernova remnants associated with M 31, as well as 62 AGNs, 59 galaxies, and 1 galaxy clusters in the background. We uniquely classify 236 foreground stars and 17 supersoft sources based on their X-ray colors. X-ray binaries (83 LMXBs, 1 HMXBs) are classified based on their X-ray colors and X-ray variabilities. The remaining X-ray sources either have too low S/N to calculate their X-ray colors or do not have a unique classification, so are regarded as unclassified. The X-ray source catalogue is published online. Study of the X-ray source populations and the contribution of X-ray sources in the unresolved X-ray emissions based on this catalogue will be published in companion papers.Comment: 30 pages, 12 figures. Accepted for publication in APJ