Constrained Approximate Similarity Search on Proximity Graph

Search engines and recommendation systems are built to efficiently display relevant information from those massive amounts of candidates. Typically a three-stage mechanism is employed in those systems: (i) a small collection of items are first retrieved by (e.g.,) approximate near neighbor search algorithms; (ii) then a collection of constraints are applied on the retrieved items; (iii) a fine-grained ranking neural network is employed to determine the final recommendation. We observe a major defect of the original three-stage pipeline: Although we only target to retrieve $k$ vectors in the final recommendation, we have to preset a sufficiently large $s$ ($s > k$) for each query, and ``hope'' the number of survived vectors after the filtering is not smaller than $k$. That is, at least $k$ vectors in the $s$ similar candidates satisfy the query constraints. In this paper, we investigate this constrained similarity search problem and attempt to merge the similarity search stage and the filtering stage into one single search operation. We introduce AIRSHIP, a system that integrates a user-defined function filtering into the similarity search framework. The proposed system does not need to build extra indices nor require prior knowledge of the query constraints. We propose three optimization strategies: (1) starting point selection, (2) multi-direction search, and (3) biased priority queue selection. Experimental evaluations on both synthetic and real data confirm the effectiveness of the proposed AIRSHIP algorithm. We focus on constrained graph-based approximate near neighbor (ANN) search in this study, in part because graph-based ANN is known to achieve excellent performance. We believe it is also possible to develop constrained hashing-based ANN or constrained quantization-based ANN

Asymmetric Hashing for Fast Ranking via Neural Network Measures

Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching measures (including complex measures such as neural network measures). However, since these ANN approaches must go through the neural measures several times during ranking, the computation is not practical if the neural measure is a large network. On the other hand, fast item ranking using existing hashing-based approaches, such as Locality Sensitive Hashing (LSH), only works with a limited set of measures. Previous learning-to-hash approaches are also not suitable to solve the fast item ranking problem since they can take a significant amount of time and computation to train the hash functions. Hashing approaches, however, are attractive because they provide a principle and efficient way to retrieve candidate items. In this paper, we propose a simple and effective learning-to-hash approach for the fast item ranking problem that can be used for any type of measure, including neural network measures. Specifically, we solve this problem with an asymmetric hashing framework based on discrete inner product fitting. We learn a pair of related hash functions that map heterogeneous objects (e.g., users and items) into a common discrete space where the inner product of their binary codes reveals their true similarity defined via the original searching measure. The fast ranking problem is reduced to an ANN search via this asymmetric hashing scheme. Then, we propose a sampling strategy to efficiently select relevant and contrastive samples to train the hashing model. We empirically validate the proposed method against the existing state-of-the-art fast item ranking methods in several combinations of non-linear searching functions and prominent datasets

Low distortion reversible database watermarking based on hybrid intelligent algorithm

In many fields, such as medicine and the computer industry, databases are vital in the process of information sharing. However, databases face the risk of being stolen or misused, leading to security threats such as copyright disputes and privacy breaches. Reversible watermarking techniques ensure the ownership of shared relational databases, protect the rights of data owners and enable the recovery of original data. However, most of the methods modify the original data to a large extent and cannot achieve a good balance between protection against malicious attacks and data recovery. In this paper, we proposed a robust and reversible database watermarking technique using a hash function to group digital relational databases, setting the data distortion and watermarking capacity of the band weight function, adjusting the weight of the function to determine the watermarking capacity and the level of data distortion, using firefly algorithms (FA) and simulated annealing algorithms (SA) to improve the efficiency of the search for the location of the watermark embedded and, finally, using the differential expansion of the way to embed the watermark. The experimental results prove that the method maintains the data quality and has good robustness against malicious attacks

Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related product categories. Given a brand, to come up with themes for a new ad is a painstaking and time consuming process for creative strategists. Strategists typically draw inspiration from the images and text used for past ad campaigns, as well as world knowledge on the brands. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. The theme recommender is based on aggregating results from a visual question answering (VQA) task, which ingests the following: (i) ad images, (ii) text associated with the ads as well as Wikipedia pages on the brands in the ads, and (iii) questions around the ad. We leverage transformer based cross-modality encoders to train visual-linguistic representations for our VQA task. We study two formulations for the VQA task along the lines of classification and ranking; via experiments on a public dataset, we show that cross-modal representations lead to significantly better classification accuracy and ranking precision-recall metrics. Cross-modal representations show better performance compared to separate image and text representations. In addition, the use of multimodal information shows a significant lift over using only textual or visual information.Comment: 7 pages, 8 figures, 2 tables, accepted by The Web Conference 202

Heritability enrichment of immunoglobulin G N-glycosylation in specific tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson’s disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases

Association between prophylactic hydration volume and risk of contrast-induced nephropathy after emergent percutaneous coronary intervention

Background: Intravenous hydration during percutaneous coronary intervention (PCI) significantly reduces the risk of contrast-induced nephropathy (CIN), but there are no well-defined protocols regard¬ing the optimal hydration volume (HV) required to prevent CIN following emergent PCI. Therefore, this study investigates the association between the intravenous HV and CIN after emergent PCI. Methods: 711 patients were prospectively recruited who had underwent emergent PCI with hydration at routine speed and the relationship was investigated between HV or HV to weight ratio (HV/W) and the CIN risk, which was defined as a ≥ 25% or ≥ 0.5 mg/dL increase in serum creatinine levels from baseline within 48–72 h of exposure to the contrast. Results: The overall CIN incidence was 24.7%. Patients in the higher HV quartiles had elevated CIN rates. Multivariate analysis showed that higher HV/W ratios were not associated with a decreased risk (using the HV) of CIN, but they were associated with an increased risk (using the HV/W) of CIN (Q4 vs. Q1: adjusted odds ratio 1.99; 95% confidence interval 1.05–3.74; p = 0.034). A higher HV/W ratio was not significantly associated with a reduced risk of long-term death (all p > 0.05). Conclusions: The data suggests that a higher total HV is not associated with a decreased CIN risk or beneficial long-term prognoses, and that excessive HV may increase the risk of CIN after emergent PCI

Intraoperative and postoperative short-term outcomes of intracorporeal anastomosis versus extracorporeal anastomosis in laparoscopic right hemicolectomy

BackgroundIntracorporeal anastomosis (IA) is a difficult but popular anastomotic approach for reconstruction of digestive tract after laparoscopic right hemicolectomy, which may reduce some limitations faced during extracorporeal anastomosis (EA).MethodsA retrospective review of 78 patients who underwent laparoscopic right hemicolectomy by a veteran surgeon in a high-volume public tertiary hospital, including 50 patients with IA and 28 patients with EA. The intraoperative-related factors and short-term results of the two anastomotic approaches were compared.ResultsThere was no significant difference in demographics and clinical characteristics between the two groups (P>0.05). The intraoperative blood loss was less (P=0.010) and the incision length was shorter (P<0.001) in the intracorporeal group. Postoperative farting time was faster (P=0.005) and postoperative pain score (VAS) was lower (P<0.001) in IA group. Although the anastomotic time of IA was shorter (P<0.001), the operative time of the two groups were similar. And number of lymph nodes harvested, NLR from POD1 to POD3, postoperative hospital stay and overall hospital stay between the two groups were comparable. Except for significant difference in abdominal infection rate, the Clavien-Dindo classification and the incidence of other postoperative complications were not statistically different. Moreover, the morbidity of abdominal infection decreased with time in the IA group (P=0.040).ConclusionIA is a reliable and feasible procedure, which has faster anastomotic time, earlier return of bowel function and superior postoperative comfort of patient, compared to EA. The postoperative complication rate of IA is similar to that of EA, and may be improved with the IA technical maturity of surgeons, which potentially contributes to the development of ERAS

A joint numerical study of multi-regime turbulent combustion

This article presents a joint numerical study on the Multi Regime Burner configuration. The burner design consists of three concentric inlet streams, which can be operated independently with different equivalence ratios, allowing the operation of stratified flames characterized by different combustion regimes, including premixed, non-premixed, and multi-regime flame zones. Simulations were performed on three LES solvers based on different numerical methods. Combustion kinetics were simplified by using tabulated or reduced chemistry methods. Finally, different turbulent combustion modeling strategies were employed, covering geometrical, statistical, and reactor based approaches. Due to this significant scattering of simulation parameters, a conclusion on specific combustion model performance is impossible. However, with ten numerical groups involved in the numerical simulations, a rough statistical analysis is conducted: the average and the standard deviation of the numerical simulation are computed and compared against experiments. This joint numerical study is therefore a partial illustration of the community's ability to model turbulent combustion. This exercise gives the average performance of current simulations and identifies physical phenomena not well captured today by most modeling strategies. Detailed comparisons between experimental and numerical data along radial profiles taken at different axial positions showed that the temperature field is fairly well captured up to 60 mm from the burner exit. The comparison reveals, however, significant discrepancies regarding CO mass fraction prediction. Three causes may explain this phenomenon. The first reason is the higher sensitivity of carbon monoxide to the simplification of detailed chemistry, especially when multiple combustion regimes are encountered. The second is the bias introduced by artificial thickening, which overestimates the species’ mass production rate. This behavior has been illustrated by manufacturing mean thickened turbulent flame brush from a random displacement of 1-D laminar flame solutions. The last one is the influence of the subgrid-scale flame wrinkling on the filtered chemical flame structure, which may be challenging to model.</p