Sampling expansions associated with quaternion difference equations

Starting with a quaternion difference equation with boundary conditions, a parameterized sequence which is complete in finite dimensional quaternion Hilbert space is derived. By employing the parameterized sequence as the kernel of discrete transform, we form a quaternion function space whose elements have sampling expansions. Moreover, through formulating boundary-value problems, we make a connection between a class of tridiagonal quaternion matrices and polynomials with quaternion coefficients. We show that for a tridiagonal symmetric quaternion matrix, one can always associate a quaternion characteristic polynomial whose roots are eigenvalues of the matrix. Several examples are given to illustrate the results

Differential Compound Prioritization via Bidirectional Selectivity Push with Power

Effective in silico compound prioritization is a critical step to identify promising drug candidates in the early stages of drug discovery. Current computational methods for compound prioritization usually focus on ranking the compounds based on one property, typically activity, with respect to a single target. However, compound selectivity is also a key property which should be deliberated simultaneously so as to minimize the likelihood of undesired side effects of future drugs. In this paper, we present a novel machine-learning based differential compound prioritization method dCPPP. This dCPPP method learns compound prioritization models that rank active compounds well, and meanwhile, preferably rank selective compounds higher via a bidirectional selectivity push strategy. The bidirectional push is enhanced by push powers that are determined by ranking difference of selective compounds over multiple bioassays. Our experiments demonstrate that the new method dCPPP achieves significant improvement on prioritizing selective compounds over baseline models

Dependency of the Finite-Impulse-Response-Based Head-Related Impulse Response Model on Filter Order

Various approaches have been reported on HRIR modeling to lighten the high computation cost of the 3-D audio systems without sacrificing the quality of the rendered sounds. The performance of these HRIR models have been widely evaluated usually in terms of the objective estimation errors between the original measured HRIRs and the modeled HRIRs. However, it is still unclear how much these objective evaluation results match the psychoacoustic evaluations. In this research, an efficient finite-impulse-response (FIR) model is studied as a case study which is essentially based on the concept of the minimum-phase modeling technique. The accuracy dependency of this modeling approach on the order of FIR filter is examined with the objective estimation errors and the psychoacoustic tests. In the psychoacoustic tests, the MIT HRIR database are exploited and evaluated in terms of sound source localization difference and sound quality difference by comparing the synthesized stimuli with the measured HRIRs and those with the FIR models of different orders. Results indicated that the measured hundred-sample-length HRIRs can be sufficiently modeled by the low-order FIR model from the perceptual point of view, and provided the relationship between perceptual sound localization/quality difference and the objective estimation results that should be useful for evaluating the other HRIR modeling approaches

Traffic Impact Analysis of Urban Intersections with Comprehensive Waiting Area on Urban Intersection based on PARAMICS

AbstractTo improve the traffic operation and take advantage of the space resource, left-turn vehicles waiting area and straight vehicles waiting area are adopted separately at intersection at home and abroad, but there is not much research for using left-turn and straight waiting area together at the same intersection. Research using simulation software PARAMICS combining with programming API to simulate a specific comprehensive waiting area in Guangzhou city under nine different traffic volumes for three conditions: following control strategy, underutilizing control strategy and without control strategy. By evaluating the following four indexes: link delay, queue length, link average speed and passing vehicles, the simulation results indicate that the improving effect of following control strategy is superior to the underutilizing control strategy. Implementing comprehensive waiting area and in conjunction with the following control strategy can improve traffic operation when traffic volume is larger than the surveyed situation volume while the improvement is more effectively when traffic volume continues to increase. Setting comprehensive waiting area cannot improve intersection traffic operation but will worsen the traffic operation when traffic volume is less than the surveyed situation volume

Achieving Lightweight Federated Advertising with Self-Supervised Split Distillation

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i) there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task Matched Pair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local deployment mode and the federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.Comment: Accepted to the Trustworthy Federated Learning workshop of IJCAI2022 (FL-IJCAI22). 6 pages, 3 figures, 3 tables Old title: Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfe

PredDSMC: A predictor for driver synonymous mutations in human cancers

Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations.Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC.Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations.Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting

Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear patterns of traffic data. However, the promising results achieved on current public datasets may not be applicable to practical scenarios due to limitations within these datasets. First, the limited sizes of them may not reflect the real-world scale of traffic networks. Second, the temporal coverage of these datasets is typically short, posing hurdles in studying long-term patterns and acquiring sufficient samples for training deep models. Third, these datasets often lack adequate metadata for sensors, which compromises the reliability and interpretability of the data. To mitigate these limitations, we introduce the LargeST benchmark dataset. It encompasses a total number of 8,600 sensors in California with a 5-year time coverage and includes comprehensive metadata. Using LargeST, we perform in-depth data analysis to extract data insights, benchmark well-known baselines in terms of their performance and efficiency, and identify challenges as well as opportunities for future research. We release the datasets and baseline implementations at: https://github.com/liuxu77/LargeST

Correlation of pain with substance P and neurokinin-1 receptor in the L5–S2 spinal cord in rats with chronic nonbacterial prostatitis

The incidence of prostate pain is 90%–95% in prostatitis. The symptoms are persistent, which is prone to relapse and difficult to be cured. It seriously affects the survival and quality of life of patients. This study analyzed the correlation between pain and substance P (SP) and neurokinin-1 receptors (NK-1R) in the L5–S2 spinal cord of chronic nonbacterial prostatitis (CNP) rats, which may give a new way to explore the pathogenesis and treatment of pain in prostatitis. We randomly divided the rats into control group, 45 d group, 60 d group, and 90 d group. After making a rat model with autoimmune method, the paw withdrawal threshold (PWT) was measured, the histomorphological changes in the prostate was observed by transmission electron microscopy and light microscopy. The expression of SP and NK-1R was measured by immunohistochemistry, and the concentrations of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), and interleukin-10 (IL-10) were measured by enzyme linked immunosorbent assay (ELISA). Compared with the control group, the PWT was decreased by 34.21%, 41.90% and 64.79%, TNF-α was increased by 74.19%, 89.45% and 132.15%, IL-1β was increased by 148.88%, 181.95% and 250.74%, IL-2 was increased by 75.97%, 82.15% and 128.57% and IL-10 was increased by 31.04%, 63.28% and 212.99% in the 45 d group, 60 d group and 90 d group respectively. Microscope observation showed the structure of prostate tissue in control group was normal. However, the prostate tissue had obvious inflammatory response with the model extension. The expressions of SP and NK-1R in each model group were significantly higher than the control group. There was a significant correlation between pain and SP in L5–S2 spinal cord in CNP rats. These findings are indicative of a correlation between pain and the expression levels of SP and NK-1R in the L5–S2 spinal cord of CNP rats