Cardinality Estimation in Inner Product Space

This article addresses the problem of cardinality estimation in inner product spaces. Given a set of high-dimensional vectors, a query, and a threshold, this problem estimates the number of vectors such that their inner products with the query are not less than the threshold. This is an important problem for recent machine-learning applications that maintain objects, such as users and items, by using matrices. The important requirements for solutions of this problem are high efficiency and accuracy. To satisfy these requirements, we propose a sampling-based algorithm. We build trees of vectors via transformation to a Euclidean space and dimensionality reduction in a pre-processing phase. Then our algorithm samples vectors existing in the nodes that intersect with a search range on one of the trees. Our algorithm is surprisingly simple, but it is theoretically and practically fast and effective. We conduct extensive experiments on real datasets, and the results demonstrate that our algorithm shows superior performance compared with existing techniques.Hirata K., Amagata D., Hara T.. Cardinality Estimation in Inner Product Space. IEEE Open Journal of the Computer Society 3, 208 (2022); https://doi.org/10.1109/OJCS.2022.3215206

Categorical Diversity-Aware Inner Product Search

The problem of maximum inner product search (MIPS) is one of the most important components in machine learning systems. However, this problem does not care about diversity, although result diversification can improve user satisfaction. This paper hence considers a new problem, namely the categorical diversity-aware IPS problem, in which users can select preferable categories. Exactly solving this problem needs O(n) time, where n is the number of vectors, and is not efficient for large n. We hence propose an approximation algorithm that has a probabilistic success guarantee and runs in sub-linear time to n. We conduct extensive experiments on real datasets, and the results demonstrate the superior performance of our algorithm to that of a baseline using an existing MIPS technique.Hirata K., Amagata D., Fujita S., et al. Categorical Diversity-Aware Inner Product Search. IEEE Access 11, 2586 (2023); https://doi.org/10.1109/ACCESS.2023.3234072

The Combination of Gemcitabine, Cisplatin, and Paclitaxel as Salvage Chemotherapy for Advanced Urothelial Carcinoma

There is no standard second-line or salvage treatment for advanced urothelial carcinoma (UC). Here we investigated the efficacy and safety of gemcitabine, cisplatin, and paclitaxel (GCP) combination chemotherapy as salvage chemotherapy for advanced UC. We retrospectively analyzed the cases of 23 patients with advanced UC who showed progression or recurrence after cisplatin-based chemotherapy. Gemcitabine (1000 mg/m2), and paclitaxel (80 mg/m2) were administered on days 1 and 8. Cisplatin (70 mg/m2) was administered on day 1. The 3-week cycle regimen was repeated until disease progression if it had no intolerable toxicity. The overall response rate was 61% (95%CI, 41-78%). The median overall survival and progression-free survival times were 14 months and 5.5 months, respectively. Of the already known risk factors of chemotherapy for advanced UC, only the performance status was a prognostic factor for OS. Overall, 16 of the 23 patients (70%) experienced grade 3/4 toxicities, and no fatal adverse events were observed. GCP therapy was a promising option as second-line or salvage therapy for advanced UC

Mutual Effects of Orexin and Bone Morphogenetic Proteins on Gonadotropin Expression by Mouse Gonadotrope Cells

Orexin plays a key role in the regulation of sleep and wakefulness and in feeding behavior in the central nervous system, but its receptors are expressed in various peripheral tissues including endocrine tissues. In the present study, we elucidated the effects of orexin on pituitary gonadotropin regulation by focusing on the functional involvement of bone morphogenetic proteins (BMPs) and clock genes using mouse gonadotrope L beta T2 cells that express orexin type 1 (OX1R) and type 2 (OX2R) receptors. Treatments with orexin A enhanced LH beta and FSH beta mRNA expression in a dose-dependent manner in the absence of GnRH, whereas orexin A in turn suppressed GnRH-induced gonadotropin expression in L beta T2 cells. Orexin A downregulated GnRH receptor expression, while GnRH enhanced OX1R and OX2R mRNA expression. Treatments with orexin A as well as GnRH increased the mRNA levels of Bmal1 and Clock, which are oscillational regulators for gonadotropin expression. Of note, treatments with BMP-6 and -15 enhanced OX1R and OX2R mRNA expression with upregulation of clock gene expression. On the other hand, orexin A enhanced BMP receptor signaling of Smad1/5/9 phosphorylation through upregulation of ALK-2/BMPRII among the BMP receptors expressed in L beta T2 cells. Collectively, the results indicate that orexin regulates gonadotropin expression via clock gene expression by mutually interacting with GnRH action and the pituitary BMP system in gonadotrope cells