A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning

Interpolation methodologies have been widely used within the domain of indoor positioning systems. However, existing indoor positioning interpolation algorithms exhibit several inherent limitations, including reliance on complex mathematical models, limited flexibility, and relatively low precision. To enhance the accuracy and efficiency of indoor positioning interpolation techniques, this paper proposes a simple yet powerful geometric-aware interpolation algorithm for indoor positioning tasks. The key to our algorithm is to exploit the geometric attributes of the local topological manifold using manifold learning principles. Therefore, instead of constructing complicated mathematical models, the proposed algorithm facilitates the more precise and efficient estimation of points grounded in the local topological manifold. Moreover, our proposed method can be effortlessly integrated into any indoor positioning system, thereby bolstering its adaptability. Through a systematic array of experiments and comprehensive performance analyses conducted on both simulated and real-world datasets, we demonstrate that the proposed algorithm consistently outperforms the most commonly used and representative interpolation approaches regarding interpolation accuracy and efficiency. Furthermore, the experimental results also underscore the substantial practical utility of our method and its potential applicability in real-time indoor positioning scenarios

LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval

In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latency. Despite it deeply exploiting the lexicon-representing capability of pre-trained language models, a crucial gap remains between language modeling and lexicon-weighting retrieval -- the former preferring certain or low-entropy words whereas the latter favoring pivot or high-entropy words -- becoming the main barrier to lexicon-weighting performance for large-scale retrieval. To bridge this gap, we propose a brand-new pre-training framework, lexicon-bottlenecked masked autoencoder (LexMAE), to learn importance-aware lexicon representations. Essentially, we present a lexicon-bottlenecked module between a normal language modeling encoder and a weakened decoder, where a continuous bag-of-words bottleneck is constructed to learn a lexicon-importance distribution in an unsupervised fashion. The pre-trained LexMAE is readily transferred to the lexicon-weighting retrieval via fine-tuning. On the ad-hoc retrieval benchmark, MS-Marco, it achieves 42.6% MRR@10 with 45.8 QPS for the passage dataset and 44.4% MRR@100 with 134.8 QPS for the document dataset, by a CPU machine. And LexMAE shows state-of-the-art zero-shot transfer capability on BEIR benchmark with 12 datasets.Comment: Appeared at ICLR 202

Analysis of the expression pattern of the BCL11B gene and its relatives in patients with T-cell acute lymphoblastic leukemia

<p>Abstract</p> <p>Background</p> <p>In a human T-cell acute lymphoblastic leukemia (T-ALL) cell line (Molt-4), siRNA-mediated suppression of <it>BCL11B </it>expression was shown to inhibit proliferation and induce apoptosis, functions which may be related to genes involved in apoptosis (such as <it>TNFSF10 </it>and <it>BCL2L1</it>) and TGF-β pathways (such as <it>SPP1</it>and <it>CREBBP</it>).</p> <p>Methods</p> <p>The expression levels of the above mentioned genes and their correlation with the <it>BCL11B </it>gene were analyzed in patients with T-ALL using the TaqMan and SYBR Green I real-time polymerase chain reaction technique.</p> <p>Results</p> <p>Expression levels of <it>BCL11B, BCL2L1</it>, and <it>CREBBP </it>mRNA in T-ALL patients were significantly higher than those from healthy controls (<it>P <</it>0.05). In T-ALL patients, the <it>BCL11B </it>expression level was negatively correlated with the <it>BCL2L1 </it>expression level (<it>r</it>_s= -0.700; <it>P </it><it><</it>0.05), and positively correlated with the <it>SPP1 </it>expression level (<it>r</it>_s= 0.683; <it>P </it><it><</it>0.05). In healthy controls, the <it>BCL11B </it>expression level did not correlate with the <it>TNFSF10</it>, <it>BCL2L1</it>, <it>SPP1</it>, or <it>CREBBP </it>expression levels.</p> <p>Conclusions</p> <p>Over-expression of <it>BCL11B </it>might play a role in anti-apoptosis in T-ALL cells through up-regulation of its downstream genes <it>BCL2L1 </it>and <it>CREBBP</it>.</p