Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems

As one of the central tasks in machine learning, regression finds lots of applications in different fields. An existing common practice for solving regression problems is the mean square error (MSE) minimization approach or its regularized variants which require prior knowledge about the models. Recently, Yi et al., proposed a mutual information based supervised learning framework where they introduced a label entropy regularization which does not require any prior knowledge. When applied to classification tasks and solved via a stochastic gradient descent (SGD) optimization algorithm, their approach achieved significant improvement over the commonly used cross entropy loss and its variants. However, they did not provide a theoretical convergence analysis of the SGD algorithm for the proposed formulation. Besides, applying the framework to regression tasks is nontrivial due to the potentially infinite support set of the label. In this paper, we investigate the regression under the mutual information based supervised learning framework. We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique. For the proposed formulation, we give the convergence analysis of the SGD algorithm for solving it in practice. Finally, we consider a multi-output regression data model where we derive the generalization performance lower bound in terms of the mutual information associated with the underlying data distribution. The result shows that the high dimensionality can be a bless instead of a curse, which is controlled by a threshold. We hope our work will serve as a good starting point for further research on the mutual information based regression.Comment: 28 pages, 2 figures, presubmitted to AISTATS2023 for reviewin

A Noncoding RNA Modulator Potentiates Phenylalanine Metabolism in Mice

The functional role of long noncoding RNAs (lncRNAs) in inherited metabolic disorders, including phenylketonuria (PKU), is unknown. We demonstrated that the mouse lncRNA Pair and human HULC associate with phenylalanine hydroxylase (PAH). Pair-knockout mice exhibited excessive blood phenylalanine, musty odor, hypopigmentation, growth retardation, and progressive neurological symptoms including seizures, which faithfully models human PKU. HULC depletion led to reduced PAH enzymatic activities in human induced pluripotent stem cell (hiPSC)-differentiated hepatocytes. Mechanistically, HULC modulated the enzymatic activities of PAH by facilitating PAH-substrate and PAH-cofactor interactions. To develop a therapeutic strategy for restoring liver lncRNAs, we designed GalNAc-tagged lncRNA mimics that exhibit liver enrichment. Treatment with GalNAc-HULC mimics reduced excessive phenylalanine in Pair−/− and PahR408W/R408W mice and improved the phenylalanine tolerance of these mice

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Attention-Guided Image Captioning through Word Information

Image captioning generates written descriptions of an image. In recent image captioning research, attention regions seldom cover all objects, and generated captions may lack the details of objects and may remain far from reality. In this paper, we propose a word guided attention (WGA) method for image captioning. First, WGA extracts word information using the embedded word and memory cell by applying transformation and multiplication. Then, WGA applies word information to the attention results and obtains the attended feature vectors via elementwise multiplication. Finally, we apply WGA with the words from different time steps to obtain previous word guided attention (PW) and current word attention (CW) in the decoder. Experiments on the MSCOCO dataset show that our proposed WGA can achieve competitive performance against state-of-the-art methods, with PW results of a 39.1 Bilingual Evaluation Understudy score (BLEU-4) and a 127.6 Consensus-Based Image Description Evaluation score (CIDEr-D); and CW results of a 39.1 BLEU-4 score and a 127.2 CIDER-D score on a Karpathy test split

VIBRATION MODAL ANALYSIS AND RESEARCH OF CANTILEVER BEAM BASED ON POLY IIR METHOD

This paper analyses the vibration modes of a simply supported beam with vibration table. Firstly,the one dimensional vibration theory is used to acquire the beam vibration mathematical model. Following to use Bernoulli Euler beam theory to calculate the beam vibration differential equation,in addition to the beam set specific boundary constraint conditions to calculate each order modal parameters,using the Workbench beam finite element simulation analysis for the first five order natural frequency and vibration mode. In the end,the experimental modal algorithm based on Poly IIR is applied to identify the modal parameters of simply supported beam. The modal frequencies and mode shapes are obtained in this paper. It is keeping 3%about the consistency of the simulation value and experiment result,Which can subsequent beam or plate and so on EMA elastomer complex structure analysis and research to provide certain reference

Leakage Current Stability Analysis for Subthreshold SRAM

Low-power memories typically operate in the subthreshold region of the device; however, as the supply voltage continues to decrease, the impact of leakage current on SRAM stability becomes more significant. The traditional method of measuring static noise tolerance only considers the effect of voltage, and the measurement results are not accurate enough. Therefore, this paper proposes a leakage-current-based stability analysis that provides better metrics, reads current noise tolerance (RINM) and writes current noise tolerance (WINM) to measure the stability of subthreshold SRAMs. Both currents and voltages were taken into account. The results demonstrate that the method is more accurate than the conventional method under subthreshold levels

Impact of Type 2 Diabetes Mellitus on the Prognosis of Non-Small Cell Lung Cancer

Objective: Type 2 diabetes mellitus (T2DM) is the most common metabolic disease and is characterized by sustained hyperglycemia. The impact of T2DM on the survival of lung cancer patients remains controversial. The aim of this study was to investigate the associations of type 2 diabetes with lung cancer mortality. Methods: From January 2019 to January 2020, 228 patients with non-small cell lung cancer (NSCLC) staging earlier than IIIA were included. Results: In our study, we found that the overall survival (OS) and progression-free survival (PFS) of lung cancer patients with diabetes was longer than non-diabetes group. Diagnosed T2DM was associated with the prognosis of lung cancer after adjusting for age and covariates. The association between T2DM and OS was influenced by age, stage of cancer and cancer treatment, as well as whether taking metformin was associated with the OS of lung cancer. However, with the adjustment for age and covariates, the relation trended to lose statistical significance. Conclusion: T2DM is an independent prognostic factor for patients with NSCLC staging before IIIA. The patients with both NSCLC and T2DM trended to having a longer OS, possibly due to metformin