CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction

Non-communicable disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models cannot provide causal interpretable predictions and estimate treatment effects, limiting their decision-making perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncover causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by the treatment of descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions. Source code is in \href{https://github.com/DanielSun94/CFPA}{here}.Comment: 25 pages, 5 figures, 12 table

Spintronic Sources of Ultrashort Terahertz Electromagnetic Pulses

Spintronic terahertz emitters are novel, broadband and efficient sources of terahertz radiation, which emerged at the intersection of ultrafast spintronics and terahertz photonics. They are based on efficient spin-current generation, spin-to-charge-current and current-to-field conversion at terahertz rates. In this review, we address the recent developments and applications, the current understanding of the physical processes as well as the future challenges and perspectives of broadband spintronic terahertz emitters

NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

The rise of deep neural networks provides an important driver in optimizing recommender systems. However, the success of recommender systems lies in delicate architecture fabrication, and thus calls for Neural Architecture Search (NAS) to further improve its modeling. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the data multi-modality and architecture heterogeneity challenges in recommendation domain, NASRec establishes a large supernet (i.e., search space) to search the full architectures, with the supernet incorporating versatile operator choices and dense connectivity minimizing human prior for flexibility. The scale and heterogeneity in NASRec impose challenges in search, such as training inefficiency, operator-imbalance, and degraded rank correlation. We tackle these challenges by proposing single-operator any-connection sampling, operator-balancing interaction modules, and post-training fine-tuning. Our results on three Click-Through Rates (CTR) prediction benchmarks show that NASRec can outperform both manually designed models and existing NAS methods, achieving state-of-the-art performance

Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale

Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle. In this paper, we present Rankitect, a NAS software framework for ranking systems at Meta. Rankitect seeks to build brand new architectures by composing low level building blocks from scratch. Rankitect implements and improves state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under the same search space, including sampling-based NAS, one-shot NAS, and Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple production ranking models at Meta. We find that Rankitect can discover new models from scratch achieving competitive tradeoff between Normalized Entropy loss and FLOPs. When utilizing search space designed by engineers, Rankitect can generate better models than engineers, achieving positive offline evaluation and online A/B test at Meta scale.Comment: Wei Wen and Kuang-Hung Liu contribute equall

Skeletal muscle O-GlcNAc transferase is important for muscle energy homeostasis and whole-body insulin sensitivity

Objective: Given that cellular O-GlcNAcylation levels are thought to be real-time measures of cellular nutrient status and dysregulated O-GlcNAc signaling is associated with insulin resistance, we evaluated the role of O-GlcNAc transferase (OGT), the enzyme that mediates O-GlcNAcylation, in skeletal muscle. Methods: We assessed O-GlcNAcylation levels in skeletal muscle from obese, type 2 diabetic people, and we characterized muscle-specific OGT knockout (mKO) mice in metabolic cages and measured energy expenditure and substrate utilization pattern using indirect calorimetry. Whole body insulin sensitivity was assessed using the hyperinsulinemic euglycemic clamp technique and tissue-specific glucose uptake was subsequently evaluated. Tissues were used for histology, qPCR, Western blot, co-immunoprecipitation, and chromatin immunoprecipitation analyses. Results: We found elevated levels of O-GlcNAc-modified proteins in obese, type 2 diabetic people compared with well-matched obese and lean controls. Muscle-specific OGT knockout mice were lean, and whole body energy expenditure and insulin sensitivity were increased in these mice, consistent with enhanced glucose uptake and elevated glycolytic enzyme activities in skeletal muscle. Moreover, enhanced glucose uptake was also observed in white adipose tissue that was browner than that of WT mice. Interestingly, mKO mice had elevated mRNA levels of Il15 in skeletal muscle and increased circulating IL-15 levels. We found that OGT in muscle mediates transcriptional repression of Il15 by O-GlcNAcylating Enhancer of Zeste Homolog 2 (EZH2). Conclusions: Elevated muscle O-GlcNAc levels paralleled insulin resistance and type 2 diabetes in humans. Moreover, OGT-mediated signaling is necessary for proper skeletal muscle metabolism and whole-body energy homeostasis, and our data highlight O-GlcNAcylation as a potential target for ameliorating metabolic disorders. Keywords: O-GlcNAc signaling, Type 2 diabetes, N-acetyl-d-glucosamine, Tissue cross talk, Epigenetic regulation of Il15 transcription, Insulin sensitivit

Optimal mean-variance portfolio selection with mean-field reinforcement learning

We study the mean-variance portfolio selection problem which is important in the finance field. The objective of the mean-variance portfolio selection problem is to find an optimal allocation strategy that achieves a great balance between expected return and risk. Because of the non-separable variance term, it is challenging to directly utilize dynamic programming or standard reinforcement learning to solve the problem. In this work, we construct a novel mean-field reinforcement learning framework to find the optimal strategy of the multi-period mean-variance portfolio problem in the discrete time-space. We first build a mean-field formulation of the mean-variance portfolio selection problem for mean-field reinforcement learning. After that, we propose and implement the multiple-period mean-field Q-learning with function approximation algorithm to obtain the optimal strategies. We design the linear quadratic Q-functions that fit the objective function and discrete time-space of the problem. we also per- form evaluations in various parameter settings to demonstrate the effectiveness of our proposed mean-field reinforcement learning framework.Bachelor of Science in Mathematical Science

Construction of biorthogonal multiwavelets

AbstractThere are perfect formulas for the constructions of biorthogonal uniwavelets. Let φ(x)=∑k∈Zpkφ(2x−k),φ̃(x)=∑k∈Zp̃kφ̃(2x−k) be a pair of biorthogonal uniscaling functions, then a pair of biorthogonal uniwavelet associated with the above biorthogonal uniscaling functions can be easily expressed as ψ(x)=∑k∈Z(−1)k−1p̃1−kφ(2x−k),ψ̃(x)=∑k∈Z(−1)k−1p1−kφ̃(2x−k). However, it seems that there is not such a good formula of similar structure for biorthogonal multiwavelets. In this paper, we will give a procedure for constructing compactly supported biorthogonal multiwavelets, which makes construction of biorthogonal multiwavelets easy like in the construction of biorthogonal uniwavelet. Our approach is also suitable for the case of compactly supported orthogonal multiwavelets. Four examples for constructing multiwavelets are given

Optimization of the Heat Dissipation Performance of a Lithium-Ion Battery Thermal Management System with CPCM/Liquid Cooling

In view of the harsh conditions of rapid charging and discharging of electric vehicles, a hybrid lithium-ion battery thermal management system combining composite phase change material (PCM) with liquid cooling was proposed. Based on the numerical heat transfer model, a simulation experiment for the battery thermal management system was carried out. Taking the maximum temperature and temperature difference of the battery module as the objectives, the effects of PCM thickness, the liquid flow rate and the cross-sectional area of the liquid channel on the temperature of the battery module were analyzed using response surface methodology (RSM). The results show that 31 groups of candidate parameter combinations can be obtained through response surface analysis, and phase change material (PCM) thickness should be minimized in order to improve space utilization in the battery module. The optimal parameter combination is a flow rate of 0.4 m/s and a PCM thickness of 5.58 mm, with the cross-sectional area of the liquid channel as 3.35 mm2