Miracle-dz/UniFAR-FTUs huggingface dataset:
SpliceCannon: A novel framework for the prediction of canonical and non-canonical splice sites based on deep learning

Accurately predicting splice sites in DNA sequences is not only of utmost importance in the study of gene expression and protein synthesis, but also crucial to precisely understanding the pathogenesis of various diseases related to splicing. In the past few decades, several tools for identifying splice sites have been developed, however, many of them ignore the contextual influence in DNA sequences in the process of encoding, consequently leading to considerable false positives in prediction. Additionally, most methods solely investigate canonical splicing whereas pay little attention to non-canonical ones. To address the issues mentioned above, we propose a comprehensive method, SpliceCannon, for accurately predicting both canonical and non-canonical splice sites based on neural networks. This method uses the combination of k-nucleotide frequencies and Doc2vec to encode the sequence, then employs a series of comprehensive neural networks such as bidirectional long short-term memory networks and residual networks to effectively extract features from the original sequence while avoiding the problems such as gradient vanishing. The results demonstrate that our method consistently achieves high accuracy when tested on multiple species. Compared to other recent methods, our model exhibits superior performance, stronger generalization ability, and wider applicability. In particular, The recognition accuracy is also remarkably high on the non-canonical dataset. We envision that this proposed method will serve as a promising tool for splice sites prediction to facilitate genomic research.
A Family of Hyper-parameter Estimators for Regularized Linear System Identification

Hyper-parameter estimation is one of the fundamental issues for kernel-based regularized system identification methods. Empirical Bayes (EB) estimator and Stein's unbiased risk estimator (SURE) are two popular hyper-parameter estimators, but they both have advantages and disadvantages. Specifically, EB is not asymptotically optimal in the mean squared error (MSE) sense but SURE is, while SURE is more sensitive to ill-conditioned regression matrix but EB is more robust. In this paper, to find a better estimator by combining their strength and mitigating their weakness, we propose a family of hyper-parameter estimators by linking EB and SURE estimators together through an index. The finite sample and asymptotic properties of this family of estimators have been established. The Monte Carlo simulation results show that there does exist a ‘middle’ hyper-parameter estimator in this family that is superior to the EB and SURE.
PWA Control Functions: From the Projection of mpQP Solution and Back to the Convexification by Lifting

This paper focuses on the geometric properties of the Piece-Wise Affine (PWA) feedback function as they appear from the optimal solution of the multi-parameter quadratic programming (mpQP) problem. Such optimization problems are popular formulations, for example, in the design of model-based predictive controllers (MPC) for discrete linear systems subject to input and state constraints. The paper considers such a PWA function as input data and provides a method for reconstructing a feasible convex set and a PWA curve within it, which retrieves the identical structure of the solution in the original parametric feasible set. The proposed method involves establishing and decomposing the topology structure of the polyhedral critical regions, which form the domain of the PWA function by means of a graph of interconnections. The regions are split into the boundary and interior collections using convex-concave lifting. The explicit solution is then merged based on the convex-concave liftings to reconstruct the feasible domain and the PWA curves.
Adversarial U-Network for Predicting Blood Oxygen Level-Dependent Time Series

Functional magnetic resonance imaging (fMRI) plays a vital role in brain science as it measures and maps brain activity through the analysis of blood flow changes, offering valuable insights into cognitive functions and neural processes. However, due to the intricacy and dynamism of brain activity, conventional approaches failed to accurately predict the blood oxygen level-dependent (BOLD) time series in fMRI data. To tackle this issue, we proposed an end-to-end adversarial U-network (AUN) to verify the predictability of existing BOLD signals in primary cortex (i.e., primary visual, primary motor and primary sensory) and higher cortex(i.e., dorsolateral prefrontal and posterior cingulate). The model combined the U-network architecture and adversarial strategy to ensure that the predicted results capture both the intricate nonlinear details and the overall distribution characteristics. We performed the experiment using the Human Connectome Project (HCP) database. The results demonstrated the predictability of both primary and higher cortex, with primary cortex showing higher predictability. Additionally, the AUN performed better than other popular methods. We also found the improvement in dynamic functional connectivity (dFC) metrics through accurate prediction. The above results confirm the feasibility of predicting BOLD signals and their potential application in clinical settings.1
Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of Computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer.
Performance Comparison Between S-Band And C-Band Radar In Quantitative Precipitation Estimation

Meteorological radar has evolved into an indispensable instrument for monitoring precipitation and researching its correlation with hydrological response. Weather radar has often been employed to give precipitation inputs to flood forecasting operations for various river basins throughout the globe. The meteorological radar sends electromagnetic waves, which interact with raindrops and reflect a portion of the intercepted power. The frequency of the electromagnetic waves utilized depends on the application of the radar. In Malaysia, three types of common radar bands have been used to estimate the precipitation at various distances: S-band, C-band, and X-band radars. The difference lies in the wavelength of the emitted electromagnetic waves. The S-band radars have the longest wavelength, while the C-Band radars are typically designed for short wavelengths, although they may also be utilized for medium and long-range precipitation analysis. This study is to investigate the difference in performance of the S-band and C-band in quantitative precipitation estimates (QPE) based on point and mean rainfall analyses for two different case study river basins. The comparison between both radar bands shows that the C-band radar has a higher correlation between the radar QPE (indirectly estimated from the reflectivity) and gauge rainfall with higher R value. However, the study found that the radar QPE from C-band radar has larger error indicated by the higher discrepancy ratio as compared to the radar QPE from the S-band radar.
A Review of Deep Learning-Based Super-Resolution

Super-resolution technology generally refers to the use of known information to infer more vivid and detailed images. Due to their wide range of applications, they have consistently garnered significant attention and discussion in the field of image vision. This paper aims to give a comprehensive overview of the super-resolution reconstruction technology under the framework of deep learning, divide it into five categories according to the network model and expand the overview. In addition, it also organizes the commonly used data sets and evaluation indicators for training super-resolution algorithms, and summarizes the influence of data sets on these algorithms. Finally, this paper points out the challenges and limitations of the current super-resolution technology, and looks forward to the future.
Named entity recognition of Traditional Chinese Medicine cases based on RoBERTa-BiLSTM-CRF

Named entity recognition of Traditional Chinese Medicine cases plays an important position in TCM text mining. In this research , RoBERTa-BiLSTM-CRF model is constructed to realize the named entity recognition task of TCM cases text. With RoBERTa as the pre-training model, BiLSTM as the feature extractor, and CRF as the sequence annotation, the recognition of six entity named entity types, namely, symptom, tongue diagnosis, pulse diagnosis, prescription, dialectic, and Chinese medicine, is realized by manually annotating the corpus set. After iterative training of the model, the accuracy of the comprehensive experimental results was 96. 24% for accuracy, 83. 51% for precision, 88. 39% for recall, and 85. 88% for F-value; In each classification task, the accuracy rate of symptom was 79. 16%, the accuracy rate of T tongue diagnosis was 64. 59%, the accuracy rate of pulse diagnosis was 61. 83%, the accuracy rate of prescription was 90. 35%, the accuracy rate of dialectic was 77. 94%, and the accuracy rate of Chinese medicine was 98. 02%. Named entity recognition using RoBERTa-BiLSTM-CRF model provides effective support for TCM knowledge discovery, construction of knowledge graphs in TCM field and assisting physicians to utilize the potential application values in Traditional Chinese Medicine cases more effectively.
Recessions and Recoveries in Asia: What Can the Past Teach Us about the Present

With the global economy still in recession, two important questions arise for Asia: how soon will the recession end, and how vigorous will the region's recovery be? The purpose of this paper is to look at past recessions and recoveries in Asia in order to shed light on these issues. Several important stylized facts emerge from this study: (i) recessions accompanied by financial stress—notably, stress in domestic banking sectors—have been substantially longer and deeper than the norm, suggesting that the current recession could have been even costlier and more drawn out had Asia's banks not entered the downturn in such strong shape; (ii) recoveries in Asia have been weak because they were typically driven by a single engine: exports. In contrast, other emerging economies have tended to experience more vigorous recoveries because of a stronger contribution from domestic demand, notably investment; (iii) in Asia, deep recessions have resulted in substantial declines in potential output growth, meaning that their effects are not just cyclical but permanent. A clear lesson emerges from past experience: given the expected weak recovery in the eurozone and the United States, Asia should not count on exports to rebound strongly as it did in previous upturns. Rather, a fundamental rebalancing towards domestic demand is needed if Asia wants to preserve the high growth rates that have characterized its recent past. Finally, it remains to be seen whether potential output will fully recover from pre-crisis levels in the countries most affected by the crisis.
Cut Sequencing Algorithm for Safely Disassembling Large Structures

Disassembly and fragmentation are key operations in the dismantling and recycling of decommissioned structures such as aircraft, vessels, and buildings. Often, such operations are hazardous requiring careful planning for safe execution based on the experience and intuition of workers and forepersons. We propose and devise an algorithm for the automated sequencing of cuts to disassemble large structures. Using feedback from physics-based simulations and a mathematical model for safety, our algorithm performs sequential decision-making yielding the order of the cuts on the structure and the corresponding safe standing positions of the cutter (representing a worker or a robot). Our goal is to determine a sequence of cuts and cutter locations to maximize safety for the cutter and the environment. We establish the optimal solution via exhaustive searching, and design a greedy decision scheme to reduce the search runtime. Using our evaluations in simulation, we compare our greedy decision scheme against exhaustive searching and random searching, concluding that it satisfices the goal with high safety scores and low runtime.
Leaf Temperature Monitoring System Using Low-Cost Thermal Camera and IoT Technology

Leaf temperature provides valuable information about the water status of the plant. A leaf temperature monitoring system will offer potential benefits in precision agriculture. This paper presents an implementation of low-cost cameras, namely MLX90640 and AMG8833, for monitoring the leaf temperature. The low-cost camera is connected to a Raspberry Pi Zero W, a tiny single-board computer that performs the data processing and interfacing to the ThingSpeak IoT platform. The experimental results show that even though the AMG8833 has a low resolution of 8×8 pixels, it can be used to measure the leaf temperature, similar to the MLX90640, whose resolution is 32×24 pixels. The AMG8833 achieves a refresh rate of 26.57 fps, very fast than the MLX90640, which achieves 2.10 fps. Moreover, three thresholding algorithms, Otsu, Yen, and Mean thresholding work properly to separate the leaf from the background. Thus the leaf and background temperature can be defined easily.
Efficient Cascaded Streaming ASR System Via Frame Rate Reduction

In this paper, we explore various frame rate reduction schemes on the two-pass cascaded encoder model to improve its efficiency without scarifying the transcription quality. We conduct extensive studies on frame rate reduction strategies, left and right context window length, trade-offs in quality, latency, computation and power consumption, and performance in short-and long-form datasets. With the proposed schemes, we can lower the 2nd pass frame rate to $120 \mathrm{~ms}$, half of the 1st pass’s. This achieves $20 \%$ RTF reduction / $13 \%$ power saving / $19 \%$ lower final latency, without impact on the word-error-rate nor partial results’ latency. If allowing partial latency increase, we can further reduce the frame rate to $180 \mathrm{~ms}$ or even $240 \mathrm{~ms}$ from the 1st pass, and obtain $45 \%$ RTF / 35% power savings, with a similar or even better (on the short-form testset) recognition accuracy.
Simulation and Optimization of a Microgrid Energy Management System

This paper deals with the deployment and integration of renewable energies and storage systems. An Energy management system is necessary to achieve this objective. Two energy management techniques are considered in this work. They are termed: “heuristic” and “optimization” methods. Both methods aim to reduce the overall reliance on the conventional energy source from the main grid by maximizing the output of the two other sources (solar energy and storage system) and thus, reducing the overall cost. This study contrasts the two methods in terms of both grid usage and cost. Simulations considering cloudy and clear sky conditions while ensuring the user's satisfaction are carried out.
Atrial Fibrillation Dynamic Features Recognition using 1-D Convolutional Neural Networks

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires accurate detection for effective management and treatment. This study aims to propose a simple yet effective one-dimensional convolutional neural network (1D-CNN) for the AF dynamic features recognition which are extracted using a second-order dynamic system (SODS) concept. The SODS concept utilizes a dynamic system approach to capture the short-term behavior of ECG signals. It employs a second-order differential equation to model the ECG signals based on parameters such as the natural frequency (ω), damping coefficient (ξ), and forcing input (u) to enable the characterization and differentiation of ECG signal. The 1D-CNN model was trained and validated with preprocessed ECG signals from the MIT-BIH Atrial Fibrillation and MIT-BIH Normal Sinus Rhythm database. The results demonstrate the efficacy of the SODS and 1D CNN method for AF recognition. The proposed approach achieves high accuracy, with an accuracy of 98.35% using natural frequency, ω feature.
Characterization of oncogenes and tumor suppressor genes targeted by onco-microRNAs and tumor suppressor microRNAs in kidney cancer

Mutated oncogenes and tumor suppressor genes can significantly alter cell functions during the development of cancer. A thorough analysis of their mutation patterns may offer some insights into cancer emergence as well as some suggestions for potential drug targeting. Kidney renal clear cell carcinoma (KIRC) is the most common subtype of kidney cancer. KIRC has a poor prognosis and a high mortality rate. To date, immunotherapy based on immune checkpoints is the most promising treatment. MicroRNAs (miRNAs) down-regulate target genes by binding to the 3’ UTR of the respective targets. MiRNAs target genes at the post-transcriptional level after mRNA has been transcribed. The complexity of the miRNA-gene targeting interaction, as a result, has made this research more challenging. There are two classes of genes that this study focuses on which are oncogenes and tumor suppressor genes. Oncogenes function by deregulating cell proliferation and suppressing apoptosis. Tumor suppressor genes function by inhibiting cell proliferation and tumor development. There have been many studies that focus on the targeting relationship between genes and miRNAs, but few studies investigate the big picture of the relationship between oncogenes and tumor suppressor genes and how it can be regulated at the genome-wide level in the context of the miRNA targeting mechanism. We downloaded the significant miRNA-gene targeting cluster files for KIRC from a published study. We then separated and annotated the tumor suppressors and oncogenes based on their survival analysis. We grouped the data into four different categories based on the miRNA-targeted gene relationship (TS-TS, Onco-Onco, TS-Onco, Onco-TS). Based on the survival significance (p-value < .05) analysis, we have identified 28 oncogenes and 188 tumor suppressor genes. We have also identified 8 tumor suppressor miRNAs and 62 onco-miRNAs.
A Comparison of Optimization Methods for Path Finding Problem

Abstract—Optimizing travel time on a multi-lane highway with dynamically changing traffic density poses a 2-dimensional path optimization challenge. The temporal evolution of traffic density introduces a quasi-quadratic nature to the optimization problem, rendering quadratic optimization methods applicable. This research compares prevalent quadratic optimization techniques—specifically, the interior point method (IPM), sequential quadratic programming (SQP), and active set (AS) methods—in terms of both travel times and computational efficiency. A Monte-Carlo (MC) study is conducted, incorporating variables like the number of lanes, road length, and constraints on car speed (i.e., speed limits). Additionally, a novel path optimization approach, termed ’segmented path optimization,' is introduced. This method involves dividing the road into segments, optimizing each segment, merging their end and start points, and discretizing the optimal path. Through our comparative analysis, we find that the efficacy of these methods is context-dependent. There is no universally superior method, as each demonstrates its advantages under different conditions. However, the pipeline and the optimization flowchart may help the structuring of future works that contain the optimization processes, especially in artificial intelligence, smart recommendation, and GPS-based systems.
A Spectral Bundle Method for Sparse Semidefinite Programs

Semidefinite programs (SDPs) have many applications in the field of controls. To improve scalability, it is important to exploit the inherent sparsity when solving SDPs. In this paper, we develop a new spectral bundle algorithm that solves sparse SDPs without introducing additional variables. We first apply chordal decomposition to replace a large positive semidefinite (PSD) constraint with a set of smaller coupled constraints. Then, we move the coupled constraints into the cost function via exact penalty. This leads to an equivalent non-smooth penalized problem, which can be solved by bundle methods. We present a new efficient spectral bundle algorithm, where subgradient information is incorporated to update a lower approximation at each iteration. We further establish sublinear convergences in terms of objective value, primal feasibility, dual feasibility, and duality gap. Under Slater's condition, the algorithm converges with the rate of $\mathcal{O}(1/\epsilon^{3})$, and the rate improves to $\mathcal{O}(1/\epsilon)$ when strict complementarity holds. Our numerical experiments support the theoretical analysis.
Diagnosing and Repairing Feature Representations Under Distribution Shifts

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned features. When the features the robot needs to learn to perform its task are missing or do not generalize well to new settings, the robot will not be able to learn the task the human wants and, even worse, may learn a completely different and undesired behavior. Prior work shows how the robot can detect when its representation is missing some feature and can, thus, ask the human to be taught about the new feature; however, these works do not differentiate between features that are completely missing and those that exist but do not generalize to new environments. In the latter case, the robot would detect misalignment and simply learn a new feature, leading to an arbitrarily growing feature representation that can, in turn, lead to spurious correlations and incorrect learning down the line. In this work, we propose separating the two sources of misalignment: we propose a framework for determining whether a feature the robot needs is incorrectly learned and does not generalize to new environment setups vs. is entirely missing from the robot's representation. Once we diagnose the source of error, we show how the human can initiate the realignment process for the model: if the feature is missing, we follow prior work for learning new features; however, if the feature exists but does not generalize, we use data augmentation to expand its training and, thus, complete the repair process. We demonstrate the proposed approach in experiments with a simulated 7DoF robot manipulator and physical human corrections.