Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning

In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha factors. Alphas in formula forms are more interpretable and thus favored by practitioners concerned with risk. In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together. However, most traditional alpha generators mine alphas one by one separately, overlooking the fact that the alphas would be combined later. In this paper, we propose a new alpha-mining framework that prioritizes mining a synergistic set of alphas, i.e., it directly uses the performance of the downstream combination model to optimize the alpha generator. Our framework also leverages the strong exploratory capabilities of reinforcement learning~(RL) to better explore the vast search space of formulaic alphas. The contribution to the combination models' performance is assigned to be the return used in the RL process, driving the alpha generator to find better alphas that improve upon the current set. Experimental evaluations on real-world stock market data demonstrate both the effectiveness and the efficiency of our framework for stock trend forecasting. The investment simulation results show that our framework is able to achieve higher returns compared to previous approaches.Comment: Accepted by KDD '23, ADS trac

Bistatic InSAR interferometry imaging and DSM generation for TH-2

TH-2 is a bistatic synthetic aperture radar (SAR) satellite system in formation flight. Compared with traditional InSAR systems, it can eliminate decoherent sources such as time and atmosphere, besides, it can generate highly coherent SAR image pairs. This paper firstly describe the extended chirp scaling (ECS) imaging algorithm based on the hyperbolic equivalent method, and also introduces pre-filtering to deal with problems such as reduced coherence and interference phase errors caused by mixed baselines. Secondly, it introduces the interference processing method and the technical process of DSM reconstruction in the bistatic mode. Finally, an interference imaging experiment is performed using the original echo data of a certain mountainous experimental area, and the 3D reconstruction experiment is performed by using the generated SAR image pair, which analyzes the coherence of the image, the phase unwrapping results and the DSM reconstruction results. The experimental results verify that the interference imaging algorithm in this paper has good focusing effect and phase preservation capacity. At the same time, the interferometry and 3D reconstruction capabilities of the data are verified as well

Expression of SET Protein in the Ovaries of Patients with Polycystic Ovary Syndrome

Background. We previously found that expression of SET gene was up-regulated in polycystic ovaries by using microarray. It suggested that SET may be an attractive candidate regulator involved in the pathophysiology of polycystic ovary syndrome (PCOS). In this study, expression and cellular localization of SET protein were investigated in human polycystic and normal ovaries. Method. Ovarian tissues, six normal ovaries and six polycystic ovaries, were collected during transsexual operation and surgical treatment with the signed consent form. The cellular localization of SET protein was observed by immunohistochemistry. The expression levels of SET protein were analyzed by Western Blot. Result. SET protein was expressed predominantly in the theca cells and oocytes of human ovarian follicles in both PCOS ovarian tissues and normal ovarian tissues. The level of SET protein expression in polycystic ovaries was triple higher than that in normal ovaries (P<0.05). Conclusion. SET was overexpressed in polycystic ovaries more than that in normal ovaries. Combined with its localization in theca cells, SET may participate in regulating ovarian androgen biosynthesis and the pathophysiology of hyperandrogenism in PCOS

Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems

Neural networks have been increasingly applied for control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and efficiency, as well as reducing the need for complex physical models. However, the lack of safety guarantees for such neural network based controllers has significantly impeded their adoption in safety-critical CPSs. In this work, we propose a controller adaptation approach that automatically switches among multiple controllers, including neural network controllers, to guarantee system safety and improve energy efficiency. Our approach includes two key components based on formal methods and machine learning. First, we approximate each controller with a Bernstein-polynomial based hybrid system model under bounded disturbance, and compute a safe invariant set for each controller based on its corresponding hybrid system. Intuitively, the invariant set of a controller defines the state space where the system can always remain safe under its control. The union of the controllers' invariants sets then define a safe adaptation space that is larger than (or equal to) that of each controller. Second, we develop a deep reinforcement learning method to learn a controller switching strategy for reducing the control/actuation energy cost, while with the help of a safety guard rule, ensuring that the system stays within the safe space. Experiments on a linear adaptive cruise control system and a non-linear Van der Pol's oscillator demonstrate the effectiveness of our approach on energy saving and safety enhancement

Longitudinal Genomic Evolution of Conventional Papillary Thyroid Cancer With Brain Metastasis

BackgroundBrain metastasis is extremely rare but predicts dismal prognosis in papillary thyroid cancer (PTC). Dynamic evaluation of stepwise metastatic lesions was barely conducted to identify the longitudinal genomic evolution of brain metastasis in PTC.MethodChronologically resected specimen was analyzed by whole exome sequencing, including four metastatic lymph nodes (lyn 1–4) and brain metastasis lesion (BM). Phylogenetic tree was reconstructed to infer the metastatic pattern and the potential functional mutations.ResultsContrasting with lyn1, ipsilateral metastatic lesions (lyn2–4 and BM) with shared biallelic mutations of TSC2 indicated different genetic originations from multifocal tumors. Lyn 3/4, particularly lyn4 exhibited high genetic similarity with BM. Besides the similar mutational compositions and signatures, shared functional mutations (CDK4R24C, TP53R342*) were observed in lyn3/4 and BM. Frequencies of these mutations gradually increase along with the metastasis progression. Consistently, TP53 knockout and CDK4R24C introduction in PTC cells significantly decreased radioiodine uptake and increased metastatic ability.ConclusionGenomic mutations in CDK4 and TP53 during the tumor evolution may contribute to the lymph node and brain metastasis of PTC

Optimization of Passenger Transportation Corridor Mode Supply Structure in Regional Comprehensive Transport Considering Economic Equilibrium

Reasonable transportation network layoutsarecritical for optimizing a comprehensive transport system. With the gradual development of a transportation industry from quantitative expansion to structural optimization, and transformation of various transportation modes from independent operation to integrated development, traditional comprehensive transport planning theories and methods have not adapted. In thispaper, a new planning concept is proposed from the perspective of economic equilibrium with theaim ofoptimizing a supply structure for a comprehensive transport passenger transportation corridor. An in-depth analysis was conducted of the internal mechanism of the dynamic equilibrium between supply and demand of this corridor,wherein the maximum of the globaltransportation demand subjectcustomer surplus wastaken as a target function, respective interest functions of a demand subject and a supply subject served as constraints to quantitatively optimize the supply structure of the passenger transportation corridor in comprehensive transport, and a Gradient Descent algorithmwasdesigned. The results show that the proposed model better reflectstheeconomic operation mechanism of a passenger transportation market in a comprehensive transport corridor (CTC), and prove that the supply structure of CTC is closely related to passenger flow, travel value distribution, a supply subject's scale rate of return,and travel time. These research results have important academic values in terms of improving passenger transportation corridor structure optimization in region-specific comprehensive transport that conforms to a market economy mechanism. This concept can be extended from single corridor planning to point-to-point and door-to-door transportation supply structure planning, andto comprehensive transport network planning and urban transportation planning without loss of generality

Heat Transfer Characteristics of High-Temperature Dusty Flue Gas from Industrial Furnaces in a Granular Bed with Buried Tubes

Experimental heat transfer equipment with a buried tube granular bed was set up for waste heat recovery of flue gas. The effects of flue gas inlet temperature (1096.65&ndash;1286.45 K) and cooling water flow rate (2.6&ndash;5.1 m3/h) were studied through experiment and computational fluid dynamics&rsquo; (CFD) method. On the basis of logarithmic mean temperature difference method, the total heat transfer coefficient of the granular bed was used to characterize its heat transfer performance. Experimental results showed that the waste heat recovery rate of the equipment exceeded 72%. An increase in the cooling water flow rate and inlet gas temperature was beneficial to recovering waste heat. The cooling water flow rate increases from 2.6 m3/h to 5.1 m3/h and the recovery rate of waste heat increases by 1.9%. Moreover, the heat transfer coefficient of the granular bed increased by 4.4% and the inlet gas temperature increased from 1096.65 K to 1286.45 K. The recovery rate of waste heat increased by 1.7% and the heat transfer coefficient of the granular bed rose by 26.6%. Therefore, experimental correlations between the total heat transfer coefficient of a granular bed and the cooling water flow rate and inlet temperature of dusty gas were proposed. The CFD method was used to simulate the heat transfer in the granular bed, and the effect of gas temperature on the heat transfer coefficient of granular bed was studied. Results showed that the relative error was less than 2%