Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions

It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration. It is responsible for the unreliability of practical machine learning systems. For example, in online advertising, an ad can receive a click-through rate prediction of 0.1 over some population of users where its actual click rate is 0.15. In such cases, the probabilistic predictions have to be fixed before the system can be deployed. In this paper, we first introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We show that existing post-hoc calibration methods have limited improvements in the new field-level metric and other non-calibration metrics such as the AUC score. To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set. We present extensive experiments on five large-scale datasets. The results showed that Neural Calibration significantly improves against uncalibrated predictions in common metrics such as the negative log-likelihood, Brier score and AUC, as well as the proposed field-level calibration error.Comment: WWW 202

Iteration Method for Predicting Essential Proteins Based on Orthology and Protein-protein Interaction Networks

Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12. Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks

(2-Acetyl­phenolato)(2,2′-bipyridine)nitratocopper(II)

In the title compound, [Cu(C8H7O2)(NO3)(C10H8N2)], the CuII ion is five-coordinate in a distorted square-pyramidal geometry. The basal positions are occupied by two N atoms from a 2,2′-bipyridine ligand and two O atoms from the 2-acetyl­phenolate anion. The axial position is occupied by one O atom of a nitrate anion. In the bipyridine ligand, the two pyridine rings are slightly twisted by an angle of 3.5 (1)°. The crystal structure is stabilized by C—H⋯O hydrogen bond

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

catena-Poly[zinc(II)-bis­[μ2-3-(3-pyrid­yl)­benzoato]-κ2 O:N;κ2 N:O]

In the title compound, [Zn(C12H8NO2)2]n, the Zn2+ cation is coordinated by a pair of carboxyl­ate O atoms as well as two pyridyl N atoms to afford a distorted tetra­hedral environment. Adjacent Zn2+ cations, with a separation of 8.807 (2) Å, are linked by two 3-(3-pyrid­yl)benzoate ligand bridges, generating an infinite ribbon extending parallel to [001]

Effects of Sangu Decoction on Osteoclast Activity in a Rat Model of Breast Cancer Bone Metastasis

Bone metastasis (BM) is a major clinical problem for which current treatments lack full efficacy. The Traditional Chinese Medicine (TCM) Sangu Decoction (SGD) has been widely used to treat BM in China. However, no in vivo experiments to date have investigated the effects of TCM on osteoclast activity in BM. In this study, the protective effect and probable mechanism of SGD were evaluated. The model was established using the breast cancer MRMT-1 cells injected into the tibia of rat. SGD was administrated, compared with Zoledronic acid as a positive control. The development of the bone tumor and osteoclast activity was monitored by radiological analysis. TRAP stain was used to identify osteoclasts quantity and activity. TRAP-5b in serum or bone tumor and TRAP mRNA were also quantified. Radiological examination showed that SGD inhibited tumor proliferation and preserved the cortical and trabecular bone structure. In addition, a dramatic reduction of TRAP positive osteoclasts was observed and TRAP-5b levels in serum and bone tumor decreased significantly. It also reduced the mRNA expression of TRAP. The results indicated that SGD exerted potent antiosteoclast property that could be directly related to its TRAP inhibited activity. In addition it prevented bone tumor proliferation in BM model