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Figure S1 Flow diagram for the enrolment and exclusion of patients.

Table S2 Consistency analysis of integration at different time points

Figure S2 The integral or cumulative total score uncombined predicted the loss rate of HBsAg at EOF. (A) Integral score at baseline,week 12.and 24. (B) Cumulative total score at week 12.and 24

Table S4 Weighted Kappa consistency test between baseline and 24W

Figure S3 Application of the score model in the initial treatment of patients with PEG-IFN? monotherapy.

Table S5 Weighted Kappa consistency test between 12W and 24W

Table S3 Weighted Kappa consistency test between baseline and 12W.

As deep image classification applications, e.g., face recognition, become increasingly prevalent in our daily lives, their fairness issues raise more and more concern. It is thus crucial to comprehensively test the fairness of these applications before deployment. Existing fairness testing methods suffer from the following limitations: 1) applicability, i.e., they are only applicable for structured data or text without handling the high-dimensional and abstract domain sampling in the semantic level for image classification applications; 2) functionality, i.e., they generate unfair samples without providing testing criterion to characterize the model's fairness adequacy. To fill the gap, we propose DeepFAIT, a systematic fairness testing framework specifically designed for deep image classification applications. DeepFAIT consists of several important components enabling effective fairness testing of deep image classification applications: 1) a neuron selection strategy to identify the fairness-related neurons; 2) a set of multi-granularity adequacy metrics to evaluate the model's fairness; 3) a test selection algorithm for fixing the fairness issues efficiently. We have conducted experiments on widely adopted large-scale face recognition applications, i.e., VGGFace and FairFace. The experimental results confirm that our approach can effectively identify the fairness-related neurons, characterize the model's fairness, and select the most valuable test cases to mitigate the model's fairness issues.

BackgroundPhase 2 cooperative group meningioma trial assessing the safety and efficacy of risk-adaptive management strategies. This is the initial analysis of the high-risk cohort.Methods and materialsHigh-risk patients were those with a new or recurrent World Health Organization (WHO) grade III meningioma of any resection extent, recurrent WHO grade II of any resection extent, or new WHO grade II after subtotal resection. Patients received intensity-modulated radiotherapy (IMRT) using a simultaneous integrated boost technique (60 Gy high dose and 54 Gy low dose in 30 fractions). Three-year progression-free survival (PFS) was the primary endpoint. Adverse events (AEs) were scored per NCI Common Terminology Criteria for Adverse Events version3.ResultsOf 57 enrolled patients, 53 received protocol treatment. Median follow-up was 4.0 years (4.8 years for living patients). Two patients withdrew without progression before year 3; for the remaining 51 patients, 3-year PFS was 58.8%. Among all 53 protocol-treated patients, 3-year PFS was 59.2%. Three-year local control was 68.9%, and overall survival was 78.6%. Of 51 patients, 1 patient (1.9%) experienced a late grade-5 necrosis-related AE. All other acute (23 of 53 patients) and late (21 of 51 patients) AEs were grades 1 to3.ConclusionsPatients with high-risk meningioma treated with IMRT (60 Gy/30) experienced 3-year PFS of 58.8%. Combined acute and late AEs were limited to grades 1 to 3, except for a single necrosis-related grade 5 event. These results support postoperative IMRT for high-risk meningioma and invite ongoing investigations to improve outcomes further.

BackgroundFearing increased myelotoxicity, many practitioners adjust the body surface area (BSA)-calculated doses in obese patients. Regarding temozolomide (TMZ), a prior study suggested men with a BSA >2 m2 may experience increased toxicity; however, surprisingly, the inverse observation was noted in women, ie, BSA

Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples. Adversarial samples are often crafted through adversarial perturbation, i.e., manipulating the original sample with minor modifications so that the DNN model labels the sample incorrectly. Given that it is almost impossible to train perfect DNN, adversarial samples are shown to be easy to generate. As DNN are increasingly used in safety-critical systems like autonomous cars, it is crucial to develop techniques for defending such attacks. Existing defense mechanisms which aim to make adversarial perturbation challenging have been shown to be ineffective. In this work, we propose an alternative approach. We first observe that adversarial samples are much more sensitive to perturbations than normal samples. That is, if we impose random perturbations on a normal and an adversarial sample respectively, there is a significant difference between the ratio of label change due to the perturbations. Observing this, we design a statistical adversary detection algorithm called nMutant (inspired by mutation testing from software engineering community). Our experiments show that nMutant effectively detects most of the adversarial samples generated by recently proposed attacking methods. Furthermore, we provide an error bound with certain statistical significance along with the detection., Comment: Sumitted to NIPS 2018

In this paper, based on the study of f-distributor in (1), we defined the concept of f-center and discussed some properties of f-center. As an application of these properties, we generalize some properties of p-center and center of groups.

The development of slag glass–ceramics has environmental and commercial value. However, new types of these materials are usually developed using the “trial and error” method because of little understanding of the relationship between the composition, processing, microstructure, and properties. In this paper, artificial neural network (ANN) technology was applied to investigate the relationship between the composition content and the properties of slag glass–ceramic. The investigation showed that the ANN model had an outstanding learning ability and was effective in complex data analysis. If the data set reflects the relationship of the composition and property, the trained network will learn the relationship and then give relatively accurate and stable prediction. A new “virtual sample” technology has also been created which improves the prediction performance of the network by providing greater accuracy and reliability. With this virtual sample technology, the ANN model can establish the exact relationship from a small-size-data set, and gives accurate predictions. This improved ANN model is a powerful and reliable tool for data analysis and property prediction, and will facilitate the material design and development of slag glass–ceramics.

The radio-voltaic effect is investigated to be applied radioisotopic batteries in this work. The collection rates of the electron-hole pairs are 94 percent for the alpha particles emitted by239Pu source and 65 percent for the beta particles emitted by90Sr-90Y source. The maximum energy conversion efficiency got by the silicon element is about 16%. A prototype radioisotopic battery with147Pm beta sources is constructed. Its maximum short-circuit current and off voltage are about 2μ A and 190 mV respectively. The experimental data suggest that the life of the prototype battery may be more than three years.

The microstructure of CaO-Al2O3-SiO2(CAS)glass system is studied by molecular dynamics method.When Ca/Al equals to 1/2,which is called tectosilicate glass system,CAS is not a totally full network as conventional theories,but with some Non-Bridging Oxygen(NBO).The theoretical result proves Stebins's experimental conclusion.The result also reveals that when the ratio of Ca/Al is greater than 1/2 Al is preferred inserting in polymerized network to the end of polymerized network,when the ratio of Ca/Al is less than 1/2,the situationis is contrary.The “aluminium avoidance” principle believes that Al—O—Al linkages are energetically less favorable than Al—O—Si linkages in CAS glass system.However when the ratio of Ca/Al alters down across 1/2,there are some Al—O—Si linkages change to Al—O—Al and Si—O—Si linkages.