Deep Incremental Learning of Imbalanced Data for Just-In-Time Software Defect Prediction

This work stems from three observations on prior Just-In-Time Software Defect Prediction (JIT-SDP) models. First, prior studies treat the JIT-SDP problem solely as a classification problem. Second, prior JIT-SDP studies do not consider that class balancing processing may change the underlying characteristics of software changeset data. Third, only a single source of concept drift, the class imbalance evolution is addressed in prior JIT-SDP incremental learning models. We propose an incremental learning framework called CPI-JIT for JIT-SDP. First, in addition to a classification modeling component, the framework includes a time-series forecast modeling component in order to learn temporal interdependent relationship in the changesets. Second, the framework features a purposefully designed over-sampling balancing technique based on SMOTE and Principal Curves called SMOTE-PC. SMOTE-PC preserves the underlying distribution of software changeset data. In this framework, we propose an incremental deep neural network model called DeepICP. Via an evaluation using \numprojs software projects, we show that: 1) SMOTE-PC improves the model's predictive performance; 2) to some software projects it can be beneficial for defect prediction to harness temporal interdependent relationship of software changesets; and 3) principal curves summarize the underlying distribution of changeset data and reveals a new source of concept drift that the DeepICP model is proposed to adapt to

Concordane of OSTA and lumbar spine BMD by DXA in identifying risk of osteoporosis

OBJECTIVE: To investigate the accuracy of Osteoporosis Self-assessment Tool for Asians (OSTA) in identifying the risk of osteoporosis in postmenopausal women. To validate use of OSTA risk index by comparing it with the bone mineral density (BMD) of lumbar spine measured by dual energy X-ray absorptiometry (DXA). METHODS: The data of lumbar spine BMD (LS BMD) measurements by DXA of 218 postmenopausal women of Han nationality in Sichuan province were compared with OSTA risk index. The concordance of OSTA and LS BMD were calculated and analyzed by fourfold table and receiver operating characteristic (ROC) curve. RESULTS: The prevalence of osteoporosis in these women was 40.4% and 61.5%, with the LS BMD T score cutoffs -2.5 and -2.0, respectively. The sensitivity, specificity, and accuracy of OSTA risk index compared with T score cutoff -2.5 of LS BMD were 59.1%, 56.9% and 57.8%, respectively, while they were 57.5%, 63.1%, 59.6% by T score cutoff -2.0. CONCLUSION: For identifying risk of osteoporosis, the concurrence was lower than those reported studies when comparing LS BMD measurements to OSTA risk index in Chinese Han nationality postmenopausal women of Sichuan province. Physicians should identify women who need BMD measurement according to more factors rather than age and body weight

Software for doing computations in graded Lie algebras

We introduce the Macaulay2 package GradedLieAlgebras for doing computations in graded Lie algebras presented by generators and relations.Comment: 5 page

Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction

Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by dividing the bill into smaller parts to deduct as much as possible. This article proposes an end-to-end approach of automatically learning the optimal deduction paths (deduction amount in order), which reduces the cost of manual path design and maximizes the amount of successful deduction. Specifically, in view of the large search space of the paths and the extreme sparsity of historical successful deduction records, we propose a deep hierarchical reinforcement learning approach which abstracts the action into a two-level hierarchical space: an upper agent that determines the number of steps of deductions each day and a lower agent that decides the amount of deduction at each step. In such a way, the action space is structured via prior knowledge and the exploration space is reduced. Moreover, the inherited information incompleteness of the business makes the environment just partially observable. To be precise, the deducted amounts indicate merely the lower bounds of the available account balance. To this end, we formulate the problem as a partially observable Markov decision problem (POMDP) and employ an environment correction algorithm based on the characteristics of the business. In the world's largest electronic payment business, we have verified the effectiveness of this scheme offline and deployed it online to serve millions of users

The Surface Chemistry and Structure of Colloidal Lead Halide Perovskite Nanocrystals

Since the initial discovery of colloidal lead halide perovskite nanocrystals, there has been significant interest placed on these semiconductors because of their remarkable optoelectronic properties, including very high photoluminescence quantum yields, narrow size- and composition-tunable emission over a wide color gamut, defect tolerance, and suppressed blinking. These material attributes have made them attractive components for next-generation solar cells, light emitting diodes, low-threshold lasers, single photon emitters, and X-ray scintillators. While a great deal of research has gone into the various applications of colloidal lead halide perovskite nanocrystals, comparatively little work has focused on the fundamental surface chemistry of these materials. While the surface chemistry of colloidal semiconductor nanocrystals is generally affected by their particle morphology, surface stoichiometry, and organic ligands that contribute to the first coordination sphere of their surface atoms, these attributes are markedly different in lead halide perovskite nanocrystals because of their ionicity. In this Account, emerging work on the surface chemistry of lead halide perovskite nanocrystals is highlighted, with a particular focus placed on the most-studied composition of CsPbBr3. We begin with an in-depth exploration of the native surface chemistry of as-prepared, 0-D cuboidal CsPbBr3 nanocrystals, including an atomistic description of their surface termini, vacancies, and ionic bonding with ligands. We then proceed to discuss various post-synthetic surface treatments that have been developed to increase the photoluminescence quantum yields and stability of CsPbBr3 nanocrystals, including the use of tetraalkylammonium bromides, metal bromides, zwitterions, and phosphonic acids, and how these various ligands are known to bind to the nanocrystal surface. To underscore the effect of post-synthetic surface treatments on the application of these materials, we focus on lead halide perovskite nanocrystal-based light emitting diodes, and the positive effect of various surface treatments on external quantum efficiencies. We also discuss the current state-of-the-art in the surface chemistry of 1-D nanowires and 2-D nanoplatelets of CsPbBr3, which are more quantum confined than the corresponding cuboidal nanocrystals but also generally possess a higher defect density because of their increased surface area-to-volume ratios

High-Throughput Acoustofluidic Fabrication of Tumor Spheroids

Three-dimensional (3D) culture of multicellular spheroids, offering a desirable biomimetic microenvironment, is appropriate for recapitulating tissue cellular adhesive complexity and revealing a more realistic drug response. However, current 3D culture methods are suffering from low-throughput, poor controllability, intensive-labor, and variation in spheroid size, thus not ready for many high-throughput screening applications including drug discovery and toxicity testing. Herein, we developed a high-throughput multicellular spheroid fabrication method using acoustofluidics. By acoustically-assembling cancer cells with low-cost and disposable devices, our method can produce more than 12 000 multicellular aggregates within several minutes and allow us to transfer these aggregates into ultra-low attachment dishes for long-term culture. This method can generate more than 6000 tumor spheroids per operation, and reduce tumor spheroid formation time to one day. Our platform has advantages in forming spheroids with high throughput, short time, and long-term effectiveness, and is easy-to-operation. This acoustofluidic spheroid assembly method provides a simple and efficient way to produce large numbers of uniform-sized spheroids for biomedical applications in translational medicine, pharmaceutical industry and basic life science research