Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques

Pattern Visual Evoked Potential as a Predictor of Occlusion Therapy for Amblyopia

PURPOSE: This study was conducted to investigate the role of the pattern visual evoked potential (pVEP) as a predictor of occlusion therapy for patients with strabismic, anisometropic, and isometropic amblyopia. The secondary aim was to compare the characteristics of pVEP between strabismic and anisometropic amblyopia. METHODS: This retrospective comparative case series included 120 patients who had received occlusion therapy or a glasses prescription for correction of strabismic, anisometropic, and isometropic amblyopia (20 patients had strabismic amblyopia, 41 patients had anisometropic amblyopia, and 59 patients had isometropic amblyopia). For each patient, the value of the P100 latency on pVEP at the time of the initial diagnosis of amblyopia was collected. Subsequently, the P100 latency was compared according to types of amblyopia. Fifty of 120 patients (7 patients with strabismic amblyopia, 21 patients with anisometropic amblyopia, and 22 patients with isometropic amblyopia) who were followed-up for longer than 6 months were divided into two groups based on the value of their P100 latency (Group 1, P100 latency 120 msec or less; Group 2, P100 latency longer than 120 msec.) The amount of visual improvement after occlusion therapy or glasses was compared between two study groups. RESULTS: The mean P100 latency was 119.7+/-25.2 msec in eyes with strabismic amblyopia and 111.9+/-17.8 msec in eyes with non-strabismic (anisometropic or isometropic) amblyopia (p=0.213). In Group 1, the mean visual improvement after occlusion therapy or glasses was 3.69+/-2.14 lines on Dr. Hahn's standard test chart; in Group 2, the mean improvement was 2.27+/-2.21 lines (p=0.023). CONCLUSIONS: The P100 latency on pVEP at the time of initial diagnosis was significantly related to the visual improvement after occlusion therapy or glasses in patients with strabismic, anisometropic, and isometropic amblyopia. Therefore, it was presumed that patients with a delayed P100 latency might have less visual improvement after occlusion therapy or glasses. In addition, there was no apparent difference in P100 latency between patients with strabismic and non-strabismic (anisometropic or isometropic) amblyopia.ope

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...Comment: 12 pages, 4 figure

Development of a Remote Testing System for Performance of Gas Leakage Detectors

In this research, we designed a remote system to test parameters of gas detectors such as gas concentration and initial response time. This testing system is available to measure two gas instruments simultaneously. First of all, we assembled an experimental jig with a square structure. Those parts are included with a glass flask, two high-quality cameras, and two Ethernet modems for transmitting data. This remote gas detector testing system extracts numerals from videos with continually various gas concentrations while LCDs show photographs from cameras. Extracted numeral data are received to a laptop computer through Ethernet modem. And then, the numerical data with gas concentrations and the measured initial response speeds are recorded and graphed. Our remote testing system will be diversely applied on gas detector’s test and will be certificated in domestic and international countries

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis

Background Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built. Results In this study, we developed StressGenePred, a neural network-based machine learning method, to integrate time-series transcriptome data of multiple stress types. StressGenePred is designed to detect single stress-specific biomarker genes by using a simple feature embedding method, a twin neural network model, and Confident Multiple Choice Learning (CMCL) loss. The twin neural network model consists of a biomarker gene discovery and a stress type prediction model that share the same logical layer to reduce training complexity. The CMCL loss is used to make the twin model select biomarker genes that respond specifically to a single stress. In experiments using Arabidopsis gene expression data for four major environmental stresses, such as heat, cold, salt, and drought, StressGenePred classified the types of stress more accurately than the limma feature embedding method and the support vector machine and random forest classification methods. In addition, StressGenePred discovered known stress-related genes with higher specificity than the Fisher method. Conclusions StressGenePred is a machine learning method for identifying stress-related genes and predicting stress types for an integrated analysis of multiple stress time-series transcriptome data. This method can be used to other phenotype-gene associated studies.This work and publication costs were supported by National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. NRF2017M3C4A7065887), and the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541). This work was supported for W.J. by the Agenda program (No. PJ014307), Rural Development of Administration of Republic of Korea

Surgical treatment and long-term outcomes of low-grade myofibroblastic sarcoma: a single-center case series of 15 patients

Abstract Background Low-grade myofibroblastic sarcoma (LGMS) is a poorly studied, rare, soft tissue sarcoma. LGMS is characterized by a low malignancy potential, tendency for local recurrence, and low likelihood of distant metastases. However, no studies have reported on the surgical treatment method and its long-term outcomes. Methods We included all patients treated for LGMS at our institution between March 2010 and March 2021. Medical charts were retrospectively reviewed to collect demographic information, as well as information about the clinical course, tumor characteristics, and outcomes. Statistical analysis was performed to identify the factors associated with the recurrence rate. Results Fifteen patients who underwent surgical treatment were enrolled in this study. There were seven cases in the upper extremities, four in the trunk area, three in the lower extremities, and one in the head and neck area. There were no metastatic cases and two cases of local recurrence. Conclusions The incidence of LGMS in the extremities or trunk may be higher than expected based on the current literature. Univariate analysis showed that local tissue invasion and surgical method could be associated with local recurrence. Although further large studies are needed to establish risk factors of local recurrence or extent of resection margins, based on our study, wide local excision under the proper diagnosis is the most important treatment

Role of intermediate phase for stable cycling of Na_7V_4(P_2O_7)_4PO_4 in sodium ion battery

Sodium ion batteries offer promising opportunities in emerging utility grid applications because of the low cost of raw materials, yet low energy density and limited cycle life remain critical drawbacks in their electrochemical operations. Herein, we report a vanadium-based ortho-diphosphate, Na_7V_4(P_2O_7)_4PO_4, or VODP, that significantly reduces all these drawbacks. Indeed, VODP exhibits single-valued voltage plateaus at 3.88 V vs. Na/Na+ while retaining substantial capacity (>78%) over 1,000 cycles. Electronic structure calculations reveal that the remarkable single plateau and cycle life originate from an intermediate phase (a very shallow voltage step) that is similar both in the energy level and lattice parameters to those of fully intercalated and deintercalated states. We propose a theoretical scheme in which the reaction barrier that arises from lattice mismatches can be evaluated by using a simple energetic consideration, suggesting that the presence of intermediate phases is beneficial for cell kinetics by buffering the differences in lattice parameters between initial and final phases. We expect these insights into the role of intermediate phases found for VODP hold in general and thus provide a helpful guideline in the further understanding and design of battery materials

Natural Products in the Prevention of Metabolic Diseases: Lessons Learned from the 20th KAST Frontier Scientists Workshop

The incidence of metabolic and chronic diseases including cancer, obesity, inflammation-related diseases sharply increased in the 21st century. Major underlying causes for these diseases are inflammation and oxidative stress. Accordingly, natural products and their bioactive components are obvious therapeutic agents for these diseases, given their antioxidant and anti-inflammatory properties. Research in this area has been significantly expanded to include chemical identification of these compounds using advanced analytical techniques, determining their mechanism of action, food fortification and supplement development, and enhancing their bioavailability and bioactivity using nanotechnology. These timely topics were discussed at the 20th Frontier Scientists Workshop sponsored by the Korean Academy of Science and Technology, held at the University of Hawaii at Manoa on 23 November 2019. Scientists from South Korea and the U.S. shared their recent research under the overarching theme of Bioactive Compounds, Nanoparticles, and Disease Prevention. This review summarizes presentations at the workshop to provide current knowledge of the role of natural products in the prevention and treatment of metabolic diseases

Evaluation of Polycaprolactone Applicability for Manufacturing High-Performance Cellulose Nanocrystal Cement Composites

This experimental study examined the aplication effect of polycaprolactone (PCL), an organic resin material with excellent elasticity and ductility, on improving the mechanical performance of cellulose nanocrystal (CNC) cement composites. PCL was compared according to its shape, and in the case of Granules, which is the basic shape, interfacial adhesion with cement was not achieved, so a dichloromethane (DCM) solution was used to dissolve and use the Granules form. As a method for bonding PCL to the CNC surface, the CNC surface was modified using 3-aminopropyltriethoxysilane (APTES), and surface silylation was confirmed through Fourier transform infrared spectroscopy (FT-IR) analysis. In order to evaluate the dispersibility according to the application of PCL to the modified CNC, particle size analysis (PSA) and zeta potential analysis were performed according to the PCL mixing ratio. Through the PSA and zeta potential values, the highest dispersion stability was shown at 1 vol.%, the cohesive force of CNC was low, and the dispersion stability was high according to the application of PCL. According to the results of the dispersion stability evaluation, the degree of hydration of the dissolved PCL 1 vol.%, CNC-only specimens, and plain specimens were analyzed. CNC acted as a water channel inside the cement to accelerate hydration in the non-hydrated area, resulting in an increased degree of hydration. However, the incorporation of PCL showed a low degree of hydration, and the analysis of strength characteristics also showed a decrease of approximately 27% compared with that of plain specimens. This was because the bonding with SiO2 was not smooth owing to the solvent, thus affecting internal hydration. In order to investigate the effect of the PCL shape, the compressive and flexural strength characteristics were compared using PCL powder as an additional parameter. The compressive strength and flexural strength were improved by about 54% and 26%, respectively, in the PCL powder 15 wt% specimen compared to the general specimen. Scanning electron microscopy (SEM) analysis confirmed that the filler effect, which made the microporous structure denser, affects the mechanical performance improvement

Implementation of SOH Estimator in Automotive BMSs Using Recursive Least-Squares

This paper presents a computationally efficient state-of-health (SOH) estimator that is readily applicable to automotive battery management systems (BMSs). The proposed scheme uses a recursive estimator to improve the original scheme based on a batch estimator. In the batch process, state estimation requires significantly longer CPU time than data measurement, and the original scheme may fail to satisfy real-time guarantees. To prevent this problem, we apply recursive least-squares. By replacing the batch process to solve the normal equation with a recursive update, the proposed scheme can spread CPU utilization and reduce memory footprint. The benefits of the recursive estimator are quantitatively validated by comparing its CPU time and memory footprint with those of the batch estimator. A similar level of SOH estimation accuracy is achievable with over 60% less memory usage, and the CPU time stabilizes around 5 ms. This enables implementation of the proposed scheme in automotive BMSs