Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).Comment: Accepted by EMNLP 2023 (The Industry Track

S3: Syntax- and semantic-guided repair synthesis via programming by examples

National Science Foundatio

Robust Binary Neural Network Operation from 233 K to 398 K via Gate Stack and Bias Optimization of Ferroelectric FinFET Synapses

A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of a Fe-FinFET-based artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the "0" state programmed deep in the subthreshold and the "1" state in strong inversion, is crucial for robust DNN inference. The performance of a purely software-based binary neural network (BNN), with 96.1% accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects and maintained a minimum inference accuracy of 95.2% within a temperature range of -233K to 398K after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 398K.Comment: Accepted to be published in IEEE ED

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Neuronal reprogramming of mouse and human fibroblasts using transcription factors involved in suprachiasmatic nucleus development

Summary: The hypothalamic suprachiasmatic nucleus (SCN) is composed of heterogenous populations of neurons that express signaling peptides such as vasoactive intestinal polypeptide (VIP) and arginine vasopressin (AVP) and regulate circadian rhythms in behavior and physiology. SCN neurons acquire functional and morphological specializations from waves of transcription factors (TFs) that are expressed during neurogenesis. However, the in vitro generation of SCN neurons has never been achieved. Here we supplemented a highly efficient neuronal conversion protocol with TFs that are expressed during SCN neurogenesis, namely Six3, Six6, Dlx2, and Lhx1. Neurons induced from mouse and human fibroblasts predominantly exhibited neuronal properties such as bipolar or multipolar morphologies, GABAergic neurons with expression of VIP. Our study reveals a critical contribution of these TFs to the development of vasoactive intestinal peptide (Vip) expressing neurons in the SCN, suggesting the regenerative potential of neuronal subtypes contained in the SCN for future SCN regeneration and in vitro disease remodeling

JFIX: Semantics-based repair of Java programs via symbolic PathFinder

Recently there has been a proliferation of automated program repair (APR) techniques, targeting various programming languages. Such techniques can be generally classified into two families: syntactic- and semantics-based. Semantics-based APR, on which we focus, typically uses symbolic execution to infer semantic constraints and then program synthesis to construct repairs conforming to them. While syntactic-based APR techniques have been shown successful on bugs in real-world programs written in both C and Java, semantics-based APR techniques mostly target C programs. This leaves empirical comparisons of the APR families not fully explored, and developers without a Java-based semantics APR technique. We present JFix, a semantics-based APR framework that targets Java, and an associated Eclipse plugin. JFix is implemented atop Symbolic PathFinder, a well-known symbolic execution engine for Java programs. It extends one particular APR technique (Angelix), and is designed to be sufficiently generic to support a variety of such techniques. We demonstrate that semantics-based APR can indeed efficiently and effectively repair a variety of classes of bugs in large real-world Java programs. This supports our claim that the framework can both support developers seeking semantics-based repair of bugs in Java programs, as well as enable larger scale empirical studies comparing syntactic- and semantics-based APR targeting Java. The demonstration of our tool is available via the project website at: https://xuanbachle.github.io/semanticsrepair

Sequence Analysis of a Functional Drosophila Centromere

Centromeres are the site for kinetochore formation and spindle attachment and are embedded in heterochromatin in most eukaryotes. The repeat-rich nature of heterochromatin has hindered obtaining a detailed understanding of the composition and organization of heterochromatic and centromeric DNA sequences. Here, we report the results of extensive sequence analysis of a fully functional centromere present in the Drosophila Dp1187 minichromosome. Approximately 8.4% (31 kb) of the highly repeated satellite DNA (AATAT and TTCTC) was sequenced, representing the largest data set of Drosophila satellite DNA sequence to date. Sequence analysis revealed that the orientation of the arrays is uniform and that individual repeats within the arrays mostly differ by rare, single-base polymorphisms. The entire complex DNA component of this centromere (69.7 kb) was sequenced and assembled. The 39-kb “complex island” Maupiti contains long stretches of a complex A+T rich repeat interspersed with transposon fragments, and most of these elements are organized as direct repeats. Surprisingly, five single, intact transposons are directly inserted at different locations in the AATAT satellite arrays. We find no evidence for centromere-specific sequences within this centromere, providing further evidence for sequence-independent, epigenetic determination of centromere identity and function in higher eukaryotes. Our results also demonstrate that the sequence composition and organization of large regions of centric heterochromatin can be determined, despite the presence of repeated DNA. [Supplemental material is available online at www.genome.org. The sequence data from this study have been submitted to GenBank under accession nos.: Beagle = , F = , 412 = , Bel = , You = , Maupiti = , AATAT = , –, and TTCTC = –, –, –]