Response of carbon isotopic compositions of Early-Middle Permian coals in North China to palaeo-climate change

To investigate the magnitude to which the carbon isotopic ratio (delta C-13) varies in coals in response to their contemporary terrestrial environment, the Early-Middle Permian Huainan coals (including coals from the Shanxi Formation, Lower Shihezi Formation and Upper Shihezi Formation) in North China were systematically sampled. A 2.5 parts per thousand variation range of delta C-13 values (-25.15%o to -22.65%o) was observed in Huainan coals, with an average value of -24.06 parts per thousand. As coal diagenesis exerts little influence on carbon isotope fractionation, delta C-13 values in coals were mainly imparted by those of coal -forming flora assemblages which were linked to the contemporary climate. The delta C-13 values in coals from the Shanxi and Lower Shihezi Formations are variable, reflecting unstable climatic oscillations. Heavy carbon isotope is enriched in coals of the Capitanian Upper Shihezi Formation, implying a shift to high positive delta C-13 values of coeval atmospheric CO2. Notably, our study provides evidence of the Kamura event in the terrestrial environment for the first time

Integrating BIM with building performance analysis in project life-cycle

Adopting Building Information Modelling (BIM) in Building Performance Analysis (BPA) is becoming an emerging research area in the application of information technology in the Architecture, Engineering, and Construction (AEC) industry. To investigate the current state of research in the adoption of BIM in BPA, this study performed a holistic review consisting of a bibliometric analysis of existing literature, content analysis of selected studies, as well as follow-up qualitative discussion in BIM integration with BPA. The bibliometric analysis identified 60 relevant studies; the content analysis of these studies revealed the research focuses of BIM-enabled BPA, including interoperability, semantics, and sustainability rating systems; the qualitative discussion further highlighted the learning process throughout project delivery stages and addressed the potential gap between ‘as-designed’ building performance and ‘as-built’ performance. Overall, this study contributes to existing research by identifying key input attributes and workflow in BPA, reviewing the state-of-the-art research on BIM integration with BPA, and investigating the major research areas, namely, interoperability issues in BIM-enabled BPA within the context of life-cycle BPA

The Global Landscape of Neural Networks: An Overview

One of the major concerns for neural network training is that the non-convexity of the associated loss functions may cause bad landscape. The recent success of neural networks suggests that their loss landscape is not too bad, but what specific results do we know about the landscape? In this article, we review recent findings and results on the global landscape of neural networks. First, we point out that wide neural nets may have sub-optimal local minima under certain assumptions. Second, we discuss a few rigorous results on the geometric properties of wide networks such as "no bad basin", and some modifications that eliminate sub-optimal local minima and/or decreasing paths to infinity. Third, we discuss visualization and empirical explorations of the landscape for practical neural nets. Finally, we briefly discuss some convergence results and their relation to landscape results.Comment: 16 pages. 8 figure

A nearest level PWM method for the MMC in DC distribution grids

For modular multilevel converters (MMCs) applied to medium-voltage DC distribution grids, using the traditional Nearest Level Modulation (NLM) as in HVDC systems can lead to severe current distortion due to significantly reduced module number. This paper proposes a hybrid modulation method combining NLM and Pulse Width Modulation (PWM) where only one module per arm operates under PWM mode. The proposed Nearest Level PWM (NL-PWM) method not only significantly reduces the current distortion, but also avoids the complicated voltage balancing control in each module. The harmonic characteristics of NL-PWM are derived using double Fourier transform, which provides theoretical basis for selecting module number and switching frequency for medium-voltage application in accordance with grid harmonic requirements. Finally, the harmonic characteristics and feasibility of the proposed modulation method are validated by simulation and experimental studies on a MMC with 6 modules per arm. The simulated and experimental results reveal that NL-PWM has better voltage and current harmonic characteristics over NLM and CPS-PWM, thereby suiting the application of MMC with few models

Light the Signal: Optimization of Signal Leakage Attacks against LWE-Based Key Exchange

Key exchange protocols from the learning with errors (LWE) problem share many similarities with the Diffie–Hellman–Merkle (DHM) protocol, which plays a central role in securing our Internet. Therefore, there has been a long time effort in designing authenticated key exchange directly from LWE to mirror the advantages of DHM-based protocols. In this paper, we revisit signal leakage attacks and show that the severity of these attacks against LWE-based (authenticated) key exchange is still underestimated. In particular, by converting the problem of launching a signal leakage attack into a coding problem, we can significantly reduce the needed number of queries to reveal the secret key. Specifically, for DXL-KE we reduce the queries from 1,266 to only 29, while for DBS-KE, we need only 748 queries, a great improvement over the previous 1,074,434 queries. Moreover, our new view of signals as binary codes enables recognizing vulnerable schemes more easily. As such we completely recover the secret key of a password-based authenticated key exchange scheme by Dabra et al. with only 757 queries and partially reveal the secret used in a two-factor authentication by Wang et al. with only one query. The experimental evaluation supports our theoretical analysis and demonstrates the efficiency and effectiveness of our attacks. Our results caution against underestimating the power of signal leakage attacks as they are applicable even in settings with a very restricted number of interactions between adversary and victim

Recurrent Olfactory Neuroblastoma Treated With Cetuximab and Sunitinib: A Case Report

Abstract Olfactory neuroblastoma (ONB) is a rare cancer originating in the olfactory epithelium of the nasal vault. The recurrence rate of ONB is high, as the standard treatment of surgery followed by radiotherapy and/or chemotherapy is usually unsuccessful. The use of targeted therapy based on individual genomic variations after cancer relapse has not been reported. Here, we present the case of a 44-year-old man who was diagnosed with recurrent ONB and treated with a regimen developed using whole exome sequencing. Potential targets were first identified and then matched to appropriate drugs. Gene mutations in the genes encoding EGFR, FGFR2, KDR, and RET were discovered in the patient's tumor tissue by whole exome sequencing and the patient was treated with a combination of the targeted drugs cetuximab and sunitinib. Five days after treatment, enhancement magnetic resonance imaging showed a 65% reduction in tumor size, and the Visual analog scale headache scores went down to 2/10 from 10/10. Repeat imaging at 1 month showed a complete response. This study represents the first demonstration of an effective personalized treatment of ONB by targeted drugs, and sheds light on how precision medicine can be used to treat recurrent ONB that fails to respond to routine tumor resection, radiotherapy, and/or chemotherapy

TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise

Large Language Models (LLMs) exhibit impressive reasoning and data augmentation capabilities in various NLP tasks. However, what about small models? In this work, we propose TeacherLM-7.1B, capable of annotating relevant fundamentals, chain of thought, and common mistakes for most NLP samples, which makes annotation more than just an answer, thus allowing other models to learn "why" instead of just "what". The TeacherLM-7.1B model achieved a zero-shot score of 52.3 on MMLU, surpassing most models with over 100B parameters. Even more remarkable is its data augmentation ability. Based on TeacherLM-7.1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting. The experimental results indicate that the data augmentation provided by TeacherLM has brought significant benefits. We will release the TeacherLM series of models and augmented datasets as open-source.Comment: 5 figures, 15 page