TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree transformation

Large-scale language models have made great progress in the field of software engineering in recent years. They can be used for many code-related tasks such as code clone detection, code-to-code search, and method name prediction. However, these large-scale language models based on each code token have several drawbacks: They are usually large in scale, heavily dependent on labels, and require a lot of computing power and time to fine-tune new datasets.Furthermore, code embedding should be performed on the entire code snippet rather than encoding each code token. The main reason for this is that encoding each code token would cause model parameter inflation, resulting in a lot of parameters storing information that we are not very concerned about. In this paper, we propose a novel framework, called TransformCode, that learns about code embeddings in a contrastive learning manner. The framework uses the Transformer encoder as an integral part of the model. We also introduce a novel data augmentation technique called abstract syntax tree transformation: This technique applies syntactic and semantic transformations to the original code snippets to generate more diverse and robust anchor samples. Our proposed framework is both flexible and adaptable: It can be easily extended to other downstream tasks that require code representation such as code clone detection and classification. The framework is also very efficient and scalable: It does not require a large model or a large amount of training data, and can support any programming language.Finally, our framework is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives. To explore the effectiveness of our framework, we conducted extensive experiments on different software engineering tasks using different programming languages and multiple datasets

Multicystic Changes of Juvenile Nasopharyngeal Angiofibroma: The First Case Report in the Literature

Multicystic changes of juvenile nasopharyngeal angiofibroma: the first case report in the literature. Otolaryngologists, pathologists, and radiologists had better pay attention to this infrequent incidence

Nitric oxide-driven modifications of lipoic arm inhibit α-ketoacid dehydrogenases

Pyruvate dehydrogenase complex (PDHC) and oxoglutarate dehydrogenase complex (OGDC), which belong to the mitochondrial α-ketoacid dehydrogenase family, play crucial roles in cellular metabolism. These multi-subunit enzyme complexes use lipoic arms covalently attached to their E2 subunits to transfer an acyl group to coenzyme A (CoA). Here, we report a novel mechanism capable of substantially inhibiting PDHC and OGDC: reactive nitrogen species (RNS) can covalently modify the thiols on their lipoic arms, generating a series of adducts that block catalytic activity. S-Nitroso-CoA, a product between RNS and the E2 subunit\u27s natural substrate, CoA, can efficiently deliver these modifications onto the lipoic arm. We found RNS-mediated inhibition of PDHC and OGDC occurs during classical macrophage activation, driving significant rewiring of cellular metabolism over time. This work provides a new mechanistic link between RNS and mitochondrial metabolism with potential relevance for numerous physiological and pathological conditions in which RNS accumulate

Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensing

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cram\'er-Rao bound (CRB) accordingly.Comment: 32 page

The IPIN 2019 Indoor Localisation Competition—Description and Results

IPIN 2019 Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points. Results presented here are a showcase of state-of-the-art systems tested side by side in real-world settings as part of the on-site real-time competition Tracks. Results for off-site Tracks allow a detailed and reproducible comparison of the most recent positioning and tracking algorithms in the same environment as the on-site Tracks

Multi-Scale Attention 3D Convolutional Network for Multimodal Gesture Recognition

Gesture recognition is an important direction in computer vision research. Information from the hands is crucial in this task. However, current methods consistently achieve attention on hand regions based on estimated keypoints, which will significantly increase both time and complexity, and may lose position information of the hand due to wrong keypoint estimations. Moreover, for dynamic gesture recognition, it is not enough to consider only the attention in the spatial dimension. This paper proposes a multi-scale attention 3D convolutional network for gesture recognition, with a fusion of multimodal data. The proposed network achieves attention mechanisms both locally and globally. The local attention leverages the hand information extracted by the hand detector to focus on the hand region, and reduces the interference of gesture-irrelevant factors. Global attention is achieved in both the human-posture context and the channel context through a dual spatiotemporal attention module. Furthermore, to make full use of the differences between different modalities of data, we designed a multimodal fusion scheme to fuse the features of RGB and depth data. The proposed method is evaluated using the Chalearn LAP Isolated Gesture Dataset and the Briareo Dataset. Experiments on these two datasets prove the effectiveness of our network and show it outperforms many state-of-the-art methods

Effects of Climatic Disturbance on the Trade-Off between the Vegetation Pattern and Water Balance Based on a Novel Model and Accurately Remotely Sensed Data in a Semiarid Basin

Vegetation is a natural link between the atmosphere, soil, and water, and it significantly influences hydrological processes in the context of climate change. Under global warming, vegetation greening significantly aggravates the water conflicts between vegetation water use and water resources in water bodies in arid and semiarid regions. This study established an improved eco-hydrological coupled model with related accurately remotely sensed hydrological data (precipitation and soil moisture levels taken every 3 j with multiply verification) on a large spatio-temporal scale to determine the optimal vegetation coverage (M*), which explored the trade-off relationship between the water supply, based on hydrological balance processes, and the water demand, based on vegetation transpiration under the impact of climate change, in a semiarid basin. Results showed that the average annual actual vegetation coverage (M) in the Hailar River Basin from 1982 to 2012 was 0.62, and that the average optimal vegetation coverage (M*) was 0.56. In 67.23% of the region, M* was lower than M, which aggravated the water stress problem in the Hailar River Basin. By identifying the sensitivity of M* to vegetation characteristics and meteorological parameters, relevant suggestions for vegetation-type planting were proposed. Additionally, we also analyzed the dynamic threshold of vegetation under different climatic conditions, and we found that M was lower than M* under only four of the twenty-eight climatic conditions considered (rainfall increase by 10%, 20%, and 30% with no change in temperature, and rainfall increase by 20% with a temperature increase of 1 °C), thereby meeting the system equilibrium state under the condition of sustainable development. This study revealed the dynamic relationship between vegetation and hydrological processes under the effects of climate change and provided reliable recommendations to support vegetation management and ecological restoration in river basins. The remote sensing data help us to extend the model in a semiarid basin due to its accuracy

A Mass Spectrometry-Cleavable N-Terminal Tagging Strategy for System-Level Protease Activity Profiling

Proteolysis is one of the most important protein post-translational modifications (PTMs) that influences the functions, activities, and structures of nearly all proteins during their lifetime. This irreversible PTM is regulated and catalyzed by proteases through hydrolysis reaction in the process of protein maturation or degradation. The identification of proteolytic substrates is pivotal in understanding the specificity of proteases and the physiological role of proteolytic process. However, tracking these biological phenomena in native cells is very difficult due to their low abundances. Currently, no efficient methods are available to identify proteolytic products from large-scale samples. To facilitate the targeted identification of low-abundant proteolytic products, we devise a strategy incorporating a novel biotinylated reagent PFP (pentafluorophenyl)-Rink-biotin to specifically target, enrich and identify proteolytic N-terminus. Within the PFP-Rink-biotin reagent, an MS-cleavable feature is designed to assist in the unambiguous confirmation of the enriched proteolytic N-termini. This is the first reported study of identifying proteolytic N-terminus by MS-cleavable feature widely adopted in studying low-abundant protein PTMs and cross-linking/MS. The proof-of-concept study was performed with multiple standard proteins whose N-terminus were successfully modified, enriched and identified by a signature ion (SI) in the MS/MS fragmentation, along with the determination of N-terminal peptide sequences by multistage tandem MS of the complementary fragment generated after the cleavage of MS-cleavable bond. For large-scale application, the enrichment and identification of protein N-termini from Escherichia.coli cells were demonstrated along with a bioinformatics workflow. We believe this method can be very useful to locate proteolytic products in native cellular environment with high confidence.<br /