Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

Systems analysis of auxin transport in the Arabidopsis root apex

Auxin is a key regulator of plant growth and development. Within the root tip, auxin distribution plays a crucial role specifying developmental zones and coordinating tropic responses. Determining how the organ-scale auxin pattern is regulated at the cellular scale is essential to understanding how these processes are controlled. In this study, we developed an auxin transport model based on actual root cell geometries and carrier subcellular localizations. We tested model predictions using the DII-VENUS auxin sensor in conjunction with state-of-the-art segmentation tools. Our study revealed that auxin efflux carriers alone cannot create the pattern of auxin distribution at the root tip and that AUX1/LAX influx carriers are also required. We observed that AUX1 in lateral root cap (LRC) and elongating epidermal cells greatly enhance auxin’s shootward flux, with this flux being predominantly through the LRC, entering the epidermal cells only as they enter the elongation zone. We conclude that the nonpolar AUX1/LAX influx carriers control which tissues have high auxin levels, whereas the polar PIN carriers control the direction of auxin transport within these tissues

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Hypert: hypernymy-aware BERT with Hearst pattern exploitation for hypernym discovery

Abstract Hypernym discovery is challenging because it aims to find suitable instances for a given hyponym from a predefined hypernym vocabulary. Existing hypernym discovery methods used supervised learning with word embedding from word2vec. However, word2vec embedding suffers from low embedding quality regarding unseen or rare noun phrases because entire noun phrases are embedded into a single vector. Recently, prompting methods have attempted to find hypernyms using pretrained language models with masked prompts. Although language models alleviate the problem of w embeddings, general-purpose language models are ineffective for capturing hypernym relationships. Considering the hypernym relationship to be a linguistic domain, we introduce Hypert, which is further pretrained using masked language modeling with Hearst pattern sentences. To the best of our knowledge, this is the first attempt in the hypernym relationship discovery field. We also present a fine-tuning strategy for training Hypert with special input prompts for the hypernym discovery task. The proposed method outperformed the comparison methods and achieved statistically significant results in three subtasks of hypernym discovery. Additionally, we demonstrate the effectiveness of the several proposed components through an in-depth analysis. The code is available at: https://github.com/Gun1Yun/Hypert

Sepsis diagnosis and treatment using nanomaterials

Sepsis is a life-threatening reaction that occurs when the body's severe response to an infection damages the host's own tissues. Sepsis has been globally recognized as a fatal disease. Rapid treatment of sepsis requires prompt identification, administering antibiotics, careful hemodynamic support, and treating the cause of the infection. Clinical outcomes of sepsis depend on early diagnosis and appropriate treatment. Unfortunately, current sepsis diagnosis and treatment, such as polymerase chain reaction-based assay, blood culture assay, and antibiotic therapy, are ineffective; consequently, sepsis-related mortality remains high and increases antimicrobial resistance. To overcome this challenge, nanotechnology, which involves engineering at a nanoscale, is used for diagnosing and treating sepsis. Preclinical models have shown protective effects and potential utility in managing septic shock. Furthermore, nanotechnology treatments based on diverse materials result in the effective treatment of sepsis, improving the survival rate. In this review, we present an overview of the recent research advancements in nanotechnology to diagnose and treat sepsis with a brief introduction to sepsis.11Nsciescopuskc

Flexible 3D Electrodes of Free-Standing TiN Nanotube Arrays Grown by Atomic Layer Deposition with a Ti Interlayer as an Adhesion Promoter

Nanostructured electrodes and their flexible integrated systems have great potential for many applications, including electrochemical energy storage, electrocatalysis and solid-state memory devices, given their ability to improve faradaic reaction sites by large surface area. Although many processing techniques have been employed to fabricate nanostructured electrodes onto flexible substrates, these present limitations in terms of achieving flexible electrodes with high mechanical stability. In this study, the adhesion, mechanical properties and flexibility of TiN nanotube arrays on a Pt substrate were improved using a Ti interlayer. Highly ordered and well-aligned TiN nanotube arrays were fabricated on a Pt substrate using a template-assisted method with an anodic aluminum oxide (AAO) template and atomic layer deposition (ALD) system. We show that with the use of a Ti interlayer between the TiN nanotube arrays and Pt substrate, the TiN nanotube arrays could perfectly attach to the Pt substrate without delamination and faceted phenomena. Furthermore, the I-V curve measurements confirmed that the electric contact between the TiN nanotube arrays and substrate for use as an electrode was excellent, and its flexibility was also good for use in flexible electronic devices. Future efforts will be directed toward the fabrication of embedded electrodes in flexible plastic substrates by employing the concepts demonstrated in this study

EcoProDB: the Escherichia coli protein database

EcoProDB is a web-based database for comparative proteomics of Escherichia coli. The database contains information on E. coli proteins identified on 2D gels along with other resources collected from various databases and published literature, with a special feature of showing the expression levels of E. coli proteins under different genetic and environmental conditions. It also provides comparative information of subcellular localization, theoretical 2D map, experimental 2D map and integrated protein information via an interactive web interface and application such as the Map Browser. Users can also upload their own 2D gels, extract core information associated with the proteins and 2D gel results from different experiments and consequently generate new knowledge and hypotheses for further studies.11Nsciescopu