AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization

The rapid growth of information on the Internet has led to an overwhelming amount of opinions and comments on various activities, products, and services. This makes it difficult and time-consuming for users to process all the available information when making decisions. Text summarization, a Natural Language Processing (NLP) task, has been widely explored to help users quickly retrieve relevant information by generating short and salient content from long or multiple documents. Recent advances in pre-trained language models, such as ChatGPT, have demonstrated the potential of Large Language Models (LLMs) in text generation. However, LLMs require massive amounts of data and resources and are challenging to implement as offline applications. Furthermore, existing text summarization approaches often lack the ``adaptive" nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific requirements or preferences. In this paper, we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews, which effectively captures the adaptive nature required for opinion summarization. The model generates aspect-oriented summaries given a set of reviews for a particular product, efficiently providing users with useful information on specific aspects they are interested in, ensuring the generated summaries are more personalized and informative. Extensive experiments have been conducted using real-world datasets to evaluate the proposed model. The results demonstrate that our model outperforms state-of-the-art approaches and is adaptive and efficient in generating summaries that focus on particular aspects, enabling users to make well-informed decisions and catering to their diverse interests and preferences.Comment: 21 pages, 4 figures, 7 table

Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development

MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.Comment: Accepted by 60th DA

Endolymphatic sac tumor: case report and review of the literature

Endolymphatic sac tumor (ELST) is a rare neoplasm which can be encountered sporadically or in Von Hippel-Lindau (VHL) disease. Here we report a sporadic case of ELST in 31-year-old man. Neither the symptoms nor a family history of VHL disease were found in the patient. CT imaging demonstrated an expansile lytic lesion of the mastoid process of the left petrous bone. MR scanning revealed a 5.2 cm × 4.7 cm × 4.2 cm mass which showed hyperintensity on T1- and T2-weighted images. Histologic sections showed a papillary, cystic or glandular architecture. The papillary and glandular structures were lined by a single layer of flattened cuboidal-to-columnar cells. The stroma of the papillary fronds was richly vascularized and chronically inflamed. The tumor showed diffusely positive reactivity with cytokeratin (Pan), cytokeratin 19, cytokeratin 5/6, cytokeratin 7, EMA, vimentin, CD56, and NSE and also showed variable reactivity with glial fibrillary acidic protein (GFAP) and VEGF. The Ki-67 immunostain showed a proliferation index of < 1%. Because the mass was large, it was difficult to extirpate surgically. After surgery, the patient underwent gamma-knife radiosurgery for residual tumor. The findings indicate that ELST is a rare neoplasm with benign histopathological appearance and clinically destructive behavior. Because of the rarity of this tumor, it can easily be confused with other tumors such as paraganglioma, middle ear adenoma, adenocarcinoma, papillary carcinoma of thyroid or choroid plexus papilloma. Owing to its locally aggressive nature, it is difficult to extirpate surgically when it is large

Solvent-triggered reversible interconversion of all-nitrogen-donor-protected silver nanoclusters and their responsive optical properties.

Surface organic ligands are critical in determining the formation and properties of atomically precise metal nanoclusters. In contrast to the conventionally used thiolate, phosphine and alkynyl ligands, the amine ligand dipyridylamine is applied here as a protecting agent in the synthesis of atomically precise metal nanoclusters. We report two homoleptic amido-protected Ag nanoclusters as examples of all-nitrogen-donor-protected metal nanoclusters: [Ag21(dpa)12]SbF6 (Ag21) and [Ag22(dpa)12](SbF6)2 (Ag22) (dpa = dipyridylamido). Single crystal X-ray structural analysis reveals that both clusters consist of a centered-icosahedron Ag13 core wrapped by 12 dpa ligands. The flexible arrangement of the N donors in dpa facilitates the solvent-triggered reversible interconversion between Ag21 and Ag22 due to their very different solubility. The successful use of dpa in the synthesis of well-defined silver nanoclusters may motivate more studies on metal nanoclusters protected by amido type ligands

An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier

Copyright 2013 Quan Zou et al. his is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Accelerated Li⁺ Desolvation for Diffusion Booster Enabling Low‐Temperature Sulfur Redox Kinetics via Electrocatalytic Carbon‐Grazfted‐CoP Porous Nanosheets

Lithium–sulfur (Li–S) batteries are famous for their high energy density and low cost, but prevented by sluggish redox kinetics of sulfur species due to depressive Li ion diffusion kinetics, especially under low-temperature environment. Herein, a combined strategy of electrocatalysis and pore sieving effect is put forward to dissociate the Li+ solvation structure to stimulate the free Li+ diffusion, further improving sulfur redox reaction kinetics. As a protocol, an electrocatalytic porous diffusion-boosted nitrogen-doped carbon-grafted-CoP nanosheet is designed via forming the NCoP active structure to release more free Li+ to react with sulfur species, as fully investigated by electrochemical tests, theoretical simulations and in situ/ex situ characterizations. As a result, the cells with diffusion booster achieve desirable lifespan of 800 cycles at 2 C and excellent rate capability (775 mAh g−1 at 3 C). Impressively, in a condition of high mass loading or low-temperature environment, the cell with 5.7 mg cm−2 stabilizes an areal capacity of 3.2 mAh cm−2 and the charming capacity of 647 mAh g−1 is obtained under 0 °C after 80 cycles, demonstrating a promising route of providing more free Li ions toward practical high-energy Li–S batteries

Genetic characterization of H1N2 influenza a virus isolated from sick pigs in Southern China in 2010

In China H3N2 and H1N1 swine influenza viruses have been circulating for many years. In January 2010, before swine were infected with foot and mouth disease in Guangdong, some pigs have shown flu-like symptoms: cough, sneeze, runny nose and fever. We collected the nasopharyngeal swab of all sick pigs as much as possible. One subtype H1N2 influenza viruses were isolated from the pig population. The complete genome of one isolate, designated A/swine/Guangdong/1/2010(H1N2), was sequenced and compared with sequences available in GenBank. The nucleotide sequences of all eight viral RNA segments were determined, and then phylogenetic analysis was performed using the neighbor-joining method. HA, NP, M and NS were shown to be closely to swine origin. PB2 and PA were close to avian origin, but NA and PB1were close to human origin. It is a result of a multiple reassortment event. In conclusion, our finding provides further evidence about the interspecies transmission of avian influenza viruses to pigs and emphasizes the importance of reinforcing swine influenza virus (SIV) surveillance, especially before the emergence of highly pathogenic FMDs in pigs in Guangdong

Neuroform EZ Stenting for Symptomatic Intracranial Artery Stenosis: 30 Days Outcomes in a High-Volume Stroke Center

Objective: To test whether Neuroform EZ stent placement combined with the modified techniques in symptomatic severe intracranial stenosis (ICAS) would result in lower rates of peri-procedural complications of intracranial stenting.Methods: We retrospectively reviewed the clinical data from 71 consecutive patients who underwent Neuroform EZ stent placement combined with the modified techniques for symptomatic severe ICAS at our institute between January 2016 and October 2017. The primary outcomes were ipsi-lateral ischemic stroke, intra-cerebral hemorrhage, or death within 30 days after stenting. The secondary outcome was technical success.Results: The technical success rate was 100%. The mean pre and post-stent stenoses were 84.2% ± 9.1% (median 85%, IQR75% to 90%) and 16.9% ± 10.2 % (median 15%, IQR 10% to 25%). The frequency of ipsi-lateral stroke, intra-cerebral hemorrhage, or death within 30 days was 0%.Conclusions: The combined use of Neuroform EZ stent placement and the modified techniques for symptomatic severe ICAS is technically feasible and safe, with very low peri-procedural complications. Further studies are required to assess the long-term results of this approach