171 research outputs found
Efficient Algorithms for Node Disjoint Subgraph Homeomorphism Determination
Recently, great efforts have been dedicated to researches on the management
of large scale graph based data such as WWW, social networks, biological
networks. In the study of graph based data management, node disjoint subgraph
homeomorphism relation between graphs is more suitable than (sub)graph
isomorphism in many cases, especially in those cases that node skipping and
node mismatching are allowed. However, no efficient node disjoint subgraph
homeomorphism determination (ndSHD) algorithms have been available. In this
paper, we propose two computationally efficient ndSHD algorithms based on state
spaces searching with backtracking, which employ many heuristics to prune the
search spaces. Experimental results on synthetic data sets show that the
proposed algorithms are efficient, require relative little time in most of the
testing cases, can scale to large or dense graphs, and can accommodate to more
complex fuzzy matching cases.Comment: 15 pages, 11 figures, submitted to DASFAA 200
Additive Manufacturing of Sn63Pb37 Component by Micro-coating
AbstractMicro-coating is a novel technology to build near-net component layer by layer, which uses a crucible and nozzle instead of a weld head and wire feeder to supply material compared with shaped metal deposition. A pneumatic system is adopted to adjust liquid metal flow rate and the layer height is controlled by the distance between nozzle and substrate. Height and width of a single channel are measured by confocal microscopy, it is found that the error between numerical results and experiment are 5.5% and 1.1%. Tensile stress vertically to the deposition layers reaches to 40.89Mpa, while tensile stress parallel to the deposition layers gives a value of 43.14Mpa. Yield stress of vertically and parallel to the layer are respectively 34.28Mpa and 35.23Mpa. Specimens exhibit better mechanical properties than casting component, whose tensile stress and yield stress are respectively 36.51Mpa and 29.25Mpa
Research on Mobile Network High-precision Absolute Time Synchronization based on TAP
With the development of mobile communication and industrial internet
technologies, the demand for robust absolute time synchronization based on
network for diverse scenarios is significantly growing. TAP is a novel network
timing method that aims to achieve sub-microsecond synchronization over air
interface. This paper investigates the improvement and end-to-end realization
of TAP. This paper first analyzes the effectiveness and deficiencies of TAP by
establishing an equivalent clock model which evaluates TAP from timing error
composition and allan variance. Second, this paper proposes a detailed base
station and terminal design and the corresponding improvement of TAP. Both
hardware compensation and protocol software design are taken into account so as
to minimize timing error and system cost while maximizing compatibility with
3GPP. Finally, this paper presents a TAP end-to-end 5G prototype system
developed based on software defined radio base station and COTS baseband
module. The field test results show that the proposed scheme effectively solves
the problems of TAP in application and robustly achieves 200ns level timing
accuracy in various situations. The average accuracy with long observations can
reach 1 nanosecond. It is 23 orders of magnitude better than common
network timing methods, including NTP, PTP and the original TAP
Utterance Augmentation for Speaker Recognition
The speaker recognition problem is to automatically recognize a person from their voice. The training of a speaker recognition model typically requires a very large training corpus, e.g., multiple voice samples from a very large number of individuals. In the diverse domains of application of speaker recognition, it is often impractical to obtain a training corpus of the requisite size. This disclosure describes techniques that augment utterances, e.g., by cutting, splitting, shuffling, etc., such that the need for collections of raw voice samples from individuals is substantially reduced. In effect, the original model works better on the augmented utterances on the target domain
Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker
Abstract(#br)The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare
Higher radiation doses after partial laryngectomy may raise the incidence of pneumonia: A retrospective cohort study
BackgroundCurrently, studies have shown that a high dose of radiotherapy to the throat have various harmful and adverse effects on the patients’ laryngeal function, resulting in the development of pneumonia. This study aimed to explore how radiotherapy dose affected the probability of pneumonia following laryngeal cancer surgery.Materials and methodsA retrospective analysis was done on patients diagnosed with laryngeal cancer between 2010 and 2020 and were treated surgically and with postoperative radiotherapy in the same institution. This study included 108 patients in total, 51 of who were in the low-dose group and 57 of whom were in the high-dose group. Age, gender, the location of laryngeal cancer, the presence or absence of lymph node metastasis, and other demographic and clinical characteristics were collected, and the prevalence of postoperative pneumonia was compared between the two groups.ResultsThe total prevalence of postoperative pneumonia was 59.3%, but there was a significant difference between the two groups(high-dose group 71.9% VS low-dose group 45.1%; p=0.005). A total of 9.3% (10/108) of the patients had readmission due to severe pneumonia, and the rate of readmission due to pneumonia was significantly different between the two groups (high-dose group 15.8% VS low-dose group 2.0%, p=0.032). Additionally, the high-dose group’s prevalence of Dysphagia was significantly higher than the low-dose group’s. According to multivariate logistic modeling, high-dose radiation was a risk factor for pneumonia (OR=4.224, 95%CI =1.603-11.131, p=0.004).ConclusionPneumonia risk could increase with radiotherapy doses > 50 Gy in the treatment of laryngeal cancer. Therefore, we recommend that when the radiation dose surpasses 50Gy, doctors should pay particular attention to the lung health of patients with laryngeal cancer
Low-cost flexible plasmonic nanobump metasurfaces for label-free sensing of serum tumor marker.
The use of plasmonic metasurface for sensing has great potential on label-free detection of human tumor markers, which could benefit clinical examination. In this work, we adopt nanoimprint and plasma etching to optimize the nanofabrication for low-cost flexible plasmonic metasurface sensors with gold nanobump arrays, which enable facile surface bio-functionality, high sensitivity and simple optical measurement in the visible range. A high bulk refractive index sensitivity of 454.4 nm/RIU is achieved for the prototype plasmonic metasurface sensors at the wavelengths from 620 nm to 720 nm. The rapid quantitative tumor marker sensing of carcinoembryonic antigen in human serum samples from less than 10 ng/mL to more than 87 ng/mL is achieved, which demonstrates good agreement with the conventional chemiluminescence immunoassay system and sufficiently covers the threshold tumor marker concentration of 20 ng/mL for early cancer prediction. Our method is capable of low-cost high-throughput manufacturing for flexible lightweight plasmonic metasurface sensors, which will facilitate wide applications on portable biomedical sensing devices for future point-of-care diagnosis and mobile healthcare
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem
statements sourced from the International Mathematical Olympiad (IMO)
Shortlisted Problems. Designed to facilitate advanced automated theorem proving
at the IMO level, FIMO is currently tailored for the Lean formal language. It
comprises 149 formal problem statements, accompanied by both informal problem
descriptions and their corresponding LaTeX-based informal proofs. Through
initial experiments involving GPT-4, our findings underscore the existing
limitations in current methodologies, indicating a substantial journey ahead
before achieving satisfactory IMO-level automated theorem proving outcomes
Factors affecting the production of sugarcane yield and sucrose accumulation: suggested potential biological solutions
Environmental stresses are the main constraints on agricultural productivity and food security worldwide. This issue is worsened by abrupt and severe changes in global climate. The formation of sugarcane yield and the accumulation of sucrose are significantly influenced by biotic and abiotic stresses. Understanding the biochemical, physiological, and environmental phenomena associated with these stresses is essential to increase crop production. This review explores the effect of environmental factors on sucrose content and sugarcane yield and highlights the negative effects of insufficient water supply, temperature fluctuations, insect pests, and diseases. This article also explains the mechanism of reactive oxygen species (ROS), the role of different metabolites under environmental stresses, and highlights the function of environmental stress-related resistance genes in sugarcane. This review further discusses sugarcane crop improvement approaches, with a focus on endophytic mechanism and consortium endophyte application in sugarcane plants. Endophytes are vital in plant defense; they produce bioactive molecules that act as biocontrol agents to enhance plant immune systems and modify environmental responses through interaction with plants. This review provides an overview of internal mechanisms to enhance sugarcane plant growth and environmental resistance and offers new ideas for improving sugarcane plant fitness and crop productivity
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring
the reasoning ability of the recent successful generative language models.
However, current ATP benchmarks mainly focus on symbolic inference, but rarely
involve the understanding of complex number combination reasoning. In this
work, we propose TRIGO, an ATP benchmark that not only requires a model to
reduce a trigonometric expression with step-by-step proofs but also evaluates a
generative LM's reasoning ability on formulas and its capability to manipulate,
group, and factor number terms. We gather trigonometric expressions and their
reduced forms from the web, annotate the simplification process manually, and
translate it into the Lean formal language system. We then automatically
generate additional examples from the annotated samples to expand the dataset.
Furthermore, we develop an automatic generator based on Lean-Gym to create
dataset splits of varying difficulties and distributions in order to thoroughly
analyze the model's generalization ability. Our extensive experiments show our
proposed TRIGO poses a new challenge for advanced generative LM's including
GPT-4 which is pre-trained on a considerable amount of open-source formal
theorem-proving language data, and provide a new tool to study the generative
LM's ability on both formal and mathematical reasoning.Comment: Accepted by EMNLP 2023. Code is available at
https://github.com/menik1126/TRIG
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