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

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

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

LEGO-Prover: Neural Theorem Proving with Growing Libraries

Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods using language models have demonstrated promising results, but they still struggle to prove even middle school level theorems. One common limitation of these methods is that they assume a fixed theorem library during the whole theorem proving process. However, as we all know, creating new useful theorems or even new theories is not only helpful but crucial and necessary for advancing mathematics and proving harder and deeper results. In this work, we present LEGO-Prover, which employs a growing skill library containing verified lemmas as skills to augment the capability of LLMs used in theorem proving. By constructing the proof modularly, LEGO-Prover enables LLMs to utilize existing skills retrieved from the library and to create new skills during the proving process. These skills are further evolved (by prompting an LLM) to enrich the library on another scale. Modular and reusable skills are constantly added to the library to enable tackling increasingly intricate mathematical problems. Moreover, the learned library further bridges the gap between human proofs and formal proofs by making it easier to impute missing steps. LEGO-Prover advances the state-of-the-art pass rate on miniF2F-valid (48.0% to 57.0%) and miniF2F-test (45.5% to 47.1%). During the proving process, LEGO-Prover also manages to generate over 20,000 skills (theorems/lemmas) and adds them to the growing library. Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47.1% to 50.4%. We also release our code and all the generated skills

Prevalence characteristic of BVDV in some large scale dairy farms in Western China

The aim of this study was to analyze the prevalence characteristic of Bovine viral diarrhea virus (BVDV) in some large scale dairy farms in Western China. BVDV was detected in 30 samples of bulk tank milk (BTM) collected from 30 large dairy farms in 7 provinces of western China, 93.33% (28/30) of the farms were infected with BVDV, and S/P ratio was over 0.3 in 28 positive farms. The individual status was further estimated in the dairy farm (No. 10) with the highest positive rate (S/P ratio = 1.37) and the dairy farm (No. 17) with the lowest positive rate (S/P ratio = 0.39). Two hundred cows were, respectively, selected from calf, young cows and lactating cows in farm No. 10 and farm No. 17 and the serum sample of each enrolled cow was collected. The individual positive rate of serum antibody (Ab) was 87.17% (523/600) in farm No. 10 and 31.33% (188/600) in farm No. 17. The individual positive ratio of serum antibody in calves, young cows and lactating cows were 41.75 % (167/400), 58.75% (235/400) and 77.25% (309/400), respectively. BTM Ab of farm No. 10 has an S/P ratio more than 1.0, which indicated there were emergent or persistent infection (PI) cases, and further test showed that PI cases were 0.51% in farm No. 10. Pathogens were positive in 42.34% (163/385) of nasal mucus samples collected from cows with respiratory symptom, and BVDV cases were 57 in 163 positive samples. Three strains of NCP BVDV-1, one strain of CP BVDV-1, one strain of NCP BVDV-2 and one strain of CP BVDV-2 were successfully isolated. Phylogenetic analysis revealed that the subtypes of BVDV currently prevalent in western China were BVDV-1a, BVDV-1m, BVDV-1q and BVDV-2. The findings suggested that the BVDV infection is serious in some Large Scale Dairy Farms in Western China