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All animals must maintain genome and proteome integrity, especially when experiencing endogenous or exogenous stress. To cope, organisms have evolved sophisticated and conserved response systems: unfolded protein responses (UPRs) ensure proteostasis, while DNA damage responses (DDRs) maintain genome integrity. Emerging evidence suggests that UPRs and DDRs crosstalk, but this remains poorly understood. Here, we demonstrate that depletion of the DNA primases pri-1 or pri-2 , which synthesize RNA primers at replication forks and whose inactivation causes DNA damage, activates the UPR of the endoplasmic reticulum (UPR-ER) in Caenorhabditis elegans , with especially strong activation in the germline. We observed activation of both the inositol-requiring-enzyme 1 (ire-1) and the protein kinase RNA-like endoplasmic reticulum kinase (pek-1) branches of the (UPR-ER). Interestingly, activation of the (UPR-ER) output gene heat shock protein 4 (hsp-4) was partially independent of its canonical activators, ire-1 and X-box binding protein (xbp-1), and instead required the third branch of the (UPR-ER), activating transcription factor 6 (atf-6), suggesting functional redundancy. We further found that primase depletion specifically induces the (UPR-ER), but not the distinct cytosolic or mitochondrial UPRs, suggesting that primase inactivation causes compartment-specific rather than global stress. Functionally, loss of ire-1 or pek-1 sensitizes animals to replication stress caused by hydroxyurea. Finally, transcriptome analysis of pri-1 embryos revealed several deregulated processes that could cause (UPR-ER) activation, including protein glycosylation, calcium signaling, and fatty acid desaturation. Together, our data show that the (UPR-ER), but not other UPRs, responds to replication fork stress and that the (UPR-ER) is required to alleviate this stress. [ABSTRACT FROM AUTHOR]

Small interfering RNA (siRNA)drugs have great potential in the field of anti-tumor, but they have poor biological stability and are easily degraded by nucleases in vivo, resulting in poor bioavailability. In this study, a novel albumin Poly(lactide-co-glycolide) (PLGA) nanocarrier was prepared to efficiently deliver siRNA to glioma cells to achieve tumor-targeted therapy. Recombinant VAR2CSA protein (RVP) targeting albumin PLGA nanocarriers (RVP–APN–siRNA) loaded with EGFR-siRNA were prepared by a double emulsion method. Determine its characterization performance, loading rate, in vitro release and cell uptake, and investigate the effects of nanoparticles on the cytotoxicity, proliferation and apoptosis of brain glioma cells. The prepared nanoparticles are spherical with uniform particle size, the average particle size distribution is 120–180 nm, the average potential distribution is 20–30 mv, and the loading rate is 82.36 ± 1.84%. The release rate of siRNA within 24 h is 93%, with low cytotoxicity and targeting performance, effectively inhibiting the activity of glioma cells. The prepared albumin nanoparticles have good physical and chemical properties, and have dual functions of targeted recognition of glioma cells and tumor treatment. They are efficient and safe nano-carriers for tumor-targeted treatment, and have good potential in the application of siRNA-targeted drug delivery. [ABSTRACT FROM AUTHOR]

AbstractClustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorithms can be computationally costly in high dimensions and can struggle with noisy data that has missing values. Furthermore, except for one algorithm, no others provide theoretical guarantees of clustering accuracy. In this article, we propose a general categorical data encoding method and a computationally efficient spectral-based algorithm to cluster high-dimensional noisy categorical data (nominal or ordinal). Under a statistical model for data on m attributes from n subjects in r clusters with missing probability ϵ, we show that our algorithm exactly recovers the true clusters with high probability when mn(1−ϵ)≥CMr2 log 3M, with M=max(n,m) and a fixed constant C. In addition, we show that mn(1−ϵ)2≥rδ/2 with 0<δ<1 is necessary for any algorithm to succeed with probability at least (1+δ)/2. In cases where m = n and r are fixed, the sufficient condition matches with the necessary condition up to a polylog(n) factor. In numerical studies our algorithm outperforms several existing algorithms in both clustering accuracy and computational efficiency. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Background: Time-restricted eating (TRE), a feasible form of intermittent fasting, has been proven to benefit metabolic health in animal models and humans. To our knowledge, specific guidance on the appropriate period for eating during TRE has not yet been promoted. Therefore, to compare and assess the relative effectiveness estimates and rankings of TRE with different eating windows on human metabolic health, we conducted a systematic review and network meta-analysis (NMA). Method: PubMed, EMBASE and the Cochrane Library were searched for randomized controlled trials that compared different eating windows on human metabolic health for adults. A Bayesian NMA was used to compare direct and indirect effects to determine the best different eating windows, and scientific evidence using GRADE. Results: Twenty-seven RCTs comparing TRE with different eating windows on human metabolic health were reviewed, and all were included in the NMA. Compared with the normal diet group (non-TRE), the TRE group has certain benefits in reducing weight and fasting insulin. In terms of reducing fasting insulin, the 18:6 group (eating time = 6 h) was better than the 14:10 group (eating time = 10 h) and 16:8 group (eating time = 8 h) (P < 0.05); The < 6 group (eating time < 6 h) was better than the 14:10 group (P < 0.05). In terms of reducing fasting glucose, the < 6 group was better than the 14:10 group (P < 0.05). There were no statistical variations in weight, HDL, TG, and LDL across the different modes of TRE (P > 0.05). Conclusions: Our research showed that no particular metabolic advantages of various eating windows were found. Therefore, our results suggested that different eating windows could promote similar benefits for metabolic parameters. [ABSTRACT FROM AUTHOR]

To reduce the energy consumption of the hydrogen liquefaction process, a hydrogen liquefaction process based on cascaded mixed refrigerant cycles is proposed in this paper. The two mixed refrigerant cycles are connected in series so that the cooling capacity of refrigerants can be fully utilized. Cascaded processes with different numbers of pre-cooling stages are compared. The processes are optimized using the particle swarm algorithm. And cascaded and non-cascaded processes are compared under the same conditions. The results show that the more the number of pre-cooling stages, the lower the SEC and the higher the exergy efficiency. And the cascaded process has higher energy efficiency than the non-cascaded process. The specific power consumption of the process is 5.664 kWh/kg LH2 , the coefficient of performance is 0.2322, and the exergy efficiency is 52.77%. This research can provide valuable ideas and methods for the improvement of hydrogen liquefaction process design. • A cascaded dual mixed refrigerant hydrogen liquefaction process is put up. • The cascaded process has higher energy efficiency than the non-cascaded process. • The SEC of the process is 5.664 kWh/kg LH2 and the exergy efficiency is 52.77%. • The efficiency of this liquefaction process reaches the highest level currently. [ABSTRACT FROM AUTHOR]

Objective: Leukodystrophies are a diverse group of rare inherited disorders that affect the white matter of the central nervous system with a wide phenotypic spectrum. We aimed to characterize the clinical and genetic features of leukodystrophies in a central‐southern Chinese cohort. Methods: A cohort of 16 Chinese probands with leukodystrophy was recruited and performed genetic analysis by targeted panels or whole‐exome sequencing. Further functional analysis of identified mutations in the colony stimulating factor 1 receptor (CSF1R) gene was explored. Results: A total of eight pathogenic variants (3 novel, 5 documented) were identified in genes including AARS2, ABCD1, CSF1R, and GALC. Common symptoms of leukodystrophy such as cognitive decline, behavioral symptoms, bradykinesia, and spasticity were observed in mutation carriers as well as other rare features (e.g., seizure, dysarthric, and vision impairment). Overexpressing CSF1R mutants p.M875I and p.F971Sfs*7 in vitro showed pronounced cleavage CSF1R and suppressed protein expression, respectively, and reduced transcripts of both mutants were observed. CSF1 treatment revealed deficient and suppressed CSF1R phospho‐activation with the mutants. In contrast to the plasma membrane and endoplasmic reticulum (ER) localized wild‐type CSF1R, M875I mutant showed much less membrane association and greater detainment in the ER, whereas F971Sfs*7 mutation led to aberrant non‐ER localization. Both mutations caused suppressed cell viability, which was partially resulted from deficient/suppressed CSF1R‐ERK signaling. Interpretation: In summary, our findings expand the mutation spectrum of these genes in leukodystrophies. Supported by in vitro validation of the pathogenicity of heterozygous CSF1R mutations, our data also provide insights into the pathogenic mechanisms of CSF1R‐related leukodystrophy. [ABSTRACT FROM AUTHOR]

This study evaluated the effect of Lentinus edodes fermentation (LEF) on digestive enzyme activity, antioxidant capacity and morphology of the liver and intestine in largemouth bass (Micropterus salmoides) fed high plant protein diets (HPPD). LEF was supplemented in HPPD with 0 g kg−1 (LEF0), 10 g kg−1 (LEF1), 20 g kg−1 (LEF2), 30 g kg−1 (LEF3), 40 g kg−1 (LEF4), 50 g kg−1 (LEF5), respectively, and then the six diets were fed to largemouth bass with a body weight of 28.8 ± 0.05 g for eight weeks. Juvenile fish were randomized into 6 groups and each group had 4 replicates with 40 fish. Dietary LEF supplementation alleviated the liver inflammatory reaction of largemouth bass caused by HPPD and improved liver morphology. Goblet cells multiplied and the gut muscle layer thickened after LEF supplementation. The LEF significantly increased amylase activity in the liver and intestine of largemouth bass in individual experimental groups. The LEF could increase the activity of catalase in the liver and intestine of largemouth bass (p < 0.05). The content of malondialdehyde was significantly lower than that in the control group (p < 0.05). Dietary LEF supplementation had no significant effect on the intestinal flora of largemouth bass. These findings imply that LEF supplementation can reduce liver inflammation, enhance intestinal tissue morphology, and eventually benefit largemouth bass health. [ABSTRACT FROM AUTHOR]

Although the advantage of spike timing-based over rate-based network computation has been recognized, the underlying mechanism remains unclear. Using Tempotron and Perceptron as elementary neural models, we examined the intrinsic difference between spike timing-based and rate-based computations. For more direct comparison, we modified Tempotron computation into rate-based computation with the retention of some temporal information. Previous studies have shown that spike timing-based computation are computationally more powerful than rate-based computation in terms of the number of computational units required and the capability in classifying random patterns. Our study showed that spike timing-based and rate-based Tempotron computations provided similar capability in classifying random spike patterns, as well as in text sentiment classification and spam text detection. However, spike timing-based computation is superior in performing a task involving discriminating forward vs. reverse sequence of events, i.e., information mainly temporal in nature. Further studies revealed that this superiority required the asymmetry in the profile of the postsynaptic potential (PSP), and that temporal sequence information was converted to biased spatial distribution of synaptic weight modifications during learning. Thus, the intrinsic PSP asymmetry is a mechanistic basis for the high efficiency of spike timing-based computation for processing temporal information. [ABSTRACT FROM AUTHOR]

It is of great significance to control the energy, location, topological charge, and other parameters of each beam in three-dimensional space. Thus, optical array is proposed and widely used in processing, communication, microscopy, storage, and other fields. However, the current methods to generate optical array are only effective in specific scenarios. In this work, we present an efficient and general method, which separates iterative calculation and parameter selection by introducing a special mapping space, allowing the generation of optical arrays with arbitrary dimensions, optional parameters, and any energy ratios. This method is compared with traditional methods from both simulation and experiment and is superior in diffraction efficiency, focus amount, and calculation speed. Additionally, a five-dimensional optical array with specially designed parameters is produced to show the versatility for different parameters. It shows advantages in beam shaping and parallelization and will be instrumental for applications in information storage, industrial processing, and three-dimensional imaging. [ABSTRACT FROM AUTHOR]

The internal defect detection of solar cells indifferent production processes currently adopts manual visual verification on the images captured by electroluminescence or photoluminescence system. To improve the efficiency and reliability of the inspection, this article proposes a generic and automatic component-of-interest superposition graph(CISG) method. First, the solar cell inspection region is located by shape-based matching. Second, based on the properties of Fourier transform and singular value decomposition, the frequency components of the testing image are modified adaptively, and then component graphs with different local saliency are generated. According to the normalized marginal gains of energy, these component graphs are combined into a saliency map. Finally, defective regions are extracted from the saliency map using the global threshold technique. By employing various compared approaches to conduct experiments on five datasets from different production processes, the great performance of our presented method is revealed, which also implies this method outperforms the current methods. [ABSTRACT FROM AUTHOR]

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are complete graphs with correlated Gaussian weights and the Erdős-Rényi model where the two graphs are subsampled from a common parent Erdős-Rényi graph ${\mathcal {G}}(n,p)$. For dense Erdős-Rényi graphs with $p=n^{-o(1)}$ , we prove that there exists a sharp threshold, above which one can correctly match all but a vanishing fraction of vertices and below which correctly matching any positive fraction is impossible, a phenomenon known as the “all-or-nothing” phase transition. Even more strikingly, in the Gaussian setting, above the threshold all vertices can be exactly matched with high probability. In contrast, for sparse Erdős-Rényi graphs with $p=n^{-\Theta (1)}$ , we show that the all-or-nothing phenomenon no longer holds and we determine the thresholds up to a constant factor. Along the way, we also derive the sharp threshold for exact recovery, sharpening the existing results in Erdős-Rényi graphs. The proof of the negative results builds upon a tight characterization of the mutual information based on the truncated second-moment computation and an “area theorem” that relates the mutual information to the integral of the reconstruction error. The positive results follows from a tight analysis of the maximum likelihood estimator that takes into account the cycle structure of the induced permutation on the edges. [ABSTRACT FROM AUTHOR]

The monocrystalline solar cell (MSC) interior is prone to miscellaneous defects that affect energy conversion efficiency and even cause fatal damage to the photovoltaic module. In this study, an automatic defect inspection method for MSC interior is presented. Electroluminescence (EL) imaging technology is utilized to visualize defects inside MSC. Also, accurate cell positioning is the precondition of full inspection, so a sigmoid-Hough-transform-based geometric segmentation (SHTGS) algorithm is designed to extract the complete cell region in the EL image, even though the fuzzy boundaries of the cell contain defects. Furthermore, a self-comparison method (SCM) is proposed to detect defects in the background with nonuniform luminance and complicated texture. The experimental results verify the effectiveness of this method in terms of inspection speed and recognition rate. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for lithium-ion battery electrode (LIBE) surface quality assurance. First, based on machine vision, an automatic optical inspection system is developed to check defects on LIBE. In addition, a background normalization algorithm is put forward to preprocess the large-scale LIBE with inhomogeneous thickness in uneven illumination. With the help of the auto-concentration compensation algorithm, flaws can be extracted precisely. Moreover, after characterizing the defects, features machine applied partiality parameter automatic adjustment method and partiality decision rules are exerted for defects accurate classification, which provides near-optimal performance and reduces the complexity of tuning parameters. The proposed method is computationally efficient and satisfies real-time online inspection requirement. Experimental results verify the effectiveness and performance of the proposed method according to the inspection speed and recognition rate. [ABSTRACT FROM AUTHOR]

Mid-infrared intersubband transitions are investigated in nonpolar m-plane and a-plane GaN/AlGaN multi-quantum well heterostructures. Nominally identical heterostructures were grown by ammonia molecular-beam epitaxy on free-standing m-plane and a-plane GaN substrates. A total of 12 well- and barrier-doped samples with intersubband transition energies in the range of 220–320 meV (wavelength range 3.8–5.6 μm) were grown. The intersubband absorption lines of the m-plane samples were 10–40% narrower than those of the a-plane samples, and a very narrow intersubband absorption linewidth of 38 meV (full width at half maximum) at a transition energy of approximately 250 meV (5 μm wavelength) was observed in an m-plane sample. Narrower intersubband absorption linewidths of m-plane samples can be explained by more abrupt heterostructure interfaces revealed by structural characterization, which is attributed to a higher stability of the m-plane compared to the a-plane. No significant difference in the intersubband absorption linewidth was observed between the barrier- and well-doped samples. [ABSTRACT FROM AUTHOR]

Long non-coding (lnc)RNA hypoxia inducible factor 1α-antisense RNA 1 (HIF1A-AS1) not only participates in different types of malignancies, but also serves pivotal roles in thoracic aortic aneurysms, which suggests its possible involvement in intracranial aneurysms. Therefore, the present study aimed to investigate its involvement in intracranial aneurysms. Expression levels of HIF1A-AS1 and transforming growth factor (TGF)-β1 in the blood of patients with intracranial aneurysms and healthy controls were detected using reverse transcription-quantitative polymerase chain reaction. The diagnostic value of blood HIF1A-AS1 for intracranial aneurysms was analyzed using receiver operating characteristic curve analysis. A HIF1A-AS1 expression vector was constructed and transfected into human vascular smooth muscle cells (VSMCs) and the effects on cell proliferation and TGF-β1 expression were explored using the Cell Counting kit-8 assay and western blot analysis, respectively. Upregulated HIF1A-AS1 expression levels in blood were observed in patients with intracranial aneurysms when compared with controls. Notably, upregulated HIF1A-AS1 expression effectively distinguished patients with intracranial aneurysms from healthy controls. Furthermore, HIF1A-AS1 and TGF-β1 expression levels were positively correlated with intracranial aneurysms. HIF1A-AS1 overexpression also upregulated TGF-β1 expression and inhibited VSMC proliferation. Although TGF-β1 treatment had no significant effect on HIF1A-AS1 expression, TGF-β inhibitor significantly reduced the effects of HIF1A-AS1 overexpression on cell proliferation. It was therefore concluded that HIF1A-AS1 may participate in intracranial aneurysms by regulating VSMC proliferation through the upregulation of TGF-β1. [ABSTRACT FROM AUTHOR]

Abstract Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradigm is still limited when facing the need to learn new concepts efficiently from scarce data. In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning. The training procedure, imitating the concept formation course of human, learns how to distinguish samples from different classes and aggregate samples of the same kind. In order to better utilize the advantages originated from the human behavior, we propose a sequential process, during which the network should decide how to remember each sample at every step. In this sequential process, a stable and interactive memory serves as an important module. We validate our model in some typical one-shot learning tasks and also an exploratory outlier detection problem. In all the experiments, our model gets highly competitive to reach or outperform those strong baselines. [ABSTRACT FROM AUTHOR]

Abstract Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state-of-the-art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence-level attention on all sentences of an entity pair and then employs word-level attention to construct sentence representations and fine sentence-level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods. [ABSTRACT FROM AUTHOR]

Abstract Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via deep reinforcement learning. It takes the form of Memory Networks but features a special memory that stores relational tuples, mimicking the “Image Schema” in human cognitive activities. We redefine textual reasoning as a sequential decision-making process modifying or retrieving from the memory, where logic rules serve as state-transition functions. Activating logic rules for reasoning involves two problems: variable binding and relation activating , and this is a first step to solve them jointly. Our model achieves an average error rate of 0.7% on bAbI-20, a widely-used synthetic reasoning benchmark, using less than 1k training samples and no supporting facts. [ABSTRACT FROM AUTHOR]

Understanding the correlation between cognitive ability and risk preferences is crucial for promoting technological progress and innovation in China. This study aims to investigate this relationship using survey data from the China Family Panel Studies. The results reveal a significant positive correlation between cognitive ability and risk tolerance. These findings have important implications for understanding the relationship between cognitive ability and risk preferences, as well as for informing policy decisions in areas such as financial investments and entrepreneurship incentives. [ABSTRACT FROM AUTHOR]

Graphene nanoplatelets(GNP)/cement composites were prepared using three types of GNP with different structures. In order to investigate the effects of GNP and styrene-acrylate emulsion on properties of GNP/cement composites, GNP with different addition (0-2.0 wt%) and styrene-acrylate emulsion (10 wt%) were mixed into cement through the method of mechanical stirring. Electrical performance and the pressure-sensitive property of GNP/cement composites were studied. The results showed that the addition of GNP to cement would lead to a significant drop of resistivity and make composites manifest pressure sensitivity. In addition, the structure (C/O atomic ratio) of GNP greatly affected the properties of the GNP/cement composites. A distinct enhancement in pressure sensitivity was found when emulsion was added to GNP/cement composites. The gauge factor of emulsion modified GNP/cement composites reached a peak value of 7.783, which was 1 order of magnitude higher than composites without emulsion. This work offered a new opportunity to make use of traditional cement materials combining with GNP. [ABSTRACT FROM AUTHOR]

We study the problem of reconstructing a perfect matching M ∗ hidden in a randomly weighted n × n bipartite graph. The edge set includes every node pair in M ∗ and each of the n (n - 1) node pairs not in M ∗ independently with probability d/n. The weight of each edge e is independently drawn from the distribution P if e ∈ M ∗ and from Q if e ∉ M ∗ . We show that if d B (P , Q) ≤ 1 , where B (P , Q) stands for the Bhattacharyya coefficient, the reconstruction error (average fraction of misclassified edges) of the maximum likelihood estimator of M ∗ converges to 0 as n → ∞ . Conversely, if d B (P , Q) ≥ 1 + ϵ for an arbitrarily small constant ϵ > 0 , the reconstruction error for any estimator is shown to be bounded away from 0 for both the sparse (fixed d) and dense (growing d) regimes, resolving the conjecture in Moharrami et al. (Ann Appl Probab 31(6):2663–2720, 2021. https://doi.org/10.1214/20-AAP1660) and Semerjian et al. (Phys Rev E 102:022304, 2020. https://doi.org/10.1103/PhysRevE.102.022304). Furthermore, in the special case of complete exponentially weighted graph with d = n , P = exp (λ) , and Q = exp (1 / n) , for which the sharp threshold simplifies to λ = 4 , we prove that when λ = 4 - ϵ , the optimal reconstruction error is exp - Θ (1 / ϵ) , confirming the conjectured infinite-order phase transition in Semerjian et al. (2020). [ABSTRACT FROM AUTHOR]

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC 2 ), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K -means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets. [ABSTRACT FROM AUTHOR]

Gait recognition is a challenging problem in computer vision, especially when multi-persons walk together, called as multi-gait recognition. Multi-gait recognition includes two aspects: participant segmentation and participant recognition. In this paper, we propose to segment each participant by hypergraph partition and recognize each participant by multi-linear canonical correlation analysis algorithm (UMCCA). Firstly, raw pixel areas are obtained by grid, and each pixel area is taken as a hypergraph vertex. Then HOG-based detection and tracking technology is used to calculate the weight of each hyperedge. After segmentation, UMCCA is used to extract gait features. Finally, identity of multi-gait is recognized. The experimental results demonstrate that our proposed method achieves good performance on multi-gait dataset. [ABSTRACT FROM AUTHOR]

A bacterial strain capable of degrading di(2-ethylhexyl) phthalate (DEHP) from the artificially contaminated water (with over 90% removal within 5 days of incubation) was isolated from the activated sludge obtained from a regional wastewater treatment plant in Macau Special Administrative Region, China. The isolate was identified as Acinetobacter sp. SN13 by the 16S rRNA gene sequence analysis. Two major experimental parameters, temperature (25, 30, 35 °C) and pH (3–9), were further optimized to enhance the biodegradation efficiency for DEHP. The optimal temperature was 30 °C while there were no significant differences for the pH range tested, 6–9 (p = 0.87). The growth kinetics of this indigenous isolate on DEHP followed the inhibition model, with the maximum degradation rate, the half saturation constant, and the inhibition constant of 124.8 mg l −1 day −1 , 272.3 mg l −1 , and 720.5 mg l −1 , respectively. The inhibition model for the specific growth rate was also simulated using Matlab software, and the respective maximum specific growth rate, half saturation constant, and inhibition constant were 0.1192 day −1 , 137.6 mg l −1 , and 850.3 mg l −1 . The highest degradation rate was achieved when the initial concentration of DEHP was 400 mg l −1 . Ferric ion (Fe 3+ at 100–1000 μg l −1 ) showed the stimulatory effect on the DEHP biodegradation, while Mn 2+ stimulated the biodegradation at lower concentrations (100 μg l −1 ) but inhibited at higher concentrations (500–1000 μg l −1 ). The respective DEHP degradation pathway for the isolate is also proposed by the identification of the following intermediates: mono-(2-ethylhexyl) phthalate (MEHP), phthalic acid (PA), protocatechuate, β-carboxy- cis , cis -muconic acid, and 3-katoadipate. [ABSTRACT FROM AUTHOR]

A novel multifunctional structural supercapacitor based on graphene and geopolymer infused with 2 M KOH electrolyte was fabricated. Metakaolin-based geopolymers, which were first applied as structural separators in this paper, were prepared with metakaolin and different moduli of alkaline activator solution. The widespread pores in geopolymer matrix provide enough channels for ion storage and motion. The effects of alkaline activator solution modulus and curing age on the electrochemical properties were analyzed. The results revealed the ideal capacitive behavior of structural supercapacitor. The samples with modulus of 2.0 exhibited the highest specific capacitance of 36.5 F g −1 at curing age of 28 days. A possible mechanism was proposed to explain the factors that influence the specific capacitance of geopolymer-based structural supercapacitor. The result of multifuncitonality analysis showed that samples with modulus of 1.6 exhibited the best level of multifunctionality, with compressive strength of 33.85 MPa and specific capacitance of 33.4 F g −1 at curing age of 28 days. It achieved a balance between mechanical property and electrochemical performance. [ABSTRACT FROM AUTHOR]

Relation extraction is to identify the relationship of two given entities in the text. It is an important step in the task of knowledge extraction. Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence. Sentences with the same relationship always share the similar expressions. Besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a neural network based framework for relation classification. It can simultaneously learn the relation pattern’s information and the semantic properties of given entities. In this framework, we explore two specific models: the CNN-based model and LSTM-based model. We conduct experiments on two public datasets: the SemEval-2010 Task8 dataset and the ACE05 dataset. The proposed method achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can represent the semantic relationship of the given entities effectively. [ABSTRACT FROM AUTHOR]

A rotating suspension cartridge reactor immobilized with white-rot fungus Phanerochaete chrysosporium was employed to treat synthetic wastewater containing 1000 μg l −1 of the recalcitrant pharmaceutically active compound carbamazepine. The reactor was continuously operated for 160 days under non-sterile conditions. After one month of fungal adaptation, the removal efficiency for carbamazepine exceeded 90% through such strategies as the immobilization of P. chrysosporium on the polyurethane foam cubes (1.0 × 1.0 × 1.0 cm 3 ), the pattern of liquid/air flow inside the cartridge created through the application of intermittent operational mode, and the gradual cut of external carbon source loading in the influent. Both nutrients and oxygen were effectively transferred to fungi, which contributed to the excellent bioreactor treatment performance for the long-term continuous operation. The bacterial contamination occurring in the bioreactor was effectively suppressed, providing a feasible alternative to treat recalcitrant compounds under non-sterile conditions. [ABSTRACT FROM AUTHOR]

Gait is an important biometric which can operate from a distance without subject cooperation. However, it is easily affected by changes in covariate conditions (carrying, clothing, view angle, walking speed, random noise etc.). It is hard for training set to cover all conditions. Bipartite ranking model has achieved success in gait recognition without assumption of subject cooperation. We propose a multi-view hypergraph learning re-ranking (MHLRR) method by integrating multi-view hypergraph learning (MHL) with hypergraph-based re-ranking framework. Sparse coding re-ranking (SCRR) and MHLRR are integrated under the graph-based framework to get a model. We define it as the sparse coding multi-view hypergraph learning re-ranking (SCMHLRR) method, which makes our approach achieve higher recognition accuracy under a genuine uncooperative setting. Extensive experiments demonstrate that our approach drastically outperforms existing ranking based methods, achieving good increase in recognition rate under the most difficult uncooperative settings. [ABSTRACT FROM AUTHOR]

A countercurrent seepage bioreactor immobilized with Phanerochaete chrysosporium was continuously operated under non-sterile conditions to treat a synthetic wastewater spiked with naproxen and carbamazepine (1000 μg/L each) for 165 days. There were no serious bacterial contaminations occurred during the operational period. Naproxen was always removed to the undetectable level regardless of the experimental conditions, while the average removal efficiency for carbamazepine, a well-known recalcitrant pharmaceutically active compound, reached around 80%. The excellent removal performance was mainly attributed to the application of countercurrent seepage mode and the cardhouse fabric of the carriers, which provided the high efficiency in the transfer of oxygen and nutrients inside the bioreactor. From the fungal immobilization combined with the temperature adjustment, the fungal activity including the enzyme production was protected as well as the bacterial contamination inside the reactor was suppressed effectively. [ABSTRACT FROM AUTHOR]

Scaffold, as an essential element of tissue engineering, should provide proper chemical and structural cues to direct tissue regeneration. In this study, aligned and random polycaprolactone (PCL)/gelatin fibrous scaffolds with different mass ratio were electrospun. Chemical, structural, and mechanical properties of PCL/gelatin fibrous scaffolds were characterized by FTIR and tensile measurements. The average diameters of different groups were between 334.96 ± 41.43 nm and 363.78 ± 50.49 nm. Blending PCL with gelatin increased the mechanical properties of the scaffolds. The cell culture results demonstrated that the mass ratio of PCL and gelatin showed no obvious effects on cell behavior, whereas the cell growth behavior was affected by the fibers orientation. Higher elongation ratio, enhanced cell proliferation and elevated alkaline phosphatase activity were observed for cells cultured on aligned fibers. The findings in our research provide insightful information for the design and fabrication of scaffolds for bone tissue engineering. [ABSTRACT FROM AUTHOR]

To mechanically manipulate of electromagnetic induced transparency (EIT) in terahertz asymmetric parallel-plate waveguide cavities, the influence of waveguide spacing on the transmission response has been studied experimentally. After setting the appropriate shifting length between two cavities, we found with mechanically increasing the waveguide spacing, the symmetric resonance shows degeneracy when its wavelength is smaller than the waveguide spacing. An on-to-off modulation for symmetric resonance appeared in EIT can be observed with destructive interference broken. This control mechanism of EIT will open a door to design the tunable EIT devices. [ABSTRACT FROM AUTHOR]

Terahertz (THz) filters based on extraordinary optical transmission from periodical hole array structures fabricated on aluminum slab have been experimentally investigated by using THz time-domain spectroscopy. The incident THz pulses with frequency from 0.1 to 2.7 THz could be partly filtered, and the central peak was at ∼0.26. The high frequency signal could be observed to decrease, especially for the frequency above ∼1 THz. Moreover, the transmission peak from small-size sample with less hole arrays shifts to high frequency at ∼0.53 THz due to both the effects of boundary condition and insufficient periodical extension. Furthermore, finite element method with surface plasmon polariton theory is employed to analyze this extraordinary optical transmission and filter phenomena. [ABSTRACT FROM AUTHOR]

Recently, our proposed speaker extraction model, WASE (learning When to Attend for Speaker Extraction) yielded superior performance over the prior state-of-the-art methods by explicitly modeling onset clue and regarding it as important guidance in speaker extraction tasks. However, it still remains challenging when it comes to the deployments on the resource-constrained devices, where the model must be tiny and fast to perform inference with minimal budget in CPU and memory while keeping the speaker extraction performance. In this work, we utilize model compression techniques to alleviate the problem and propose a lightweight speaker extraction model, TinyWASE, which aims to run on resource-constrained devices. Specifically, we mainly investigate the grouping effects of quantization-aware training and knowledge distillation techniques in the speaker extraction task and propose Distillation-aware Quantization. Experiments on WSJ0-2mix dataset show that our proposed model can achieve comparable performance as the full-precision model while reducing the model size using ultra-low bits (e.g. 3 bits), obtaining 8.97x compression ratio and 2.15 MB model size. We further show that TinyWASE can combine with other model compression techniques, such as parameter sharing, to achieve compression ratio as high as 23.81 with limited performance degradation. Our code is available at https://github.com/aispeech-lab/TinyWASE. [ABSTRACT FROM AUTHOR]

Eggshell color plays important biological roles and attracts the attention of both egg retailers and researchers. However, whether non-coding RNAs are involved in pigment deposition among different eggshell colors remains unknown. In this study, RNA sequencing was used to analyse the uterine gland transcriptome (CircRNA and miRNA) of Changshun chicken blue-shell hens producing four different eggshell color eggs including dark blue PK(DB) and light blue (LB), dark brown and greenish (between blue and pink, DP) and pink (p). We found that miR-192-x, targeting SLC16a7, was expressed in DB, DP, and LB groups compared with the PK group, which indicates that miR-192-x may play a role in the blue eggshell color. KEGG and GO analyses showed that the "metabolic pathways" with targeted genes such BLVRA and HMOX1 were detected in dark and light blue color eggshell chickens, which confirms the different ratios of biliverdin and HO-1 involved in the deposition of blue color. As annotated by connectivity analysis, RASGRF1 and RASGRF2, belonging to the RASGRF family, are involved in the Ras signaling pathway, which plays an important role in cell growth, differentiation, metastasis and apoptosis. Our findings enrich the database of circRNA, miRNAs and genes for chicken uterine tissue, which will be useful in accelerating molecular selection for blue eggshell color layers. [ABSTRACT FROM AUTHOR]

Neural tube defects (NTDs) are the most serious and common birth defects in the clinical setting. The Notch signaling pathway has been implicated in different processes of the embryonic neural stem cells (NSCs) during neural tube development. The aim of the present study was to investigate the expression pattern and function of Notch1 (N1) in all-trans retinoic acid (atRA)-induced NTDs and NSC differentiation. A mouse model of brain abnormality was established by administering 28 mg/kg atRA, and then brain development was examined using hematoxylin and eosin (H&E) staining. The N1 expression pattern was detected in the brain of mice embryos via immunohistochemistry and western blotting. NSCs were extracted from the fetal brain of C57 BL/6 embryos at 18.5 days of pregnancy. N1, Nestin, neurofilament (NF), glial fibrillary acidic protein (GFAP) and galactocerebroside (GALC) were identified using immunohistochemistry. Moreover, N1, presenilin 1 (PS1), Nestin, NF, GFAP and GALC were detected via western blotting at different time points in the NSCs with control media or atRA media. H&E staining identified that the embryonic brain treated with atRA was more developed compared with the control group. N1 was downregulated in the embryonic mouse brain between days 11 and 17 in the atRA-treated group compared with the untreated group. The distribution of N1, Nestin, NF, GFAP and GALC was positively detected using immunofluorescence staining. Western blotting results demonstrated that there were significantly, synchronous decreased expression levels of N1 and PS1, but increased expression levels of NF, GFAP and GALC in NSCs treated with atRA compared with those observed in the controls (P<0.05). The results suggested that the N1 signaling pathway inhibited brain development and NSC differentiation. Collectively, it was found that atRA promoted mouse embryo brain development and the differentiation of NSCs by inhibiting the N1 pathway. [ABSTRACT FROM AUTHOR]

• Uncertainties in canopy conductance are affected by vapor pressure deficit mostly. • The maximum stomatal conductance (gsm) shows variation with the leaf age. • gsm shows distinctive trends among the C3 crop/grass, C4 crop and C3 tree groups. • Stomatal conductance shows different trends with radiation among plant species. • Air temperature having little influence on the canopy conductance. Canopy conductance (G c) is a crucial variable in accurately estimating latent heat flux and evapotranspiration (ET) over vegetated surfaces. G c is highly dependent on the plant species and the surrounding environment; consequently, it is difficult to accurately simulate G c using a simplified universal model. A number of empirical parameters and functional forms have been introduced to appropriately account for various plant species. This study examines the efficacy of a widely adopted G c model and Penman-Monteith (PM) approach over a selection of vegetated sites in arid and semi-arid regions of China to investigate the factors that influence the accuracy of the model. Comprehensive micrometeorological and land surface monitoring datasets from eight eddy covariance (EC) stations in the Hei and Hai river basins were used for our analysis. E T calculated from a widely used surface conductance and PM method was compared with EC measurements, and the residuals of the comparison were analyzed with factors potentially influencing the residuals using their correlations and regression coefficients to identify factors accounting for the errors caused via G c. The results showed that the vapor pressure deficit and the maximum stomatal conductance (g s m) are most significantly correlated with uncertainties in G c for all plant species. The G c error vs. vapor pressure deficit exhibited distinctive sensitivities among the C3 crop/grass, C4 crop and C3 tree groups. The maximum stomatal conductance showed variation with the leaf age, reaching the maximum level in the mid-growth stage for most types of plant species except for coniferous trees. The residuals increased with radiation levels for sites covered with trees, which appeared to be caused by the decreasing trend of the stomatal conductance vs. radiation at high radiation levels for trees. Finally, we propose potential methods of improving canopy conductance models based on our error analysis. [ABSTRACT FROM AUTHOR]

The state-of-the-art (SOTA) crack segmentation methods still face the challenges in low-contrast shallow crack recognition and multi-scene compatibility. Therefore, this paper proposes a solution based on a multi-dimensional structure information fusion-based network (MDSIFNet). This network consists of two branches, one for extracting two-dimensional (2D) spatial feature information and another for acquiring three-dimensional (3D) geometric one. In the former, the designed 2D curve structure constraint module based on prior knowledge combined with a pretrained 2D feature extraction module can reduce learning samples and realize crack segmentation in unseen scenes. In the latter, a one-hot transformation and a 3D geometric structure information extraction module are designed to make the network pay attention to the geometric features of shallow cracks and improve their segmentation accuracy. Finally, a fusion module couples the multi-dimensional structure feature information of these two branches and outputs pixel-wise segmentation results that can be used for crack measurement and quantitative analysis. The experimental results show that the designed model network MDSIFNet performs better than the SOTA methods in terms of performance and visualized results. • A pixel-wise crack segmentation network model for the safety assessment of concrete structures is proposed. • 2D curve structure constraint and 3D geometric structure information extraction modules are designed. • 2D curve structure information can improve the multi-scene compatibility of the network model. • 3D geometric structure features of small receptive fields are beneficial for low-contrast shallow crack recognition. • Experimental results of the network model on three different datasets are analyzed. [ABSTRACT FROM AUTHOR]

Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost. [ABSTRACT FROM AUTHOR]

Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden 2k-nearest neighbor (NN) graph in an n-vertex complete graph, whose edge weights are independent and distributed according to Pn for edges in the hidden 2k-NN graph and Qn otherwise. The special case of Bernoulli distributions corresponds to a variant of the Watts-Strogatz small-world graph. We focus on two types of asymptotic recovery guarantees as n → ∞ : (1) exact recovery: all edges are classified correctly with probability tending to one; (2) almost exact recovery: the expected number of misclassified edges is o(nk). We show that the maximum likelihood estimator achieves (1) exact recovery for 2 ≤ k ≤ no(1) if lim inf 2αn/log n > 1; (2) almost exact recovery for 1 ≤ k ≤ o (log n/log log n) if lim inf kD(Pn||Qn)/log n > 1, where αn ≜ −2 log ∫ √d Pn d Qn is the Rényi divergence of order 1/2 and D(Pn||Qn) is the Kullback-Leibler divergence. Under mild distributional assumptions, these conditions are shown to be information-theoretically necessary for any algorithm to succeed. A key challenge in the analysis is the enumeration of 2k-NN graphs that differ from the hidden one by a given number of edges. We also analyze several computationally efficient algorithms and provide sufficient conditions under which they achieve exact/almost exact recovery. In particular, we develop a polynomial-time algorithm that attains the threshold for exact recovery under the small-world model. [ABSTRACT FROM AUTHOR]

Holographic beam modulation is widely applied in optical tweezers, hard-brittle material marking, high-density storage, etc. To generate high-fidelity arbitrary 3-dimensional (3D) parallel multifoci inside the material, the spherical aberration compensated automatic differentiation (SACAD) algorithm is presented. All polarization components are included and the spherical aberration compensation is embedded in the physical model. The technique of automatic differentiation is used in the error backpropagation procedure, ensuring efficient parallel computing of pixel-by-pixel gradients. In several simulation tests, the root mean square errors of the generated 3D multifoci distributions are all less than 0.01 and the diffraction efficiencies are all beyond 90%, outperforming the results of the established algorithms. In the experiments, we have verified the advantage of SACAD algorithm in complicated 3D internal marking with spherical aberration compensation. Since the SACAD algorithm can achieve high fidelity and efficiency phase retrieval with a straightforward procedure, it has the potential to become a well-received solution for internal parallel beam modulation. • Comprehensive algorithm for beam modulation with built-in aberration compensation. • Surpassing the GSW algorithm in diffraction efficiency and fidelity. • Compatible with high efficiency parallel computing using automatic differentiation. • High fidelity and low interlayer crosstalk in internal laser direct writing. [ABSTRACT FROM AUTHOR]

We report the design and operation of a nonlinear intersubband polaritonic metasurface for mid-infrared second harmonic generation. The metasurface is made of plasmonic nanoresonators filled with a multiple-quantum-well semiconductor heterostructure. Unlike the previously reported nonlinear intersubband polaritonic metasurfaces that employ full-metal backplanes below the etched metal-semiconductor nanoresonators, the metasurface reported here employs an incomplete backplane that is complementary to the pattern of the top metallization of the etched semiconductor heterostructure nanoresonators. The new approach produces high-electric-field localization and enhancement in the nanoresonators, while requiring simplified fabrication and allowing the metasurface to operate in both transmission and reflection regimes. [ABSTRACT FROM AUTHOR]

Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results. [ABSTRACT FROM AUTHOR]

We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs. Extending the exact recovery guarantees established in a companion paper for Gaussian weights, in this work, we prove the universality of these guarantees for a general correlated Wigner model. In particular, for two Erdős-Rényi graphs with edge correlation coefficient 1 - σ 2 and average degree at least polylog (n) , we show that GRAMPA exactly recovers the latent vertex correspondence with high probability when σ ≲ 1 / polylog (n) . Moreover, we establish a similar guarantee for a variant of GRAMPA, corresponding to a tighter quadratic programming relaxation of the quadratic assignment problem. Our analysis exploits a resolvent representation of the GRAMPA similarity matrix and local laws for the resolvents of sparse Wigner matrices. [ABSTRACT FROM AUTHOR]

Graph matching aims at finding the vertex correspondence between two unlabeled graphs that maximizes the total edge weight correlation. This amounts to solving a computationally intractable quadratic assignment problem. In this paper, we propose a new spectral method, graph matching by pairwise eigen-alignments (GRAMPA). Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure. The similarity matrix can also be interpreted as the solution to a regularized quadratic programming relaxation of the quadratic assignment problem. For the Gaussian Wigner model in which two complete graphs on n vertices have Gaussian edge weights with correlation coefficient 1 - σ 2 , we show that GRAMPA exactly recovers the correct vertex correspondence with high probability when σ = O (1 log n) . This matches the state of the art of polynomial-time algorithms and significantly improves over existing spectral methods which require σ to be polynomially small in n. The superiority of GRAMPA is also demonstrated on a variety of synthetic and real datasets, in terms of both statistical accuracy and computational efficiency. Universality results, including similar guarantees for dense and sparse Erdős–Rényi graphs, are deferred to a companion paper. [ABSTRACT FROM AUTHOR]

[Display omitted] • A Si-based microchannel heatsink was used to improve the transient response characteristics of IR emitter. • A 3-D FEM was used to analyze the electrical-thermal and heat transfer behaviors of devices. • The devices performance can be improved when the flow velocity of coolant is larger than 0.5 m/s. Structure design and thermal simulation of MEMS-based thermal infrared (TIR) emitter integrated with Si-based microchannel heatsink (MCHS) were presented for gas sensing application. The MCHS attached on the bottom of TIR emitter was used to transfer heat to coolant for improving the transient response characteristics of TIR emitters. A three-dimensional finite element method was used to analyze the electrical heating and heat transfer behaviors of the TIR emitter with MCHS through COMSOL Multiphysics® platform. The surface temperature distribution and transient response were simulated when different flow velocities of coolant passed through the MCHS, and the influence of the flow velocity on the response characteristics was analyzed. The result shows that the temperature in the Si frame of TIR emitters will be significant reduced when the flow velocity of coolant is larger than 0.5 m/s, which will enable an effective improvement in the response characteristics of MEMS TIR emitters. [ABSTRACT FROM AUTHOR]

We study the problem of recovering a hidden community of cardinality K from an n \times n symmetric data matrix A , where for distinct indices i,j if i, j both belong to the community and Aij \sim Q otherwise, for two known probability distributions P and Q depending on n . If P=\mathrm Bern(p) and Q=\mathrm Bern(q) with p>q , it reduces to the problem of finding a densely connected K -subgraph planted in a large Erdös–Rényi graph; if P=\mathcal N(\mu ,1) and Q=\mathcal N(0,1) with $\mu >0$ , it corresponds to the problem of locating a K \times K$ principal submatrix of elevated means in a large Gaussian random matrix. We focus on two types of asymptotic recovery guarantees as n \to \infty $ : 1) weak recovery: expected number of classification errors is o(K)$ and 2) exact recovery: probability of classifying all indices correctly converges to one. Under mild assumptions on P$ and Q and ({1-p})/({1-q}) are bounded away from zero and infinity. Previous work has shown that if weak recovery is achievable; then, exact recovery is achievable in linear additional time by a simple voting procedure. We provide a converse, showing the condition for the voting procedure to succeed is almost necessary for exact recovery. [ABSTRACT FROM AUTHOR]

A high-quality triple-channel terahertz filter based on silicon multi-cavity resonant system has been experimentally investigated by using backward wave oscillator. Theoretical methods have been employed to analyze physical origin of the resonances and the transmission properties. We find that the lower transmission peak comes from the Fabry-Perot resonance in the side two thick cavities and the two higher peaks are caused by the symmetrically and asymmetrically coupling of the two thin cavities in the middle. The resonant peaks show blue shift by increasing the incident angle, which are attributed to the change of effective refractive indices. [ABSTRACT FROM AUTHOR]

The Bi 2 O 3 -CaO-Nb 2 O 5 (BCN) materials were prepared by solid-state reaction. The BCN pyrochlores exhibited a layered structure with general formula of CaBi 2 Nb 2 O 9 when the sintering temperature below 1100 °C, and the sample in a nearly pure phase at 1100 °C. The structure of BCN ceramics was gradually transformed into Ca 2 Nb 2 O 7 pyrochlore structure with further increasing the sintering temperature. The dielectric properties varied with structures. The BCN ceramic sintered at 1100 °C exhibited excellent dielectric properties with a dielectric constant of ~ 110 and a dielectric loss of ~ 0.001. The dielectric constants of all the ceramics were independent with the measurement frequency (100 Hz–1 MHz) at room temperature. However, the dielectric loss decreased dramatically at low frequency and then increased slightly with increasing frequency. The temperature dependence of dielectric properties of BCN ceramics was also investigated. The BCN materials with a Ca 2 Nb 2 O 7 pyrochlore structure were more sensitive to the temperature than that with a CaBi 2 Nb 2 O 9 structure. [ABSTRACT FROM AUTHOR]

Resolving a conjecture of Abbe, Bandeira, and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model (SBM) of two equal-sized clusters. The same was shown for the case of a single cluster and outliers. Extending the proof techniques, in this paper, it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: 1) binary SBM with two clusters of sizes proportional to network size but not necessarily equal; 2) SBM with a fixed number of equal-sized clusters; and 3) binary censored block model with the background graph being Erdős–Rényi. Furthermore, a sufficient condition is given for an SDP procedure to achieve exact recovery for the general case of a fixed number of clusters plus outliers. These results demonstrate the versatility of SDP relaxation as a simple, general purpose, computationally feasible methodology for community detection. [ABSTRACT FROM AUTHOR]