200 research outputs found
Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation
The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method
Gender Inequalities: Women Researchers Require More Knowledge in Specific and Experimental Topics
Gender inequalities in science have long been observed globally. Studies have
demonstrated it through survey data or published literature, focusing on the
interests of subjects or authors; few, however, examined the manifestation of
gender inequalities on researchers' knowledge status. This study analyzes the
relationship between regional and gender identities, topics, and knowledge
status while revealing the female labor division in science and scientific
research using online Q&A from researchers. We find that gender inequalities
are merged with both regional-specific characteristics and global common
patterns. Women's field and topic distribution within fields are influenced by
regions, yet the prevalent topics are consistent in all regions. Women are more
involved in specific topics, particularly topics about experiments with weaker
levels of knowledge and they are of less assistance. To promote inequality in
science, the scientific community should pay more attention to reducing the
knowledge gap and encourage women to work on unexplored topics and areas
Efficient Approximation Algorithms for Spanning Centrality
Given a graph , the spanning centrality (SC) of an edge
measures the importance of for to be connected. In practice,
SC has seen extensive applications in computational biology, electrical
networks, and combinatorial optimization. However, it is highly challenging to
compute the SC of all edges (AESC) on large graphs. Existing techniques fail to
deal with such graphs, as they either suffer from expensive matrix operations
or require sampling numerous long random walks. To circumvent these issues,
this paper proposes TGT and its enhanced version TGT+, two algorithms for AESC
computation that offers rigorous theoretical approximation guarantees. In
particular, TGT remedies the deficiencies of previous solutions by conducting
deterministic graph traversals with carefully-crafted truncated lengths. TGT+
further advances TGT in terms of both empirical efficiency and asymptotic
performance while retaining result quality, based on the combination of TGT
with random walks and several additional heuristic optimizations. We
experimentally evaluate TGT+ against recent competitors for AESC using a
variety of real datasets. The experimental outcomes authenticate that TGT+
outperforms the state of the arts often by over one order of magnitude speedup
without degrading the accuracy.Comment: The technical report of the paper entitled 'Efficient Approximation
Algorithms for Spanning Centrality' in SIGKDD'2
Photonic realization of a generic type of graphene edge states exhibiting topological flat band
Cutting a honeycomb lattice (HCL) can end up with three types of edges
(zigzag, bearded and armchair), as is well known in the study of graphene edge
states. Here we theoretically investigate and experimentally demonstrate a
class of graphene edges, namely, the twig-shaped edges, using a photonic
platform, thereby observing edge states distinctive from those observed before.
Our main findings are: (i) the twig edge is a generic type of HCL edges
complementary to the armchair edge, formed by choosing the right primitive cell
rather than simple lattice cutting or Klein edge modification; (ii) the twig
edge states form a complete flat band across the Brillouin zone with
zero-energy degeneracy, characterized by nontrivial topological winding of the
lattice Hamiltonian; (iii) the twig edge states can be elongated or compactly
localized along the boundary, manifesting both flat band and topological
features. Such new edge states are realized in a laser-written photonic
graphene and well corroborated by numerical simulations. Our results may
broaden the understanding of graphene edge states, bringing about new
possibilities for wave localization in artificial Dirac-like materials.Comment: 13 pages, 4 figure
Capacity Constrained Influence Maximization in Social Networks
Influence maximization (IM) aims to identify a small number of influential
individuals to maximize the information spread and finds applications in
various fields. It was first introduced in the context of viral marketing,
where a company pays a few influencers to promote the product. However, apart
from the cost factor, the capacity of individuals to consume content poses
challenges for implementing IM in real-world scenarios. For example, players on
online gaming platforms can only interact with a limited number of friends. In
addition, we observe that in these scenarios, (i) the initial adopters of
promotion are likely to be the friends of influencers rather than the
influencers themselves, and (ii) existing IM solutions produce sub-par results
with high computational demands. Motivated by these observations, we propose a
new IM variant called capacity constrained influence maximization (CIM), which
aims to select a limited number of influential friends for each initial adopter
such that the promotion can reach more users. To solve CIM effectively, we
design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the
-approximation ratio. To improve the efficiency, we devise the scalable
implementation named RR-OPIM+ with -approximation and
near-linear running time. We extensively evaluate the performance of 9
approaches on 6 real-world networks, and our solutions outperform all
competitors in terms of result quality and running time. Additionally, we
deploy RR-OPIM+ to online game scenarios, which improves the baseline
considerably.Comment: The technical report of the paper entitled 'Capacity Constrained
Influence Maximization in Social Networks' in SIGKDD'2
Unconventional Flatband Line States in Photonic Lieb Lattices
Flatband systems typically host "compact localized states"(CLS) due to
destructive interference and macroscopic degeneracy of Bloch wave functions
associated with a dispersionless energy band. Using a photonic Lieb
lattice(LL), we show that conventional localized flatband states are inherently
incomplete, with the missing modes manifested as extended line states which
form non-contractible loops winding around the entire lattice. Experimentally,
we develop a continuous-wave laser writing technique to establish a
finite-sized photonic LL with specially-tailored boundaries, thereby directly
observe the unusually extended flatband line states.Such unconventional line
states cannot be expressed as a linear combination of the previously observed
CLS but rather arise from the nontrivial real-space topology.The robustness of
the line states to imperfect excitation conditions is discussed, and their
potential applications are illustrated
Flatband Line States in Photonic Super-Honeycomb Lattices
We establish experimentally a photonic super-honeycomb lattice (sHCL) by use
of a cw-laser writing technique, and thereby demonstrate two distinct flatband
line states that manifest as noncontractible-loop-states in an infinite
flatband lattice. These localized states (straight and zigzag lines) observed
in the sHCL with tailored boundaries cannot be obtained by superposition of
conventional compact localized states because they represent a new topological
entity in flatband systems. In fact, the zigzag-line states, unique to the
sHCL, are in contradistinction with those previously observed in the Kagome and
Lieb lattices. Their momentum-space spectrum emerges in the high-order
Brillouin zone where the flat band touches the dispersive bands, revealing the
characteristic of topologically protected bandcrossing. Our experimental
results are corroborated by numerical simulations based on the coupled mode
theory. This work may provide insight to Dirac like 2D materials beyond
graphene
A benchmark and an algorithm for detecting germline transposon insertions and measuring de novo transposon insertion frequencies
Transposons are genomic parasites, and their new insertions can cause instability and spur the evolution of their host genomes. Rapid accumulation of short-read whole-genome sequencing data provides a great opportunity for studying new transposon insertions and their impacts on the host genome. Although many algorithms are available for detecting transposon insertions, the task remains challenging and existing tools are not designed for identifying de novo insertions. Here, we present a new benchmark fly dataset based on PacBio long-read sequencing and a new method TEMP2 for detecting germline insertions and measuring de novo \u27singleton\u27 insertion frequencies in eukaryotic genomes. TEMP2 achieves high sensitivity and precision for detecting germline insertions when compared with existing tools using both simulated data in fly and experimental data in fly and human. Furthermore, TEMP2 can accurately assess the frequencies of de novo transposon insertions even with high levels of chimeric reads in simulated datasets; such chimeric reads often occur during the construction of short-read sequencing libraries. By applying TEMP2 to published data on hybrid dysgenic flies inflicted by de-repressed P-elements, we confirmed the continuous new insertions of P-elements in dysgenic offspring before they regain piRNAs for P-element repression. TEMP2 is freely available at Github: https://github.com/weng-lab/TEMP2
Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning
BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition.
METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes.
RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay.
CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)
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