55 research outputs found
A new width parameter of graphs based on edge cuts: -edge-crossing width
We introduce graph width parameters, called -edge-crossing width and
edge-crossing width. These are defined in terms of the number of edges crossing
a bag of a tree-cut decomposition. They are motivated by edge-cut width,
recently introduced by Brand et al. (WG 2022). We show that edge-crossing width
is equivalent to the known parameter tree-partition-width. On the other hand,
-edge-crossing width is a new parameter; tree-cut width and
-edge-crossing width are incomparable, and they both lie between
tree-partition-width and edge-cut width.
We provide an algorithm that, for a given -vertex graph and integers
and , in time either outputs
a tree-cut decomposition certifying that the -edge-crossing width of
is at most or confirms that the -edge-crossing width
of is more than . As applications, for every fixed , we obtain
FPT algorithms for the List Coloring and Precoloring Extension problems
parameterized by -edge-crossing width. They were known to be W[1]-hard
parameterized by tree-partition-width, and FPT parameterized by edge-cut width,
and we close the complexity gap between these two parameters.Comment: 26 pages, 1 figure, accepted to WG202
A characterization of graphs of radius- flip-width at most
The -flip-width of a graph, for , is a
graph parameter defined in terms of a variant of the cops and robber game,
called a flipper game, and it was introduced by Toru\'{n}czyk [Flip-width: Cops
and robber on dense graphs, arXiv:2302.00352]. We prove that for every , the class of graphs of
-flip-width at most is exactly the class of (, bull, gem,
co-gem)-free graphs, which are known as totally decomposable graphs with
respect to bi-joins.Comment: 14 pages, 1 figur
EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Records
We present a new text-to-SQL dataset for electronic health records (EHRs).
The utterances were collected from 222 hospital staff, including physicians,
nurses, insurance review and health records teams, and more. To construct the
QA dataset on structured EHR data, we conducted a poll at a university hospital
and templatized the responses to create seed questions. Then, we manually
linked them to two open-source EHR databases, MIMIC-III and eICU, and included
them with various time expressions and held-out unanswerable questions in the
dataset, which were all collected from the poll. Our dataset poses a unique set
of challenges: the model needs to 1) generate SQL queries that reflect a wide
range of needs in the hospital, including simple retrieval and complex
operations such as calculating survival rate, 2) understand various time
expressions to answer time-sensitive questions in healthcare, and 3)
distinguish whether a given question is answerable or unanswerable based on the
prediction confidence. We believe our dataset, EHRSQL, could serve as a
practical benchmark to develop and assess QA models on structured EHR data and
take one step further towards bridging the gap between text-to-SQL research and
its real-life deployment in healthcare. EHRSQL is available at
https://github.com/glee4810/EHRSQL.Comment: Published as a conference paper at NeurIPS 2022 (Track on Datasets
and Benchmarks)
FactKG: Fact Verification via Reasoning on Knowledge Graphs
In real world applications, knowledge graphs (KG) are widely used in various
domains (e.g. medical applications and dialogue agents). However, for fact
verification, KGs have not been adequately utilized as a knowledge source. KGs
can be a valuable knowledge source in fact verification due to their
reliability and broad applicability. A KG consists of nodes and edges which
makes it clear how concepts are linked together, allowing machines to reason
over chains of topics. However, there are many challenges in understanding how
these machine-readable concepts map to information in text. To enable the
community to better use KGs, we introduce a new dataset, FactKG: Fact
Verification via Reasoning on Knowledge Graphs. It consists of 108k natural
language claims with five types of reasoning: One-hop, Conjunction, Existence,
Multi-hop, and Negation. Furthermore, FactKG contains various linguistic
patterns, including colloquial style claims as well as written style claims to
increase practicality. Lastly, we develop a baseline approach and analyze
FactKG over these reasoning types. We believe FactKG can advance both
reliability and practicality in KG-based fact verification.Comment: Accepted to ACL 202
Graphene Via Contact Architecture for Vertical Integration of vdW Heterostructure Devices
Two-dimensional (2D) devices and their van der Waals (vdW) heterostructures attract considerable attention owing to their potential for next-generation logic and memory applications. In addition, 2D devices are projected to have high integration capabilities, while maintaining nanoscale thickness. However, the fabrication of 2D devices and their circuits is challenging because of the high precision required to etch and pattern ultrathin 2D materials for integration. Here, the fabrication of a graphene via contact architecture to electrically connect graphene electrodes (or leads) embedded in vdW heterostructures is demonstrated. Graphene via contacts comprising of edge and fluorinated graphene (FG) electrodes are fabricated by successive fluorination and plasma etching processes. A one-step fabrication process that utilizes the graphene contacts is developed for a vertically integrated complementary inverter based on n- and p-type 2D field-effect transistors (FETs). This study provides a promising method to fabricate vertically integrated 2D devices, which are essential in 2D material-based devices and circuits.N
Evaluation of monoclonal antibodyâbased immunohistochemistry for the detection of European and North American \u3ci\u3ePorcine reproductive and respiratory syndrome virus\u3c/i\u3e and a comparison with in situ hybridization and reverse transcription polymerase chain reaction
The objective of the present study was to compare the ability of 2 monoclonal antibodies (mAbs; SDOW17 and SR30) to detect types 1 and 2 Porcine reproductive and respiratory syndrome virus (PRRSV) in formalin-fixed, paraffin-embedded (FFPE) lung tissues by immunohistochemistry (IHC) and to compare the immunohistochemical results with in situ hybridization (ISH) and reverse transcription nested polymerase chain reaction (RT-nPCR) detection techniques. Lungs from 30 experimentally infected pigs (15 pigs with each genotype of PRRSV) and 20 naturally infected pigs (10 pigs with each genotype of PRRSV) with types 1 and 2 PRRSV, respectively, were used for the IHC, ISH, and RTnPCR analyses. The SR30 mAb-based IHC detected significantly more type 1 PRRSV-positive cells in the accessory and caudal lobes from the experimentally infected pigs at 7 (P = 0.025) and 14 (P = 0.018) days postinoculation, respectively, compared to the SDOW17 mAb-based IHC. The results demonstrated that SR30 mAb-based IHC is useful for detecting both types 1 and 2 PRRSV antigen in FFPE lung tissues
Establishment of hydrochloric acid/lipopolysaccharide-induced pelvic inflammatory disease model
Search for violation in D KK decays in proton-proton collisions at = 13 TeV
International audienceA search is reported for charge-parity D KK violation in D KK decays, using data collected in proton-proton collisions at = 13 TeV recorded by the CMS experiment in 2018. The analysis uses a dedicated data set that corresponds to an integrated luminosity of 41.6 fb, which consists of about 10 billion events containing a pair of áșadrons, nearly all of which decay to charm hadrons. The flavor of the neutral D meson is determined by the pion charge in the reconstructed decays D D and D D. The D KK asymmetry in D KK is measured to be ( KK) = (6.2 3.0 0.2 0.8)%, where the three uncertainties represent the statistical uncertainty, the systematic uncertainty, and the uncertainty in the measurement of the D KK asymmetry in the D K decay. This is the first D KK asymmetry measurement by CMS in the charm sector as well as the first to utilize a fully hadronic final state
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