260 research outputs found
Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084
Measurements of the properties of Lambda_c(2595), Lambda_c(2625), Sigma_c(2455), and Sigma_c(2520) baryons
We report measurements of the resonance properties of Lambda_c(2595)+ and
Lambda_c(2625)+ baryons in their decays to Lambda_c+ pi+ pi- as well as
Sigma_c(2455)++,0 and Sigma_c(2520)++,0 baryons in their decays to Lambda_c+
pi+/- final states. These measurements are performed using data corresponding
to 5.2/fb of integrated luminosity from ppbar collisions at sqrt(s) = 1.96 TeV,
collected with the CDF II detector at the Fermilab Tevatron. Exploiting the
largest available charmed baryon sample, we measure masses and decay widths
with uncertainties comparable to the world averages for Sigma_c states, and
significantly smaller uncertainties than the world averages for excited
Lambda_c+ states.Comment: added one reference and one table, changed order of figures, 17
pages, 15 figure
Measurement of the Forward-Backward Asymmetry in the B -> K(*) mu+ mu- Decay and First Observation of the Bs -> phi mu+ mu- Decay
We reconstruct the rare decays , , and in a data sample
corresponding to collected in collisions at
by the CDF II detector at the Fermilab Tevatron
Collider. Using and decays we report the branching ratios. In addition, we report
the measurement of the differential branching ratio and the muon
forward-backward asymmetry in the and decay modes, and the
longitudinal polarization in the decay mode with respect to the squared
dimuon mass. These are consistent with the theoretical prediction from the
standard model, and most recent determinations from other experiments and of
comparable accuracy. We also report the first observation of the {\mathcal{B}}(B^0_s \to
\phi\mu^+\mu^-) = [1.44 \pm 0.33 \pm 0.46] \times 10^{-6}27 \pm 6B^0_s$ decay observed.Comment: 7 pages, 2 figures, 3 tables. Submitted to Phys. Rev. Let
Search for a New Heavy Gauge Boson Wprime with Electron + missing ET Event Signature in ppbar collisions at sqrt(s)=1.96 TeV
We present a search for a new heavy charged vector boson decaying
to an electron-neutrino pair in collisions at a center-of-mass
energy of 1.96\unit{TeV}. The data were collected with the CDF II detector
and correspond to an integrated luminosity of 5.3\unit{fb}^{-1}. No
significant excess above the standard model expectation is observed and we set
upper limits on . Assuming standard
model couplings to fermions and the neutrino from the boson decay to
be light, we exclude a boson with mass less than
1.12\unit{TeV/}c^2 at the 95\unit{%} confidence level.Comment: 7 pages, 2 figures Submitted to PR
Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications
Deactylase inhibition in myeloproliferative neoplasms
Myeloproliferative neoplasms (MPN) are clonal haemopoietic progenitor cell disorders characterized by the proliferation of one or more of the haemopoietic lineages (myeloid, erythroid and/or megakaryocytic). The MPNs include eight haematological disorders: chronic myelogenous leukemia (CML), polycythemia vera (PV), essential thrombocythemia (ET), primary myelofibrosis (PMF), systemic mastocytosis (SM), chronic eosinophilic leukemia, not otherwise specified (CEL, NOS), chronic neutrophilic leukemia (CNL), and unclassifiable MPN (MPN, U). Therapeutic interventions for MPNs include the use of tyrosine kinase inhibitors (TKIs) for BCR-ABL1+ CML and JAK2 inhibitors for PV, ET and PMF. Histone deacetylase inhibitors (HDACi) are a novel class of drugs capable of altering the acetylation status of both histone and non-histone proteins, thereby affecting a repertoire of cellular functions in neoplastic cells including proliferation, differentiation, immune responses, angiogenesis and survival. Preliminary studies indicate that HDACi when used in combination with tyrosine kinase or JAK2 inhibitors may overcome resistance to the latter agents and enhance the pro-apoptotic effects on MPN cells. This review provides a review of pre-clinical and clinical studies that have explored the use of HDACi as potential therapeutics for MPNs
Epigenetic abnormalities in myeloproliferative neoplasms: a target for novel therapeutic strategies
The myeloproliferative neoplasms (MPNs) are a group of clonal hematological malignancies characterized by a hypercellular bone marrow and a tendency to develop thrombotic complications and to evolve to myelofibrosis and acute leukemia. Unlike chronic myelogenous leukemia, where a single disease-initiating genetic event has been identified, a more complicated series of genetic mutations appear to be responsible for the BCR-ABL1-negative MPNs which include polycythemia vera, essential thrombocythemia, and primary myelofibrosis. Recent studies have revealed a number of epigenetic alterations that also likely contribute to disease pathogenesis and determine clinical outcome. Increasing evidence indicates that alterations in DNA methylation, histone modification, and microRNA expression patterns can collectively influence gene expression and potentially contribute to MPN pathogenesis. Examples include mutations in genes encoding proteins that modify chromatin structure (EZH2, ASXL1, IDH1/2, JAK2V617F, and IKZF1) as well as epigenetic modification of genes critical for cell proliferation and survival (suppressors of cytokine signaling, polycythemia rubra vera-1, CXC chemokine receptor 4, and histone deacetylase (HDAC)). These epigenetic lesions serve as novel targets for experimental therapeutic interventions. Clinical trials are currently underway evaluating HDAC inhibitors and DNA methyltransferase inhibitors for the treatment of patients with MPNs
Observation of long-range, near-side angular correlations in proton-proton collisions at the LHC
This is the pre-print version of the Published Article, which can be accessed from the link below - Copyright @ 2010 Springer VerlagResults on two-particle angular correlations for charged particles emitted in proton-proton collisions at center-of-mass energies of 0.9, 2.36, and 7 TeV are presented, using data collected with the CMS detector over a broad range of pseudorapidity (eta) and azimuthal angle (phi). Short-range correlations in Delta(eta), which are studied in minimum bias events, are characterized using a simple "independent cluster" parametrization in order to quantify their strength (cluster size) and their extent in eta (cluster decay width). Long-range azimuthal correlations are studied differentially as a function of charged particle multiplicity and particle transverse momentum using a 980 inverse nb data set at 7 TeV. In high multiplicity events, a pronounced structure emerges in the two-dimensional correlation function for particle pairs with intermediate transverse momentum of 1-3 GeV/c, 2.0< |Delta(eta)| <4.8 and Delta(phi) near 0. This is the first observation of such a long-range, near-side feature in two-particle correlation functions in pp or p p-bar collisions
Search for large extra dimensions in the diphoton final state at the Large Hadron Collider
This is the pre-print version of the Published Article, which can be accessed from the link below - Copyright @ 2011 Springer VerlagA search for large extra spatial dimensions via virtual-graviton exchange in the diphoton channel has been carried out with the CMS detector at the LHC. No excess of events above the standard model expectations is found using a data sample collected in proton-proton collisions at Ăs = 7s=7TeV and corresponding to an integrated luminosity of 36 pbâ 1. New lower limits on the effective Planck scale in the range of 1.6â2.3TeV at the 95% confidence level are set, providing the most restrictive bounds to date on models with more than two large extra dimensions
Effect of SGLT2 inhibitors on stroke and atrial fibrillation in diabetic kidney disease: Results from the CREDENCE trial and meta-analysis
BACKGROUND AND PURPOSE: Chronic kidney disease with reduced estimated glomerular filtration rate or elevated albuminuria increases risk for ischemic and hemorrhagic stroke. This study assessed the effects of sodium glucose cotransporter 2 inhibitors (SGLT2i) on stroke and atrial fibrillation/flutter (AF/AFL) from CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation) and a meta-Analysis of large cardiovascular outcome trials (CVOTs) of SGLT2i in type 2 diabetes mellitus. METHODS: CREDENCE randomized 4401 participants with type 2 diabetes mellitus and chronic kidney disease to canagliflozin or placebo. Post hoc, we estimated effects on fatal or nonfatal stroke, stroke subtypes, and intermediate markers of stroke risk including AF/AFL. Stroke and AF/AFL data from 3 other completed large CVOTs and CREDENCE were pooled using random-effects meta-Analysis. RESULTS: In CREDENCE, 142 participants experienced a stroke during follow-up (10.9/1000 patient-years with canagliflozin, 14.2/1000 patient-years with placebo; hazard ratio [HR], 0.77 [95% CI, 0.55-1.08]). Effects by stroke subtypes were: ischemic (HR, 0.88 [95% CI, 0.61-1.28]; n=111), hemorrhagic (HR, 0.50 [95% CI, 0.19-1.32]; n=18), and undetermined (HR, 0.54 [95% CI, 0.20-1.46]; n=17). There was no clear effect on AF/AFL (HR, 0.76 [95% CI, 0.53-1.10]; n=115). The overall effects in the 4 CVOTs combined were: Total stroke (HRpooled, 0.96 [95% CI, 0.82-1.12]), ischemic stroke (HRpooled, 1.01 [95% CI, 0.89-1.14]), hemorrhagic stroke (HRpooled, 0.50 [95% CI, 0.30-0.83]), undetermined stroke (HRpooled, 0.86 [95% CI, 0.49-1.51]), and AF/AFL (HRpooled, 0.81 [95% CI, 0.71-0.93]). There was evidence that SGLT2i effects on total stroke varied by baseline estimated glomerular filtration rate (P=0.01), with protection in the lowest estimated glomerular filtration rate (45 mL/min/1.73 m2]) subgroup (HRpooled, 0.50 [95% CI, 0.31-0.79]). CONCLUSIONS: Although we found no clear effect of SGLT2i on total stroke in CREDENCE or across trials combined, there was some evidence of benefit in preventing hemorrhagic stroke and AF/AFL, as well as total stroke for those with lowest estimated glomerular filtration rate. Future research should focus on confirming these data and exploring potential mechanisms
- âŚ