Showing papers by "Indian Institute of Science published in 2017"
01 Jul 2017
TL;DR: In this paper, the authors propose a switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count, and provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch.
Abstract: We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.
745 citations
TL;DR: A fully-fledged particle-flow reconstruction algorithm tuned to the CMS detector was developed and has been consistently used in physics analyses for the first time at a hadron collider as mentioned in this paper.
Abstract: The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic τ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8\TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.
719 citations
Proceedings Article•
27 Dec 2017TL;DR: This paper provides some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems, and generalizes the existing results on noise-tolerant loss functions for binary classification.
Abstract: In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and on algorithms for estimating true labels from noisy labels. An alternate approach would be to look for loss functions that are inherently noise-tolerant. For binary classification there exist theoretical results on loss functions that are robust to label noise. In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems. These results generalize the existing results on noise-tolerant loss functions for binary classification. We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we illustrate the robustness of risk minimization with such loss functions for learning neural networks.
627 citations
TL;DR: In this paper, the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise, which can be used for probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra and transiting planet parameters.
Abstract: The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this purpose, but since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small data sets. In this paper, we present a novel method for GPs modeling in one dimension where the computational requirements scale linearly with the size of the data set. We demonstrate the method by applying it to simulated and real astronomical time series data sets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically driven damped harmonic oscillators-providing a physical motivation for and interpretation of this choice-but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable GP methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.
611 citations
TL;DR: In this article, a review of the surface-bulk electronic structure of metal oxide semiconductors (TiO2, WO3 and ZnO) is presented.
586 citations
California Institute of Technology1, Tel Aviv University2, Goddard Space Flight Center3, University of Maryland, College Park4, University of Wisconsin–Milwaukee5, Stockholm University6, Princeton University7, Liverpool John Moores University8, National Central University9, Hebrew University of Jerusalem10, University of Sydney11, Australian Research Council12, Indian Institute of Astrophysics13, Indian Institute of Science14, Commonwealth Scientific and Industrial Research Organisation15, University of Colorado Boulder16, Columbia University17, University of Washington18, Indian Institute of Technology Bombay19, Inter-University Centre for Astronomy and Astrophysics20, Lawrence Berkeley National Laboratory21, University of California, Berkeley22, George Washington University23, Texas Tech University24, University College London25, University of Leicester26, Space Telescope Science Institute27, National Radio Astronomy Observatory28, University of Portsmouth29, University of Southampton30, Radboud University Nijmegen31, Tokyo Institute of Technology32, Max Planck Society33, Northwestern University34, Adler Planetarium35, University of Oxford36, Weizmann Institute of Science37, San Diego State University38, Institute for the Physics and Mathematics of the Universe39, University of California, Merced40
TL;DR: It is demonstrated that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis, which is dissimilar to classical short gamma-ray bursts with ultrarelativistic jets.
Abstract: Merging neutron stars offer an excellent laboratory for simultaneously studying strong-field gravity and matter in extreme environments. We establish the physical association of an electromagnetic counterpart (EM170817) with gravitational waves (GW170817) detected from merging neutron stars. By synthesizing a panchromatic data set, we demonstrate that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis. The weak gamma rays seen in EM170817 are dissimilar to classical short gamma-ray bursts with ultrarelativistic jets. Instead, we suggest that breakout of a wide-angle, mildly relativistic cocoon engulfing the jet explains the low-luminosity gamma rays, the high-luminosity ultraviolet-optical-infrared, and the delayed radio and x-ray emission. We posit that all neutron star mergers may lead to a wide-angle cocoon breakout, sometimes accompanied by a successful jet and sometimes by a choked jet.
579 citations
TL;DR: In this paper, an improved jet energy scale corrections, based on a data sample corresponding to an integrated luminosity of 19.7 fb^(-1) collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 8 TeV, are presented.
Abstract: Improved jet energy scale corrections, based on a data sample corresponding to an integrated luminosity of 19.7 fb^(-1) collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 8 TeV, are presented. The corrections as a function of pseudorapidity η and transverse momentum p_T are extracted from data and simulated events combining several channels and methods. They account successively for the effects of pileup, uniformity of the detector response, and residual data-simulation jet energy scale differences. Further corrections, depending on the jet flavor and distance parameter (jet size) R, are also presented. The jet energy resolution is measured in data and simulated events and is studied as a function of pileup, jet size, and jet flavor. Typical jet energy resolutions at the central rapidities are 15–20% at 30 GeV, about 10% at 100 GeV, and 5% at 1 TeV. The studies exploit events with dijet topology, as well as photon+jet, Z+jet and multijet events. Several new techniques are used to account for the various sources of jet energy scale corrections, and a full set of uncertainties, and their correlations, are provided. The final uncertainties on the jet energy scale are below 3% across the phase space considered by most analyses (p_T > 30 GeV and 0|η| 30 GeV is reached, when excluding the jet flavor uncertainties, which are provided separately for different jet flavors. A new benchmark for jet energy scale determination at hadron colliders is achieved with 0.32% uncertainty for jets with p_T of the order of 165–330 GeV, and |η| < 0.8.
505 citations
TL;DR: DeepFix as mentioned in this paper proposes a fully convolutional neural network (FCN) which models the bottom-up mechanism of visual attention via saliency prediction and predicts the saliency map in an end-to-end manner.
Abstract: Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom–up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant—this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.
443 citations
01 Oct 2017
TL;DR: In this article, an unsupervised deep learning framework for multi-exposure fusion is proposed, which uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image.
Abstract: We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.
433 citations
TL;DR: In this article, the threat posed by increasing amount of dyes on daily basis, especially on our ecosystem, has brought a serious search for more efficient low-cost adsorbents.
Abstract: The threat posed by increasing amount of dyes on daily basis, especially on our ecosystem, has brought a serious search for more efficient low-cost adsorbents. Sand is mixed with cement and water to make concrete, used in the manufacture of brick, glass and other materials, and it can also be used as a medium for the filtration of water. Sand, which is ubiquitous, has been used as an adsorbent because of its enormous availability in the local environment. This review reveals that further research must be conducted to bring to the fore the expansive laboratory, industrial and environmental usage of sand materials as dye adsorbents. Consequently, the usage of different types of sand in the field of adsorption science represents a viable and powerful tool, resulting into the superior improvement in pollution control and environmental preservation.
422 citations
TL;DR: The use of fly ash in concrete dates back to the late 20th century and its advantages and disadvantages had been widely researched as mentioned in this paper, however, the level of replacement is still limited to a maximum of 35% of cement by mass.
Proceedings Article•
12 Feb 2017TL;DR: DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements and could fix 1881 programs completely and 1338 programs partially.
Abstract: The problem of automatically fixing programming errors is a very active research topic in software engineering. This is a challenging problem as fixing even a single error may require analysis of the entire program. In practice, a number of errors arise due to programmer's inexperience with the programming language or lack of attention to detail. We call these common programming errors. These are analogous to grammatical errors in natural languages. Compilers detect such errors, but their error messages are usually inaccurate. In this work, we present an end-to-end solution, called DeepFix, that can fix multiple such errors in a program without relying on any external tool to locate or fix them. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention which is trained to predict erroneous program locations along with the required correct statements. On a set of 6971 erroneous C programs written by students for 93 programming tasks, DeepFix could fix 1881 (27%) programs completely and 1338 (19%) programs partially.
01 Jun 2017-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: In this article, the tensile, fracture, and fatigue crack growth properties of 316L stainless steel (SS) produced using the selective laser melting (SLM) technique were evaluated and compared with those of conventionally manufactured (CM) austenitic SSs.
Abstract: The tensile, fracture, and fatigue crack growth properties of 316L stainless steel (SS) produced using the selective laser melting (SLM) technique were evaluated and compared with those of conventionally manufactured (CM) austenitic SSs. For SLM, both single melt (SM) and checker board (CB) laser scanning strategies were employed, so as to examine the effect of scanning strategy on the mechanical properties. The experimental results show that the SLM alloys' yield strength is significantly higher than that of CM 316L SS, a result of the substantial refinement in the microstructure. In contrast, only a marginal improvement in the ultimate tensile strength and a marked reduction ductility, which are a result of the loss of work hardening ability, are attributed to the absence of stress induced martensitic transformation common in CM austenitic SSs. In spite of these, the fracture toughness, which ranges between 63 and 87 MPa m 0.5 , of the SLM alloys is good, which is a result of the mesostructure induced crack tortuousity. The SLM process was found to marginally reduce the threshold stress intensity factor range for fatigue crack growth initiation and enhance the Paris exponent within the steady state crack growth regime. Both tensile and toughness properties were found to be anisotropic in nature. SLM with CB scanning strategy improves both these properties. All these observations on the mechanical properties are rationalized by recourse to micro- and meso-structures seen these alloys.
California Institute of Technology1, Tel Aviv University2, Goddard Space Flight Center3, University of Maryland, College Park4, University of Wisconsin–Milwaukee5, Stockholm University6, Princeton University7, Liverpool John Moores University8, National Central University9, Hebrew University of Jerusalem10, University of Sydney11, Australian Research Council12, Indian Institute of Astrophysics13, Indian Institute of Science14, Commonwealth Scientific and Industrial Research Organisation15, University of Colorado Boulder16, Columbia University17, University of Washington18, Indian Institute of Technology Bombay19, Inter-University Centre for Astronomy and Astrophysics20, Lawrence Berkeley National Laboratory21, University of California, Berkeley22, George Washington University23, Texas Tech University24, University College London25, University of Leicester26, Space Telescope Science Institute27, National Radio Astronomy Observatory28, University of Portsmouth29, University of Southampton30, Radboud University Nijmegen31, Tokyo Institute of Technology32, Max Planck Society33, Adler Planetarium34, Northwestern University35, University of Oxford36, Weizmann Institute of Science37, Institute for the Physics and Mathematics of the Universe38, San Diego State University39, University of California, Merced40
TL;DR: In this paper, the authors established the physical association of an electromagnetic counterpart EM170817 to gravitational waves (GW 170817) detected from merging neutron stars by synthesizing a panchromatic dataset.
Abstract: Merging neutron stars offer an exquisite laboratory for simultaneously studying strong-field gravity and matter in extreme environments. We establish the physical association of an electromagnetic counterpart EM170817 to gravitational waves (GW170817) detected from merging neutron stars. By synthesizing a panchromatic dataset, we demonstrate that merging neutron stars are a long-sought production site forging heavy elements by r-process nucleosynthesis. The weak gamma-rays seen in EM170817 are dissimilar to classical short gamma-ray bursts with ultra-relativistic jets. Instead, we suggest that breakout of a wide-angle, mildly-relativistic cocoon engulfing the jet elegantly explains the low-luminosity gamma-rays, the high-luminosity ultraviolet-optical-infrared and the delayed radio/X-ray emission. We posit that all merging neutron stars may lead to a wide-angle cocoon breakout; sometimes accompanied by a successful jet and sometimes a choked jet.
TL;DR: The redox modulatory effect of Mn3 O4 plays a crucial role in protecting the cells from MPP+ induced cytotoxicity in a Parkinson disease (PD)-like cellular model, indicating that manganese-based nanomaterials having multi-enzyme activity can robustly rescue the Cells from oxidative damage and thereby possess therapeutic potential to prevent ROS-mediated neurological disorders.
Abstract: Nanomaterials with enzyme-like activities (nanozymes) attracts significant interest due to their therapeutic potential for the treatment of various diseases. Herein, we report that a Mn3 O4 nanozyme functionally mimics three major antioxidant enzymes, that is, superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) and the multienzyme activity is size as well as morphology-dependent. The redox modulatory effect of Mn3 O4 plays a crucial role in protecting the cells from MPP+ induced cytotoxicity in a Parkinson disease (PD)-like cellular model, indicating that manganese-based nanomaterials having multi-enzyme activity can robustly rescue the cells from oxidative damage and thereby possess therapeutic potential to prevent ROS-mediated neurological disorders.
TL;DR: Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India, driven by an increasing variability of the low-level monsoon westerlies over the Arabian Sea.
Abstract: Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950–2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property. Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950–2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.
TL;DR: A brief overview of the bactericidal behaviour of naturally occurring and bio-inspired nanostructured surfaces against different bacteria through the physico-mechanical rupture of the cell wall is presented.
TL;DR: The study helps in understanding the contamination potential of landfill leachate and establishes linkages between microbial communities and physico-chemical parameters for effective management of landfillLeachate.
TL;DR: In this article, a room-temperature synthesis was employed to prepare the platelets with thickness 2.2 nm (4 monolayers), which is significantly smaller than the Bohr excitonic diameter of CsPbCl3 (5 nm).
Abstract: Strong quantum confinement in Mn-doped semiconductor nanocrystals enhances dopant–carrier exchange interactions. Here, we report the synthesis and optical properties of strongly quantum confined, quasi two-dimensional, Mn-doped CsPbCl3 nanoplatelets. A room-temperature synthesis was employed to prepare the platelets with thickness 2.2 nm (4 monolayers), which is significantly smaller than the Bohr excitonic diameter of CsPbCl3 (5 nm). Efficient transfer of excitonic energy from the host to the Mn2+ dopant ions leads to a spin-forbidden 4T1–6A1 Mn d-electron emission with the highest quantum yield of ∼20% and exhibits a long lifetime of 1.6 ms. Subsequent anion exchange reactions at room temperature lead to the formation of Mn-doped CsPbBr3 nanoplatelets, with weak Mn emission. These newly developed Mn-doped cesium lead halide nanoplatelets are suitable candidates for exploring the effects of quantum confinement on dopant–carrier exchange interaction and exhibiting interesting magneto-optic properties.
TL;DR: In this article, the authors calculated the QCD equation of state using Taylor expansions that include contributions from up to sixth order in the baryon strangeness and electric charge chemical potentials.
Abstract: We calculated the QCD equation of state using Taylor expansions that include contributions from up to sixth order in the baryon strangeness and electric charge chemical potentials. Calculations have been performed with the Highly Improved Staggered Quark action in the temperature range T epsilon [135 MeV 330 MeV] using up to four different sets of lattice cutoffs corresponding to lattices of size N sigma 3x N tau with aspect ratio N sigma/N tau = 4 and N tau=6-16. The strange quark mass is tuned to its physical value and we use two strange to light quark mass ratios ms/ml = 20 and 27 which in the continuum limit correspond to a pion mass of about 160 and 140 MeV respectively. Sixth order results for Taylor expansion coefficients are used to estimate truncation errors of the fourth order expansion. We show that truncation errors are small for baryon chemical potentials less then twice the temperature (mu(B) 0.9.
Vardan Khachatryan1, Albert M. Sirunyan1, Armen Tumasyan1, Wolfgang Adam +2333 more•Institutions (195)
TL;DR: In this paper, the authors acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:======BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ,======And FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS======(Colombia); MSES and CSF (Croatia); RPF (
Abstract: we acknowledge the enduring support for the construction and
operation of the LHC and the CMS detector provided by the following funding agencies:
BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ,
and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS
(Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador);
MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland);
CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece);
OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN
(Italy); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia);
BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MBIE (New
Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna);
MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain);
Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and
NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC
(United Kingdom); DOE and NSF (U.S.A.).
TL;DR: In this paper, the Higgs boson mass was measured in the H → ZZ → 4l (l = e, μ) decay channel and the signal strength modifiers for individual Higgs production modes were also measured.
Abstract: Properties of the Higgs boson are measured in the H → ZZ → 4l (l = e, μ) decay channel. A data sample of proton-proton collisions at $ \sqrt{s}=13 $ TeV, collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 35.9 fb$^{−1}$ is used. The signal strength modifier μ, defined as the ratio of the observed Higgs boson rate in the H → ZZ → 4l decay channel to the standard model expectation, is measured to be μ = 1.05$_{− 0.17}^{+ 0.19}$ at m$_{H}$ = 125.09 GeV, the combined ATLAS and CMS measurement of the Higgs boson mass. The signal strength modifiers for the individual Higgs boson production modes are also measured. The cross section in the fiducial phase space defined by the requirements on lepton kinematics and event topology is measured to be 2. 92$_{− 0.44}^{+ 0.48}$ (stat)$_{− 0.24}^{+ 0.28}$ (syst)fb, which is compatible with the standard model prediction of 2.76 ± 0.14 fb. Differential cross sections are reported as a function of the transverse momentum of the Higgs boson, the number of associated jets, and the transverse momentum of the leading associated jet. The Higgs boson mass is measured to be m$_{H}$ = 125.26 ± 0.21 GeV and the width is constrained using the on-shell invariant mass distribution to be Γ$_{H}$ < 1.10 GeV, at 95% confidence level.
TL;DR: A novel method for Gaussian processes modeling in one dimension where the computational requirements scale linearly with the size of the data set, and is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond.
Abstract: The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large datasets. Gaussian Processes are a popular class of models used for this purpose but, since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small datasets. In this paper, we present a novel method for Gaussian Process modeling in one-dimension where the computational requirements scale linearly with the size of the dataset. We demonstrate the method by applying it to simulated and real astronomical time series datasets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically-driven damped harmonic oscillators -- providing a physical motivation for and interpretation of this choice -- but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable Gaussian Process methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.
TL;DR: The conformal bootstrap as mentioned in this paper uses the Mellin representation of CFT functions and expands them in terms of crossing symmetric combinations of AdS and Witten exchange functions in order to cancel spurious powers in position space.
Abstract: We describe in more detail our approach to the conformal bootstrap which uses the Mellin representation of CFT
d
four point functions and expands them in terms of crossing symmetric combinations of AdS
d+1 Witten exchange functions. We consider arbitrary external scalar operators and set up the conditions for consistency with the operator product expansion. Namely, we demand cancellation of spurious powers (of the cross ratios, in position space) which translate into spurious poles in Mellin space. We discuss two contexts in which we can immediately apply this method by imposing the simplest set of constraint equations. The first is the epsilon expansion. We mostly focus on the Wilson-Fisher fixed point as studied in an epsilon expansion about d = 4. We reproduce Feynman diagram results for operator dimensions to O(ϵ
3) rather straightforwardly. This approach also yields new analytic predictions for OPE coefficients to the same order which fit nicely with recent numerical estimates for the Ising model (at ϵ = 1). We will also mention some leading order results for scalar theories near three and six dimensions. The second context is a large spin expansion, in any dimension, where we are able to reproduce and go a bit beyond some of the results recently obtained using the (double) light cone expansion. We also have a preliminary discussion about numerical implementation of the above bootstrap scheme in the absence of a small parameter.
TL;DR: In this article, an extension of the SYK model was proposed, which exhibits a quantum phase transition from the previously identified non-Fermi liquid fixed point to a Fermi-liquid-like state, while still allowing an exact solution in a suitable large-$N$ limit.
Abstract: We propose an extension of the Sachdev-Ye-Kitaev (SYK) model that exhibits a quantum phase transition from the previously identified non-Fermi-liquid fixed point to a Fermi-liquid-like state, while still allowing an exact solution in a suitable large-$N$ limit. The extended model involves coupling the interacting $N$-site SYK model to a new set of $pN$ peripheral sites with only quadratic hopping terms between them. The conformal fixed point of the SYK model remains a stable low-energy phase below a critical ratio of peripheral sites $pl{p}_{c}(n)$ that depends on the fermion filling $n$. The scrambling dynamics throughout the non-Fermi-liquid (NFL) phase is characterized by a universal Lyapunov exponent ${\ensuremath{\lambda}}_{\mathrm{L}}\ensuremath{\rightarrow}2\ensuremath{\pi}T$ in the low-temperature limit; however, the temperature scale marking the crossover to the conformal regime vanishes continuously at the critical point ${p}_{c}$. The residual entropy at $T\ensuremath{\rightarrow}0$, nonzero in the NFL, also vanishes continuously at the critical point. For $pg{p}_{c}$ the quadratic sites effectively screen the SYK dynamics, leading to a quadratic fixed point in the low-temperature and low-frequency limit. The interactions have a perturbative effect in this regime leading to scrambling with Lyapunov exponent ${\ensuremath{\lambda}}_{\mathrm{L}}\ensuremath{\propto}{T}^{2}$.
TL;DR: In this article, the authors discuss the recent progress in the research of these colloidal metal halide nanocrystals that are either analogous to CsPbX3 perovskites or derivatives of CspbX 3 perovsites.
Abstract: Colloidal CsPbX3 (X = Cl, Br, I) nanocrystals (NCs) are being explored extensively as an interesting variety of defect-tolerant materials, wherein high efficiencies of optical and optoelectronic processes can be achieved even in the presence of surface defects. This defect-tolerant nature arises mainly because of the unique electronic band structure of these perovskites. Consequently, synthesis and exploration of other metal halide {CsSnX3, Cs2SnX6, and (CH3)3Bi2X9} NCs with electronic band structure similar to that of CsPbX3 perovskite have begun with high promise. Another initiative to tailor the properties is the doping of metal ions (Mn2+, Zn2+, Cd2+, Sn2+, and Bi3+) into the lattice of CsPbX3 NCs. Furthermore, nanocomposites of CsPbX3–metal and CsPbX3–dielectric layer–metal have been attempted. Here we discuss the recent progress in the research of these colloidal metal halide NCs that are either analogous to CsPbX3 perovskites or derivatives of CsPbX3 perovskites.
Proceedings Article•
17 Jul 2017TL;DR: In this article, the authors considered the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown.
Abstract: We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive corresponding regret bounds. Specifically, the bounds hold when the expected reward function belongs to the reproducing kernel Hilbert space (RKHS) that naturally corresponds to a Gaussian process kernel used as input by the algorithms. Along the way, we derive a new self-normalized concentration inequality for vector- valued martingales of arbitrary, possibly infinite, dimension. Finally, experimental evaluation and comparisons to existing algorithms on synthetic and real-world environments are carried out that highlight the favorable gains of the proposed strategies in many cases.
Posted Content•
TL;DR: A novel crowd counting model that maps a given crowd scene to its density and switch convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count is proposed.
Abstract: We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.
TL;DR: This work proposes a new approach towards analytically solving for the dynamical content of conformal field theories (CFTs) using the bootstrap philosophy, and illustrates the power of this method in the ε expansion of the Wilson-Fisher fixed point by reproducing anomalous dimensions and obtaining OPE coefficients to higher orders in ε than currently available using other analytic techniques.
Abstract: We propose a new approach towards analytically solving for the dynamical content of conformal field theories (CFTs) using the bootstrap philosophy. This combines the original bootstrap idea of Polyakov with the modern technology of the Mellin representation of CFT amplitudes. We employ exchange Witten diagrams with built-in crossing symmetry as our basic building blocks rather than the conventional conformal blocks in a particular channel. Demanding consistency with the operator product expansion (OPE) implies an infinite set of constraints on operator dimensions and OPE coefficients. We illustrate the power of this method in the. expansion of the Wilson-Fisher fixed point by reproducing anomalous dimensions and, strikingly, obtaining OPE coefficients to higher orders in. than currently available using other analytic techniques (including Feynman diagram calculations). Our results enable us to get a somewhat better agreement between certain observables in the 3D Ising model and the precise numerical values that have been recently obtained.
TL;DR: In this paper, the synthesis and supercapacitor application of two redox active covalent organic frameworks (COFs) along with the role of their active functional groups for the enrichment of specific capacitance was reported.
Abstract: Two-dimensional redox-active covalent organic frameworks (COFs) are ideal materials for energy storage applications due to their high surface area, extended π conjugated structure, tunable pore size, and adjustable functionalities. Herein, we report the synthesis and supercapacitor application of two redox active COFs [TpPa-(OH)2 and TpBD-(OH)2] along with the role of their redox active functional groups for the enrichment of specific capacitance. Of these COFs, TpPa-(OH)2 exhibited the highest specific capacitance of 416 F g–1 at 0.5 A g–1 current density in three electrode configuration while the highest specific capacitance was 214 F g–1 at 0.2 A g–1 current density in two electrode configuration. Superior specific capacitance was due to emergence of excellent pseudocapacitance by virtue of precise molecular level control over redox functionalities present in the COF backbone. This COF also demonstrated 66% capacitance retention after 10 000 cycles along with 43% accessibility of the redox-active hydro...