Showing papers by "Indian Institute of Technology Bombay published in 2018"

GW170817: Measurements of Neutron Star Radii and Equation of State.

Abstract: On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function pðρÞ of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R1 ¼ 10.8 þ2.0 −1.7 km for the heavier star and R2 ¼ 10.7 þ2.1 −1.5 km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97 M⊙ as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R1 ¼ 11.9 þ1.4 −1.4 km and R2 ¼ 11.9 þ1.4 −1.4 km at the 90% credible level. Finally, we obtain constraints on pðρÞ at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5 þ2.7 −1.7 × 1034 dyn cm−2 at the 90% level.

A mildly relativistic wide-angle outflow in the neutron-star merger event GW170817

Abstract: GW170817 was the first gravitational-wave detection of a binary neutron-star merger. It was accompanied by radiation across the electromagnetic spectrum and localized to the galaxy NGC 4993 at a distance of 40 megaparsecs. It has been proposed that the observed γ-ray, X-ray and radio emission is due to an ultra-relativistic jet being launched during the merger (and successfully breaking out of the surrounding material), directed away from our line of sight (off-axis). The presence of such a jet is predicted from models that posit neutron-star mergers as the drivers of short hard-γ-ray bursts. Here we report that the radio light curve of GW170817 has no direct signature of the afterglow of an off-axis jet. Although we cannot completely rule out the existence of a jet directed away from the line of sight, the observed γ-ray emission could not have originated from such a jet. Instead, the radio data require the existence of a mildly relativistic wide-angle outflow moving towards us. This outflow could be the high-velocity tail of the neutron-rich material that was ejected dynamically during the merger, or a cocoon of material that breaks out when a jet launched during the merger transfers its energy to the dynamical ejecta. Because the cocoon model explains the radio light curve of GW170817, as well as the γ-ray and X-ray emission (and possibly also the ultraviolet and optical emission), it is the model that is most consistent with the observational data. Cocoons may be a ubiquitous phenomenon produced in neutron-star mergers, giving rise to a hitherto unidentified population of radio, ultraviolet, X-ray and γ-ray transients in the local Universe.

Generalizing Across Domains via Cross-Gradient Training

Abstract: We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other's objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.

Demonstration of high mobility and quantum transport in modulation-doped β-(AlxGa1-x)2O3/Ga2O3 heterostructures

Abstract: In this work, we demonstrate a high mobility two-dimensional electron gas (2DEG) formed at the β-(AlxGa1-x)2O3/Ga2O3 interface through modulation doping. Shubnikov-de Haas (SdH) oscillations were observed in the modulation-doped β-(AlxGa1-x)2O3/Ga2O3 structure, indicating a high-quality electron channel formed at the heterojunction interface. The formation of the 2DEG channel was further confirmed by the weak temperature dependence of the carrier density, and the peak low temperature mobility was found to be 2790 cm2/Vs, which is significantly higher than that achieved in bulk-doped Beta-phase Gallium Oxide (β-Ga2O3). The observed SdH oscillations allowed for the extraction of the electron effective mass in the (010) plane to be 0.313 ± 0.015 m0 and the quantum scattering time to be 0.33 ps at 3.5 K. The demonstrated modulation-doped β-(AlxGa1-x)2O3/Ga2O3 structure lays the foundation for future exploration of quantum physical phenomena and semiconductor device technologies based on the β-Ga2O3 material system.

Critical Role of Interlayer in Hf 0.5 Zr 0.5 O 2 Ferroelectric FET Nonvolatile Memory Performance

Abstract: We fabricate, characterize, and establish the critical design criteria of Hf0.5Zr0.5O2 (HZO)-based ferroelectric field effect transistor (FeFET) for nonvolatile memory application. We quantify ${V}_{\textsf {TH}}$ shift from electron (hole) trapping in the vicinity of ferroelectric (FE)/interlayer (IL) interface, induced by erase (program) pulse, and ${V}_{\textsf {TH}}$ shift from polarization switching to determine true memory window (MW). The devices exhibit extrapolated retention up to 10 years at 85 °C and endurance up to $5\times 10^{6}$ cycles initiated by the IL breakdown. Endurance up to 1012 cycles of partial polarization switching is shown in metal–FE–metal capacitor, in the absence of IL. A comprehensive metal–FE–insulator–semiconductor FeFET model is developed to quantify the electric field distribution in the gate-stack, and an IL design guideline is established to markedly enhance MW, retention characteristics, and cycling endurance.

Hybrid AC–DC Microgrid: Systematic Evaluation of Control Strategies

Abstract: Hybrid ac-dc microgrid architecture is attracting special attention since it combines the benefits of both ac and dc systems. Control of hybrid microgrid presents a significant research and engineering challenge and hence needs increased research efforts. This paper attempts to review control strategies that are reported in the literature for the hybrid ac-dc microgrid. At first, typical and emerging hybrid microgrid power topologies are presented briefly. Various types of interlinking converters proposed for connecting ac and dc subgrids are discussed subsequently. Following this, a detailed discussion on control strategies for satisfying various control objectives is taken up. Control strategies have been systematically reviewed focusing on each of the broad aspects of control namely modeling, power management, coordinated control, stability analysis, power quality, and protection strategies. Finally, the research gaps observed during the review process and possible solution approaches are outlined.

Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments

Abstract: . Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plantower model PMS3003) for measuring PM 2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM 2.5 (mean ± SD) of 9±9 and 10±3 µ g m −3 , respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM 2.5 of 36±17 and 116±57 µ g m −3 during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental β attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM 2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM 2.5 increased. The precision of reference instruments (T640: ±0.5 µ g m −3 for 1 h; SHARP: ±2 µ g m −3 for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and β -attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27 % for 1 h to 9 % for 24 h) than in Durham (from 201 % to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35 % to 11 %); and (3) the higher T640–PMS3003 correlations ( R2≥0.63 ) than SHARP–PMS3003 ( R2≥0.25 ). A major RH influence was found in RTP (1 h RH = 64 ± 22 %) due to the relatively high precision of the T640 measurements that can explain up to ∼30 % of the variance in 1 min to 6 h PMS3003 PM 2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM 2.5 concentrations within ∼10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM 2.5 exceeded ∼125 µ g m −3 and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM 2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization.

GW170817: Implications for the Stochastic Gravitational-Wave Background from Compact Binary Coalescences

Abstract: The LIGO Scientific and Virgo Collaborations have announced the event GW170817, the first detection of gravitational waves from the coalescence of two neutron stars. The merger rate of binary neutron stars estimated from this event suggests that distant, unresolvable binary neutron stars create a significant astrophysical stochastic gravitational-wave background. The binary neutron star component will add to the contribution from binary black holes, increasing the amplitude of the total astrophysical background relative to previous expectations. In the Advanced LIGO-Virgo frequency band most sensitive to stochastic backgrounds (near 25 Hz), we predict a total astrophysical background with amplitude ΩGW(f=25 Hz)=1.8 +2.7 −1.3×10−9 with 90% confidence, compared with ΩGW(f=25 Hz)=1.1 +1.2 −0.7×10−9 from binary black holes alone. Assuming the most probable rate for compact binary mergers, we find that the total background may be detectable with a signal-to-noise-ratio of 3 after 40 months of total observation time, based on the expected timeline for Advanced LIGO and Virgo to reach their design sensitivity.

Strong Light–Matter Interaction in Quantum Emitter/Metal Hybrid Nanostructures

Abstract: Surface plasmon polaritons (SPPs) are spatially confined electromagnetic field modes at a metal-dielectric interface capable of generating intense near-field optical forces on ultrafast time scales. Within the field of photonics, SPPs carry significant potential for guiding and manipulating light on the nanoscale. The intense SPP fields substantially enhance light–matter interactions with quantum emitters (QEs). Thus, hybrid systems comprised of SPP resonators and various types of QEs constitute key components of the modern photonics applications. Recent advances in nanotechnology have enabled fabrication of high quality QE/metal hybrid nanostructures, in which several aspects of light–matter interactions, including those in the quantum regime have been demonstrated and extensively studied. The present Perspective explores the central phenomenon in light–matter interaction in emitter/metal hybrid nanostructures, namely, the strong dipole coupling between QEs and SPPs, particularly between excitons (Xs) an...

Learning 3D Human Pose from Structure and Motion

Abstract: 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Jointly, the two networks capture the anatomical constraints in static and kinetic states of the human body. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.

"One-Pot" Protection, Glycosylation, and Protection-Glycosylation Strategies of Carbohydrates.

Abstract: Carbohydrates, which are ubiquitously distributed throughout the three domains of life, play significant roles in a variety of vital biological processes. Access to unique and homogeneous carbohydrate materials is important to understand their physical properties, biological functions, and disease-related features. It is difficult to isolate carbohydrates in acceptable purity and amounts from natural sources. Therefore, complex saccharides with well-defined structures are often most conviently accessed through chemical syntheses. Two major hurdles, regioselective protection and stereoselective glycosylation, are faced by carbohydrate chemists in synthesizing these highly complicated molecules. Over the past few years, there has been a radical change in tackling these problems and speeding up the synthesis of oligosaccharides. This is largely due to the development of one–pot protection, one–pot glycosylation, and one–pot protection–glycosylation protocols and streamlined approaches to orthogonally protect...

Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings

Abstract: Modern neural networks have recently been found to be poorly calibrated, primarily in the direction of over-confidence. Methods like entropy penalty and temperature smoothing improve calibration by clamping confidence, but in doing so compromise the many legitimately confident predictions. We propose a more principled fix that minimizes an explicit calibration error during training. We present MMCE, a RKHS kernel based measure of calibration that is efficiently trainable alongside the negative likelihood loss without careful hyperparameter tuning. Theoretically too, MMCE is a sound measure of calibration that is minimized at perfect calibration, and whose finite sample estimates are consistent and enjoy fast convergence rates. Extensive experiments on several network architectures demonstrate that MMCE is a fast, stable, and accurate method to minimize calibration error metrics while maximally preserving the number of high confidence predictions.

Highly Exfoliated MWNT-rGO Ink-Wrapped Polyurethane Foam for Piezoresistive Pressure Sensor Applications.

Abstract: The fabrication of pressure sensors based on reduced graphene oxide (rGO) as the sensing material is challenging due to the intrinsic hydrophobic behavior of graphene oxide inks as well as the agglomeration of graphene oxide flakes after reduction. Hydrazine (a reducing agent) and a dual-component additive comprising benzisothiazolinone and methylisothiazolinone in appropriate proportion were used to synthesize a rGO ink with a hydrophilic nature. Utilizing this hydrophilic rGO ink mixed with multiwalled carbon nanotubes (MWNTs), a very simple, low-cost approach is demonstrated for the fabrication of a pressure sensor based on polyurethane (PU) foam coated with the MWNT–rGO ink (MWNT–rGO@PU foam). The MWNT–rGO@PU foam-based devices are shown to be versatile pressure sensors with the potential to detect both small-scale and large-scale movements. At low pressure (below 2.7 kPa, 50% strain), the formation of microcracks that scatter electrical charges results in a detectable increase in resistance suitable ...

Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method

Abstract: Conventional supervised content-based remote sensing (RS) image retrieval systems require a large number of already annotated images to train a classifier for obtaining high retrieval accuracy. Most systems assume that each training image is annotated by a single label associated to the most significant semantic content of the image. However, this assumption does not fit well with the complexity of RS images, where an image might have multiple land-cover classes (i.e., multilabels). Moreover, annotating images with multilabels is costly and time consuming. To address these issues, in this paper, we introduce a semisupervised graph-theoretic method in the framework of multilabel RS image retrieval problems. The proposed method is based on four main steps. The first step segments each image in the archive and extracts the features of each region. The second step constructs an image neighborhood graph and uses a correlated label propagation algorithm to automatically assign a set of labels to each image in the archive by exploiting only a small number of training images annotated with multilabels. The third step associates class labels with image regions by a novel region labeling strategy, whereas the final step retrieves the images similar to a given query image by a subgraph matching strategy. Experiments carried out on an archive of aerial images show the effectiveness of the proposed method when compared with the state-of-the-art RS content-based image retrieval methods.

Transverse momentum spectra and nuclear modification factors of charged particles in pp, p-Pb and Pb-Pb collisions at the LHC

Abstract: We report the measured transverse momentum (p$_{T}$) spectra of primary charged particles from pp, p-Pb and Pb-Pb collisions at a center-of-mass energy $ \sqrt{s_{\mathrm{NN}}}=5.02 $ TeV in the kinematic range of 0.15 < p$_{T}$< 50 GeV/c and |η| < 0.8. A significant improvement of systematic uncertainties motivated the reanalysis of data in pp and Pb-Pb collisions at $ \sqrt{s_{\mathrm{NN}}}=2.76 $ TeV, as well as in p-Pb collisions at $ \sqrt{s_{\mathrm{NN}}}=5.02 $ TeV, which is also presented. Spectra from Pb-Pb collisions are presented in nine centrality intervals and are compared to a reference spectrum from pp collisions scaled by the number of binary nucleon-nucleon collisions. For central collisions, the p$_{T}$ spectra are suppressed by more than a factor of 7 around 6–7 GeV/c with a significant reduction in suppression towards higher momenta up to 30 GeV/c. The nuclear modification factor R$_{pPb}$, constructed from the pp and p-Pb spectra measured at the same collision energy, is consistent with unity above 8 GeV/c. While the spectra in both pp and Pb-Pb collisions are substantially harder at $ \sqrt{s_{\mathrm{NN}}}=5.02 $ TeV compared to 2.76 TeV, the nuclear modification factors show no significant collision energy dependence. The obtained results should provide further constraints on the parton energy loss calculations to determine the transport properties of the hot and dense QCD matter.

BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning

Abstract: Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.

Eocene greenhouse climate revealed by coupled clumped isotope-Mg/Ca thermometry

Abstract: Past greenhouse periods with elevated atmospheric CO2 were characterized by globally warmer sea-surface temperatures (SST). However, the extent to which the high latitudes warmed to a greater degree than the tropics (polar amplification) remains poorly constrained, in particular because there are only a few temperature reconstructions from the tropics. Consequently, the relationship between increased CO2, the degree of tropical warming, and the resulting latitudinal SST gradient is not well known. Here, we present coupled clumped isotope (Δ47)-Mg/Ca measurements of foraminifera from a set of globally distributed sites in the tropics and midlatitudes. Δ47 is insensitive to seawater chemistry and therefore provides a robust constraint on tropical SST. Crucially, coupling these data with Mg/Ca measurements allows the precise reconstruction of Mg/Casw throughout the Eocene, enabling the reinterpretation of all planktonic foraminifera Mg/Ca data. The combined dataset constrains the range in Eocene tropical SST to 30-36 °C (from sites in all basins). We compare these accurate tropical SST to deep-ocean temperatures, serving as a minimum constraint on high-latitude SST. This results in a robust conservative reconstruction of the early Eocene latitudinal gradient, which was reduced by at least 32 ± 10% compared with present day, demonstrating greater polar amplification than captured by most climate models.

Novel Nonlinear Droop Control Techniques to Overcome the Load Sharing and Voltage Regulation Issues in DC Microgrid

Abstract: In a dc microgrid, good load sharing and voltage regulation are desirable. These are affected by practical factors like sensor calibration errors and cable resistances. To enhance the load-sharing accuracy among the parallel-connected voltage-controlled sources and to improve the dc-bus voltage regulation, three novel nonlinear droop control techniques are proposed for the smart grid scenario. The proposed methods are completely decentralized methods and require only local information (output voltage and output current of the individual converter) for achieving aforementioned merits. Since no communication channel is required, it is easy to implement them. Furthermore, the absence of communication channel improves system reliability and offers plug-and-play features, as only local information is utilized. Also, failure of one converter does not affect the operation of other converters connected to the grid as no information is exchanged between the converters. Effect of sensor calibration errors and cable resistances is minimized by these techniques. Theoretical analysis and experimental results are presented to demonstrate the efficacy of the proposed control methods. Finally, a performance analysis of the three droop control techniques is presented along with their advantages over the conventional methods under different operating conditions.

Demonstration of β-(AlxGa1-x)2O3/Ga2O3 double heterostructure field effect transistors

Abstract: In this work, we demonstrate modulation-doped β-(AlxGa1-x)2O3/Ga2O3 double heterostructure field effect transistors. The maximum sheet carrier density for a two-dimensional electron gas (2DEG) in a β-(AlxGa1-x)2O3/Ga2O3 heterostructure is limited by the conduction band offset and parasitic channel formation in the barrier layer. We demonstrate a double heterostructure to realize a β-(AlxGa1-x)2O3/Ga2O3/(AlxGa1-x)2O3 quantum well, where electrons can be transferred from below and above the β-Ga2O3 quantum well. The confined 2DEG charge density of 3.85 × 1012 cm−2 was estimated from the low-temperature Hall measurement, which is higher than that achievable in a single heterostructure. Hall mobilities of 1775 cm2/V·s at 40 K and 123 cm2/V·s at room temperature were measured. Modulation-doped double heterostructure field effect transistors showed a maximum drain current of IDS = 257 mA/mm, a peak transconductance (gm) of 39 mS/mm, and a pinch-off voltage of −7.0 V at room temperature. The three-terminal off-state breakdown measurement on the device with a gate-drain spacing (LGD) of 1.55 μm showed a breakdown voltage of 428 V, corresponding to an average breakdown field of 2.8 MV/cm. The breakdown measurement on the device with a scaled gate-drain spacing of 196 nm indicated an average breakdown field of 3.2 MV/cm. The demonstrated modulation-doped β-(AlxGa1-x)2O3/Ga2O3 double heterostructure field effect transistor could act as a promising candidate for high power and high frequency device applications.

Energy Management in Smart Distribution Systems With Vehicle-to-Grid Integrated Microgrids

Abstract: In modern electric power systems, plug-in electric vehicle (PEV) with vehicle-to-grid (V2G) potential are becoming reliable and flexible resources for energy balancing under varying energy supply and demand scenarios. In this evolving paradigm, designing energy management strategies for feasible and cost-effective utilisation of V2G is one of the several challenges faced by the utility operators and regulators. This paper proposes two energy management strategies to effectively utilize V2G potential of PEVs in managing energy imbalances in grid-connected microgrids. The contributions of this paper are in twofold. First, it proposes a novel bidding strategy for PEVs offering V2G by including the projected battery degradation cost to integrate them into microgrid operation. Second, two energy management strategies are proposed for inclusion of V2G into the microgrid operation based on the forecast accuracy on energy supply and demand, and market prices. The proposed V2G integration strategies are implemented using a multi-agent system developed in Java agent development framework and applied to a microgrid case study system. The simulation results and their analysis show that V2G can be used to maximum depth of discharge levels if the electricity price variation is high and battery cost of PEVs is low.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

Abstract: The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω_{0}^{T}<5.58×10^{-8}, Ω_{0}^{V}<6.35×10^{-8}, and Ω_{0}^{S}<1.08×10^{-7} at a reference frequency f_{0}=25 Hz.