Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient Critical Heat Fluxes of Subcooled Water Flow Boiling in a SUS304-Circular Tube Caused by a Rapid Decrease in Velocity From Non-Boiling Regime

The flow transient critical heat fluxes (FT-CHFs, qcr,sub) in a SUS304-circular tube caused by a rapid decrease in velocity from non-boiling regime are systematically measured for initial flow velocities (u0=7.057 to 13.635 m/s for conditions of u0=6.9, 9.9 and 13.3 m/s), initial heat fluxes (q0=15.59 to 17.34 MW/m2), inlet liquid temperatures (Tin=290.12 to 308.51 K), outlet pressures (Pout=698.38 to 1288.97 kPa) and decelerations caused by a rapid decrease in velocity (u(t)=u0+αt, α=−7.357 to −0.326 m/s2) by the experimental water loop comprised of a multistage canned-type circulation pump controlled by an inverter. The SUS304-circular tubes of inner diameter (d=6 mm), heated length (L=59.5 to 59.7 mm), effective length (Leff=48.7 to 50.2 mm), L/d (=9.92 to 9.95), Leff/d (=8.12 to 8.37) and wall thickness (δ=0.5 mm) with average surface roughness (Ra=3.89 μm) are used in this work. The flow transient CHFs for SUS304-circular tube are compared with authors’ steady-state CHF data for the empty VERTICAL and HORIZONTAL SUS304-circular tubes and the values calculated by authors’ steady-state CHF correlations against outlet and inlet subcoolings for the empty circular tube. The influences of initial flow velocity (u0), initial heat flux (q0) and deceleration caused by a rapid decrease in velocity (α) on the flow transient CHF are investigated into details and the widely and precisely predictable correlations of CHF and flow velocity at the flow transient CHF for the circular tube is given based on the experimental data. The correlations can describe the flow velocity and the CHFs at the flow transient CHFs for SUS304-circular tube obtained in this work within ±20 % difference.Copyright © 2014 by ASME
Accelerated physical aging in PMMA/silica nanocomposites

We have monitored the physical aging process below the glass transition temperature (Tg) of poly(methyl methacrylate) PMMA/silica nanocomposites by means of broadband dielectric spectroscopy (BDS). To do so, we have followed the evolution with time of the dielectric strength of the PMMA secondary relaxation process that dominates the dielectric response overall below Tg. The employed silica particles are spherical and present a diameter of several hundred nanometres. We have investigated polymer nanocomposites with silica concentration of about 10% wt. This results in an interparticle distance of the order of several hundred nanometers. Despite the general similarity between the segmental dynamics of the nanocomposites and that of pure PMMA as evidenced by both differential scanning calorimetry (DSC) and BDS experiments, the former systems display markedly accelerated physical aging in comparison to the pure polymer. This striking result suggests that the relevant length scale of the system under investigation plays a crucial role in affecting the mechanism of the physical aging process. As a natural consequence of such evidence, the diffusion of free volume holes—annihilating at the “external surface” of the polymer being aged—has been invoked to explain the strong mismatch between the physical aging in the nanocomposite and that of pure PMMA. Such an interpretation is discussed in light of the recent results on physical aging of polymer nanocomposites.
The carbon chemistry in interstellar clouds toward moderately reddened stars

New data for C2 toward X Per, HD 206267, HD 207198, and Gamma Cep, for CH and CN toward X Per, and for CO toward HD 207198 have been obtained. The column densities of CH, C2, CN, and CO toward the stars in the Cepheus OB2 association are similar to reddened directions in Perseus and in Ophiuchus, indicating a similarity in physical conditions for the foreground clouds. The available data for other directions have been analyzed and the resulting data applied to study the transition from a photochemical regime to a chemical regime. The data for N(CN), N(C2), and N(CO) have been plotted against N(CH) to elucidate the chemistry of carbon-bearing molecules more clearly. The observed trends for CN and C2 suggest a change in slope at N(CH) of roughly 5 x 10 to the 13th/sq cm. Below this value, photodestruction is predicted to dominate and the slope is determined by the photochemistry. For directions with more N(CH), a linear correlation consistent with destruction by chemical reactions is expected. 41 references.
a‐SiC:H/a‐Si:H heterojunction solar cell having more than 7.1% conversion efficiency

A clear valency electron controllability with substitutional impurity doping in the hydrogenated amorphous silicon carbide has been found. The amorphous silicon carbide is produced by the plasma decomposition of [SiH4(1−X)+CH4(X)] with the dopant gas of a B2H6 or PH3 system. Electrical and optical properties of doped amorphous SiC films are briefly demonstrated. Utilizing this a‐SiC:H as a wide‐band‐gap window material, we have developed a new type of a‐SiC:H/a‐Si:H heterojunction solar cell. A typical cell performance is Voc = 0.887 V, Jsc = 12.33 mA/cm2, fill factor = 0.653, and the conversion efficiency of 7.14% under AM‐1 illumination.
The Hubble WFC3 Emission Spectrum of the Extremely Hot Jupiter KELT-9b

Recent studies of ultra-hot Jupiters suggested that their atmospheres could have thermal inversions due to the presence of optical absorbers such as titanium oxide (TiO), vanadium oxide (VO), iron hydride (FeH), and other metal hydride/oxides. However, it is expected that these molecules would thermally dissociate at extremely high temperatures, thus leading to featureless spectra in the infrared. KELT-9 b, the hottest exoplanet discovered so far, is thought to belong to this regime and host an atmosphere dominated by neutral hydrogen from dissociation and atomic/ionic species. Here, we analyzed the eclipse spectrum obtained using the Hubble Space Telescope’s Wide Field Camera 3 and, by utilizing the atmospheric retrieval code TauREx3, found that the spectrum is consistent with the presence of molecular species and is poorly fitted by a simple blackbody. In particular, we find that a combination of TiO, VO, FeH, and H- provides the best fit when considering Hubble Space Telescope (HST), Spitzer, and TESS data sets together. Aware of potential biases when combining instruments, we also analyzed the HST spectrum alone and found that TiO and VO only were needed in this case. These findings paint a more complex picture of the atmospheres of ultra-hot planets than previously thought.
VinVL: Making Visual Representations Matter in Vision-Language Models

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \emph{bottom-up and top-down} model \cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.
Simulating Effective QED on Quantum Computers

In recent years simulations of chemistry and condensed materials has emerged as one of the preeminent applications of quantum computing, offering an exponential speedup for the solution of the electronic structure for certain strongly correlated electronic systems. To date, most treatments have ignored the question of whether relativistic effects, which are described most generally by quantum electrodynamics (QED), can also be simulated on a quantum computer in polynomial time. Here we show that effective QED, which is equivalent to QED to second order in perturbation theory, can be simulated in polynomial time under reasonable assumptions while properly treating all four components of the wavefunction of the fermionic field. In particular, we provide a detailed analysis of such simulations in position and momentum basis using Trotter-Suzuki formulas. We find that the number of T-gates needed to perform such simulations on a 3D lattice of ns sites scales at worst as O(ns3/ϵ)1+o(1) in the thermodynamic limit for position basis simulations and O(ns4+2/3/ϵ)1+o(1) in momentum basis. We also find that qubitization scales slightly better with a worst case scaling of O~(ns2+2/3/ϵ) for lattice eQED and complications in the prepare circuit leads to a slightly worse scaling in momentum basis of O~(ns5+2/3/ϵ). We further provide concrete gate counts for simulating a relativistic version of the uniform electron gas that show challenging problems can be simulated using fewer than 1013 non-Clifford operations and also provide a detailed discussion of how to prepare multi-reference configuration interaction states in effective QED which can provide a reasonable initial guess for the ground state. Finally, we estimate the planewave cutoffs needed to accurately simulate heavy elements such as gold.
Understanding Power Consumption and Reliability of High-Bandwidth Memory with Voltage Underscaling

Modern computing devices employ High-Bandwidth Memory (HBM) to meet their memory bandwidth requirements. An HBM-enabled device consists of multiple DRAM layers stacked on top of one another next to a compute chip (e.g, CPU, GPU, and FPGA) in the same package. Although such HBM structures provide high bandwidth at a small form factor, the stacked memory layers consume a substantial portion of the package's power budget. Therefore, power-saving techniques that preserve the performance of HBM are desirable. Undervolting is one such technique: it reduces the supply voltage to decrease power consumption without reducing the device's operating frequency to avoid performance loss. Undervolting takes advantage of voltage guardbands put in place by manufacturers to ensure correct operation under all environmental conditions. However, reducing voltage without changing frequency can lead to reliability issues manifested as unwanted bit flips. In this paper, we provide the first experimental study of real HBM chips under reduced-voltage conditions. We show that the guardband regions for our HBM chips constitute 19% of the nominal voltage. Pushing the supply voltage down within the guardband region reduces power consumption by a factor of 1.5X for all bandwidth utilization rates. Pushing the voltage down further by 11% leads to a total of 2.3X power savings at the cost of unwanted bit flips. We explore and characterize the rate and types of these reduced-voltage-induced bit flips and present a fault map that enables the possibility of a three-factor trade-off among power, memory capacity, and fault rate.
Derivative-Free Policy Optimization for Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

Direct policy search serves as one of the workhorses in modern reinforcement learning (RL), and its applications in continuous control tasks have recently attracted increasing attention. In this work, we investigate the convergence theory of policy gradient (PG) methods for learning the linear risk-sensitive and robust controller. In particular, we develop PG methods that can be implemented in a derivative-free fashion by sampling system trajectories, and establish both global convergence and sample complexity results in the solutions of two fundamental settings in risk-sensitive and robust control: the finite-horizon linear exponential quadratic Gaussian, and the finite-horizon linear-quadratic disturbance attenuation problems. As a by-product, our results also provide the first sample complexity for the global convergence of PG methods on solving zero-sum linear-quadratic dynamic games, a nonconvex-nonconcave minimax optimization problem that serves as a baseline setting in multi-agent reinforcement learning (MARL) with continuous spaces. One feature of our algorithms is that during the learning phase, a certain level of robustness/risk-sensitivity of the controller is preserved, which we termed as the implicit regularization property, and is an essential requirement in safety-critical control systems.
Dual-Mode Volumetric Optoacoustic and Contrast Enhanced Ultrasound Imaging With Spherical Matrix Arrays

Spherical matrix arrays represent an advantageous tomographic detection geometry for non-invasive deep tissue mapping of vascular networks and oxygenation with volumetric optoacoustic tomography (VOT). Hybridization of VOT with ultrasound (US) imaging remains difficult with this configuration due to the relatively large inter-element pitch of spherical arrays. We suggest a new approach for combining VOT and US contrast-enhanced 3D imaging employing injection of clinically-approved microbubbles. Power Doppler (PD) and US localization imaging were enabled with a sparse US acquisition sequence and model-based inversion based on infimal convolution of total variation (ICTV) regularization. In vitro experiments in tissue-mimicking phantoms and in living mouse brain demonstrate the powerful capabilities of the new dual-mode imaging approach attaining $80~ \mu m$ spatial resolution and a more than 10 dB signal to noise improvement with respect to a classical delay and sum beamformer. Microbubble localization and tracking allowed for flow velocity mapping up to 40 mm/s.
RV-GAN : Retinal Vessel Segmentation from Fundus Images using Multi-scale Generative Adversarial Networks

Retinal vessel segmentation contributes signiﬁcantly to the domain of retinal image analysis for the diagnosis of vision-threatening diseases. With existing techniques the generated segmentation result deteriorates when thresh-olded with higher conﬁdence value. To alleviate from this, we propose RVGAN, a new multi-scale generative architecture for accurate retinal vessel segmentation. Our architecture uses two generators and two multi-scale autoen-coder based discriminators, for better microvessel localization and segmentation. By combining reconstruction and weighted feature matching loss, our adversarial training scheme generates highly accurate pixel-wise segmentation of retinal vessels with threshold ≥ 0 . 5 . The architecture achieves AUC of 0.9887, 0.9814, and 0.9887 on three publicly available datasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally, RV-GAN outperforms other architectures in two additional relevant metrics, Mean-IOU and SSIM.
Low Light Image Enhancement via Global and Local Context Modeling

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives to model dependencies, existing data-driven deep models do not exploit the contexts at various spatial scales to address low-light image enhancement. These contexts can be crucial towards inferring several image enhancement tasks, e.g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent. To this end, we introduce a context-aware deep network for low-light image enhancement. First, it features a global context module that models spatial correlations to find complementary cues over full spatial domain. Second, it introduces a dense residual block that captures local context with a relatively large receptive field. We evaluate the proposed approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On all these datasets, our method performs favorably against the state-of-the-arts in terms of standard image fidelity metrics. In particular, compared to the best performing method on the MIT-Adobe FiveK dataset, our algorithm improves PSNR from 23.04 dB to 24.45 dB.
Characterization of Highway Runoff in Austin, Texas, Area

Water quality of highway runoff in the Austin, Texas, area was determined by monitoring runoff at three locations on the MoPac Expressway. Daily traffic volumes, surrounding land uses, and highway drainage system types were different at each site. The concentrations of constituents in runoff at all sites were similar to median values compiled in a nationwide study of highway runoff quality. A grassy swale at one site was effective in reducing the concentrations of most constituents in runoff. The lower runoff coefficient at this site is attributable to infiltration of runoff into the grassy swale. The pollutant loads discharged from the pipe draining the swale were lower than those observed at the sites where runoff drained directly from the pavement. A first flush effect (i.e., higher pollutant concentrations at the beginning of an event) was evident during selected events, but was generally limited to a small volume. The overall effect was negligible when all monitored events were considered.
Scalable representation learning and retrieval for display advertising

Over the past decades, recommendation has become a critical component of many online services such as media streaming and e-commerce. Recent advances in algorithms, evaluation methods and datasets have led to continuous improvements of the state-of-the-art. However, much work remains to be done to make these methods scale to the size of the internet. Online advertising offers a unique testbed for recommendation at scale. Every day, billions of users interact with millions of products in real-time. Systems addressing this scenario must work reliably at scale. We propose an efficient model (LED, for Lightweight Encoder-Decoder) reaching a new trade-off between complexity, scale and performance. Specifically, we show that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale. We further provide the detailed description of a system architecture and demonstrate its operation over two months at the scale of the internet. Our design allows serving billions of users across hundreds of millions of items in a few milliseconds using standard hardware.
The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present the Pile : an 825 GiB English text corpus tar-geted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets—both existing and newly constructed—many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve signiﬁcantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction. 1
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies

A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies.
UnNatural Language Inference

Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i.e. they are invariant to random word-order permutations. This behavior notably differs from that of humans; we struggle to understand the meaning of ungrammatical sentences. To measure the severity of this issue, we propose a suite of metrics and investigate which properties of particular permutations lead models to be word order invariant. For example, in MNLI dataset we find almost all (98.7%) examples contain at least one permutation which elicits the gold label. Models are even able to assign gold labels to permutations that they originally failed to predict correctly. We provide a comprehensive empirical evaluation of this phenomenon, and further show that this issue exists in pre-Transformer RNN / ConvNet based encoders, as well as across multiple languages (English and Chinese). Our code and data are available at https://github.com/facebookresearch/unlu.
Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers

Transformers have shown improved performance when compared to previous architectures for sequence processing such as RNNs. Despite their sizeable performance gains, as recently suggested, the model is computationally expensive to train and with a high parameter budget. In light of this, we explore parameter-sharing methods in Transformers with a specific focus on generative models. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer. Our model combines sandwich-style parameter sharing, which overcomes naive cross-layer parameter sharing in generative models, and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.